Achieving sub-milliSievert CT colonography for accurate colorectal tumor detection using smart examination protocols: a prospective self-controlled study

Achieving sub-milliSievert CT colonography for accurate colorectal tumor detection using smart examination protocols: a prospective self-controlled study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Achieving sub-milliSievert CT colonography for accurate colorectal tumor detection using smart examination protocols: a prospective self-controlled study Jingyi Zhang, Mengting Hu, Qiye Cheng, Shigeng Wang, Yijun Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4578840/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Sep, 2024 Read the published version in Abdominal Radiology → Version 1 posted 9 You are reading this latest preprint version Abstract Purpose To assess the feasibility of combining Auto-kVp selection technique, higher preset ASIR-V and noise index (NI) to realize individualized sub-mSv CT colonography (CTC) for accurate colorectal tumor detection and localization. Methods Ninety patients with suspected colorectal cancer (CRC) were prospectively enrolled to undergo standard dose CTC (SDCTC) in the prone and ultra-low dose CTC (ULDCTC) in the supine position. SDCTC used 120 kVp, preset ASIR-V of 30%, SmartmA for a NI of 13; ULDCTC used Auto-kVp selection technique with 80 or 100 kVp, preset ASIR-V of 60%, SmartmA for a NI of 13 for 80 kVp, and NI of 15 for 100 kVp. The effective dose (ED), image quality [signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of colorectal neoplasms] between the two protocols were compared and the accuracies of tumor locations were evaluated for CTC in comparison with the surgery results. Results The mean ED of the 80 kVp subgroup was 0.70mSv, 71.43% lower than the 2.45mSv for the 120kVp group, while that of the 100 kVp subgroup was 0.98mSv, 73.00% lower than the 3.63mSv for the 120 kVp group ( P < 0.001). The tumor SNR and CNR of the ULDCTC were higher than those of SDCTC ( P < 0.05), while there was no difference in the subjective image quality between them with good inter-observer agreement ( Kappa : 0.805–0.923). Both SDCTC and ULDCTC groups had high detection rate of colorectal tumors, along with good consistency in determining tumor location compared with surgery reports ( Kappa : 0.718–0.989). Conclusion The combination of Auto-kVp selection, higher preset ASIR-V and NI achieves individualized sub-mSv CTC with good performance in detecting and locating CRC with surgery and consistent results between SDCTC and ULDCTC. Colonography Computed Tomographic Colorectal Neoplasms Radiation Dosage Image Reconstruction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Colorectal cancer (CRC) has become the third leading cause of death in patients diagnosed with malignant neoplasms in the digestive system[ 1 ]. In the recent decades, the decrease in CRC mortality could be attributed to the improvements in the early detection[ 2 ]. Optical colonoscopy (OC), serving as the gold standard, plays an important role in the reliable diagnosis of CRC[ 3 ]. However, the invasive nature of the procedure and high requirement of the physician’s experience narrow the application of OC in some circumstances[ 4 – 5 ]. Nowadays, CT Colonography (CTC), also known as virtual colonoscopy, becomes a compensation for incomplete colonoscopy (IC), because of its ability to provide the entire colon evaluation, especially for the proximal colon obstructed by occlusive CRC[ 6 ]. Furthermore, an increasing number of organizations, including American College of Gastroenterology, the European Society of Gastrointestinal Endoscopy and European Society of Gastrointestinal and Abdominal Radiology, have involved CTC as a routine screening test for CRC, which highlighted the increasing clinical value of CTC[ 7 – 8 ]. However, the routine CTC requires two scans in prone and supine positions, causing the risk of radiation exposure a non-negligible issue. Since the potential harm from radiation has raised great awareness, exploring effective dose reduction approaches has always been a hot topic in the field of medical imaging. With the continuous development of CT technology, various tools have been introduced to decrease radiation dose without sacrificing image quality. One of them is the automated kilovoltage peak selection (Auto-kVp), been known as Auto-prescription technique, provides the patient with the most appropriate tube voltage based on the x-ray attenuation features derived from patient’s body habitus[ 9 ]. Taking advantage of Auto-kVp can realize individualized low-dose imaging, which has been studied in the thoracoabdominal aorta and portal vein[ 10 – 11 ]. Another dose reduction approach, been frequently neglected, is adjusting the preset strength of adaptive statistical iterative reconstruction algorithm-V (preset ASIR-V). As the weight of preset ASIR-V increases, the radiation dose required to achieve the same noise level for a given patient decreases exponentially[ 12 ]. Furthermore, increasing noise index (NI) can also decrease radiation dose. The increasing image noise caused by the higher NI can be dealt with applying high strength of post-set ASIR-V, which has great denoising capacity to compensate for the image degradation[ 13 – 14 ]. To the best of our knowledge, combining the above-mentioned dose-reduction techniques in CRC patients has not been paid much attention. Therefore, this study aims to assess the feasibility of combining Auto-prescription technique, higher preset ASIR-V and higher NI to realize individualized sub-mSv CTC, as well as evaluating its diagnostic performance by the detection rate of CRC under ultra-low dose protocol and the accuracy of defining tumor location in comparison with the results of surgery. Materials and methods This prospective study was approved by the Institutional Review Board of our Hospital. Written informed consent for study participation was obtained from all involved patients. Study participants During the period of December 2023 to April 2024, a total of 110 consecutive patients who were scheduled to undergo CTC examination with clinically suspected CRC in our hospital were prospectively enrolled in this study. The exclusion criteria were as follows: patients with poor bowel preparation, unable to tolerate gas injection, metal artifacts from lumbar implants or total hip replacement, and patients who were recommended to use 120 kVp tube voltage by Auto prescription technique. Among the 110 patients, 11 patients who had no pathological report, 5 patients who had maximum tumor diameter less than 10 mm, and another 4 patients who underwent chemotherapy for treatment were further excluded from analysis. Thus, 90 patients finally made up of our study population (Fig. 1 ). Sub-milliSievert CT Colonography Prior to the CTC, a clear liquid diet was restricted to all patients three days before the examination. A standard bowel preparation was conducted on all patients with a polyethylene glycol electrolyte powder (Hygecon, Jiangxi Hygecon Pharmecutical) as laxative, and without fecal tagging. On the day of CTC examination, all patients had fasted overnight and lied in the left lateral decubitus position on the CT examination table. A senior radiologist assisted by a dedicated nurse manually inflated CO 2 into the colon via a flexible rectal tube, until the patient experienced abdominal discomfort. The CTC scanning started with patients in prone position with care taken to avoid abdominal compression by putting a pillow under the chest. After the patients rotated into the supine position, the colon distension was checked based on the supine scout image. If the residual air was inadequate, further inflation would be performed before the second scan to ensure the ULDCTC image quality. All CTC examinations were performed with a 256-row spectral CT scanner (Revolution CT, GE HealthCare, Milwaukee, WI, USA )using the following parameters: detector width, 80 mm; pitch, 0.992; rotation time, 0.5 s; slice thickness, 5 mm; tube voltage, 120 kVp in the prone position [standard dose CTC (SDCTC)] and Auto-prescription kVp selection in the supine position [ultra-low dose CTC (ULDCTC)] with subgroups of 80 kVp or 100 kVp; tube current, Smart mA mode (range, 20–450 mA); preset ASIR-V, 30% for the SDCTC and 60% for the ULDCTC. Radiation doses were controlled by using noise index (NI) setting: 13 for the 120 kVp group and 80 kVp subgroup and 15 for the 100 kVp subgroup. All raw data were reconstructed using a STND kernel with 30% and 80% post-set ASIR-V for the SDCTC and the ULDCTC, respectively. The reconstruction thickness and increment were both 0.625 mm. Radiation dose metrics The volume CT dose index (CTDI vol ) and dose-length product (DLP) were recorded for comparison. Moreover, the scan length was obtained from the starting and ending points for comparison between the prone and supine positions. Furthermore, the effective dose (ED) was calculated from DLP using 0.015 mSv/ (mGy·cm) as the conversion factor in abdomen, recommended by the European guidelines on quality criteria[ 15 ]. Quantitative evaluation All axial images were transferred to an AW 4.7 ( GE HealthCare, Milwaukee, WI, USA) workstation for the following reformatted images: two-dimensional (2D) Multi-planar Reformation (MPR), three-dimensional (3D) endoluminal view from CT Virtual Colonoscopy (CTVC) and 3D Raysum. On the axial images, the circular regions of interest (ROIs) were drawn on the homogenous area of the colorectal tumor and intraluminal air to measure the CT value and the standard deviation (SD) values, in order to calculate the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) of tumors. The formulas were as follows: SNR=CT tumor /SD tumor , CNR༝(CT tumor -CT intraluminal air )/SD intraluminal air . The tumor ROIs were depicted on the slice of maximum diameter, and were manually placed on the prone and supine images, keeping the ROI size accounting for 70%-80% of the tumor. The measurements on three consecutive slices were recorded and averaged to avoid measuring bias, and the ROI sizes ranged from 50–100 mm 2 . Qualitative evaluation Two abdominal radiologists (one junior radiologist with 5-year experience, and one senior radiologist with 8-year experience in abdominal imaging), who were blinded to the group division, independently performed subjective image quality assessment on the workstation. The 2D MPR images and 3D CTVC images were graded using a five-point scale. The images scored greater than or equal to 3 points were considered diagnostically acceptable. The marking criteria were demonstrated as follows: 5 points: clear image, virtually no image noise, excellent confidence to detect colorectal lesions; 4 points: relatively clear image, low image noise, good confidence to detect colorectal lesions; 3 points: fair image, moderate image noise, fair confidence to detect colorectal lesions; 2 points: slightly blurry image, large image noise, low confidence to detect colorectal lesions; 1 point: blurry image, heavy image noise, uninterpretable Different criteria were applied to 3D images: 5 points: smooth endoluminal wall, clear lesion morphology; 4 points: relatively smooth endoluminal wall, distinguishable lesion morphology; 3 points: irregular endoluminal wall, relatively distinguishable lesion morphology; 2 points: rough endoluminal wall, barely distinguishable lesion morphology; 1 point: highly rough endoluminal wall, indistinguishable lesion morphology Tumor detection and location The colonic and rectal tumor locations were recorded by the same radiologists mentioned above while assessing the image quality. The colon and rectum were divided into nine segments[ 16 ]: ileocecal junction, ascending colon, hepatic flexure, transverse colon, splenic flexure, descending colon, sigmoid colon, rectosigmoid junction, and rectum. If divergent opinions appeared, a consensus reading was performed to determine a final conclusion. An intergroup comparison was made between the tumor locations reported by the two radiologists and by a surgeon, using the operation result as the reference standard. Statistical analysis Data analysis was performed with SPSS 26.0 statistical software. For the normally distributed continuous variables, expressed as mean ± SD, the Paired sample t test was used, whereas for the non-normally distributed variables, expressed as median (Interquartile range, IQR), Wilcoxon sign rank test was used. The comparison of subjective scores between the control group and Auto-kVp groups used Wilcoxon sign rank test. The Kappa test was used to compare the inter-reader consistency and tumor location consistency between CTC and surgery: Kappa value ≥ 0.75, good consistency, 0.75 > Kappa value > 0.4, moderate consistency, Kappa value ≤ 0.4, poor consistency. P < 0.05 represented a statistically significant difference. Results Patient demographics After applying the inclusion and exclusion criteria, a total of 90 patients (44 male and 46 females; age range, 37–85 years; mean age 65.80 ± 8.90 years) were finally included in our study, with an average body mass index (BMI) of 23.26 ± 2.96 kg/m 2 (range, 16.67–30.11 kg/m 2 ) (Table 1 ). Table 1 Patient demographics SDCTC ULDCTC SDCTC ULDCTC Tube voltage (kVp) 120 80 120 100 Gender (n) Male 27 17 Female 23 23 Age (years) 66.32 ± 9.56 65.15 ± 8.08 Total body weight (kg) 59.86 ± 7.70 69.68 ± 8.19 BMI (kg/m 2 ) 21.62 ± 2.21 25.30 ± 2.48 Location (n) Colon 29 27 Rectum 21 13 Maximum diameter (cm) 4.0 (3.5, 5.0) 3.0 (2.4, 4.1) Lesion morphology (n) ulcerative 27 15 protruding 23 25 Age, total body weight and BMI (body mass index) are expressed as mean values ± standard deviations, maximum diameter is expressed as median (interquartile range, IQR). SDCTC standard dose CT colonography; ULDCTC ultra-low dose CT colonography Radiation dose The scan lengths (in mm) of the prone and supine images, respectively representing the SDCTC (120 kVp) and ULDCTC (80 or 100 kVp), had statistically insignificant difference (427.00 ± 36.61 vs 420.92 ± 30.85, 441.65 ± 40.22 vs 434.38 ± 46.36, both P >0.05). The mean CTDI vol and DLP with 80 kVp decreased by 70.46% and 71.43%, respectively compared with the same patient of 120 kVp (both P < 0.001, Table 2 ). The average ED for 80 kVp was 0.70 mSv, compared to 2.45 mSv for 120 kVp, resulting in a dose reduction of 71.43% ( P < 0.001). All cases (50/50) had an ED less than 1 mSv, which all met the ultra-low dose requirement. When it comes to the 100 kVp subgroup, the average CTDI vol , DLP, and ED were 1.29 mGy, 65.19 mGy·cm, and 0.98 mSv, compared to the 4.63 mGy, 241.78 mGy·cm, and 3.63 mSv for 120 kVp, resulting in a dose reduction of 72.14%, 73.04%, and 73.00%, respectively (all P < 0.001, Table 2 ). 40/40 cases had an ED less than 1.10 mSv, which was comparable to the dose of 80 kVp group. Table 2 Radiation dose comparison Group N BMI (kg/m 2 ) Tube voltage (kVp) CTDI vol (mGy) DLP (mGy·cm) ED (mSv) P SDCTC 50 21.62 ± 2.21 120 3.25 ± 0.61 163.10 ± 35.26 2.45 ± 0.53 P <0.001 ULDCTC 80 0.96 ± 0.12 46.59 ± 7.81 0.70 ± 0.12 SDCTC 40 25.30 ± 2.48 120 4.63 ± 0.90 241.78 ± 51.17 3.63 ± 0.77 P <0.001 ULDCTC 100 1.29 ± 0.11 65.19 ± 4.87 0.98 ± 0.07 SDCTC standard dose CT colonography; ULDCTC ultra-low dose CT colonography Quantitative image analysis The CT values of tumors with 80 kVp were significantly higher than those with 120 kVp, and the mean image noise of 80 kVp was significantly lower than that of 120 kVp (both P < 0.001), because the higher postset ASIR-V weight of 80 kVp significantly reduced image noise. Hence, the tumour SNR and CNR of 80 kVp were higher than those of 120 kVp ( P < 0.001, Fig. 2 ). In addition, the same results could be found in the comparison between 100 kVp and 120 kVp. The image standard deviations of tumors were significantly lower and their CT values, SNR and CNR values were significantly higher with 100 kVp than with 120 kVp (all P 0.05). A good consistency of subjective image quality existed among the two reviewers ( Kappa value 0.805ཞ0.923, P <0.001). In both groups, no images were found to be non-diagnostic (Fig. 3 ). Representative examples are presented in Figs. 4 and 5 . Green represents subjective score 5, blue represents subjective score 4, and yellow represents subjective score 3. Diagnostic performance of SDCTC and ULDCTC The locations of colorectal tumors reported in CTC and surgery are summarized in Table 3 . A total of 91 colorectal tumors were reported in surgery, including 82 CRC with one patient had two cancers (one in the ascending colon, and one in the rectum), 8 premalignant tubulovillous adenomas with high-grade dysplasia, and 1 benign tubular adenoma. Compared with surgical results, SDCTC and ULDCTC both detected 90 out of 91 tumors (98.9%), including the patient who had two tumors in the colon. The percentage of colorectal tumors detected in ULDCTC-80 kVp group were 100%, whereas only one tumor was missed in ULDCTC-100 kVp group (97.5%), which was confirmed to be a benign tubular adenoma in ileocecal junction by pathology. The minimal tumor diameters reported by surgery were 1.6 cm and 1.7 cm in 80 kVp and 100 kVp subgroups, respectively, which were two premalignant tubular adenomas been successfully detected on the ULDCTC as well. Both the SDCTC and ULDCTC had good consistency in determining tumor location with surgery reports ( Kappa value 0.718ཞ0.989, P <0.05). Table 3 Comparison of tumor location Group ileocecal junction ascending colon hepatic flexure transverse colon splenic flexure descending colon sigmoid colon rectosigmoid junction rectum 120 kVp 0 5 1 1 1 4 12 5 22 80 kVp 0 5 1 1 1 4 12 5 22 Surgery 0 5 1 1 1 3 10 6 24 Kappa 0.837(95% CI: 0.718ཞ0.957) P <0.001 1,2 120 kVp 1 3 3 5 0 0 13 2 13 100 kVp 1 3 3 5 0 0 13 2 13 Surgery 1 3 3 5 0 0 11 2 15 Kappa 0.868(95% CI:0.747ཞ0.989) 1,2 P <0.001 1,2 1 represents the comparison between 120 kVp and surgery, 2 represents the comparison between 80 kVp or 100 kVp and surgery. Discussion Our study realized individualized ultra-low dose CTC (ULDCTC) without degrading image quality and tumor diagnosis. The air injected during CTC examination, serving as a negative contrast, not only increased the contrast between the intestinal tract and lesions, but also provided a great potential for a dramatic dose reduction, making sub-mSv CTC technically feasible[ 17 ]. Our results demonstrated that for the CTC patients given 80 kVp, the ULDCTC could be achieved by combining 60% preset ASIR-V with a NI of 13, whereas for the subgroup of 100 kVp, the target could be achieved by combining 60% preset ASIR-V with a NI of 15. In our study, few patients were recommended 120 kVp by the Auto-prescription technique, might be due to the relatively small body habitus of the Asian population compared with patients in the western countries. Therefore, those patients given 120 kVp were excluded in order to ensure the ULDCTC image quality. Even though a few studies indeed realized a sub-mSv CTC under 120 kVp, their limitations were also obvious. For example, although a previous study by Yasuda et al.[ 18 ] achieved an even lower average ED of 0.64 mSv, its maximum ED reached up to 2.64 mSv, implying that a significant proportion of patients in their study received the radiation dosage higher than 1 mSv. Another case in point was that Cianci et al.[ 19 ] decreased tube current of CTC from a a quality reference of 55 milliamper second (mAs) to an ultra-low level of 25 mAs, resulting in a 63.2% effective dose reduction. However, 14 out of 82 patients (17.1%) in Cianci’s study received a median ED of 1.31 mSv, demonstrating that the ED in patients with large body sizes failed to reach an ultra-low level. On the contrary, the outperforming advantage of our study is that not only the mean effective dose (ED) reached the standard of ultra-low dose, but the ED for each patient in both 80 kVp and 100 kVp groups was lower than 1 mSv. At this point, our study realized an actual sub-mSv CTC imaging, with a personalized radiation dose below 1 mSv, which was superior to other previous research[ 17 – 19 ]. Furthermore, the body mass indices of our enrolled patients ranged from 16.67 to 30.11 kg/m 2 , which covered a relatively large population with different body sizes, indicating that our ULDCTC protocol had a great general applicability. In the context of individualized low-dose imaging, Li et al.[ 20 ] manually selected tube voltage based on the CTC patients’ BMI, resulting in a 63.6%, 44.6% and 32.1% ED reduction in patients scanned by 70 kVp, 80 kVp, and 100 kVp, respectively. Another study by Wei et al.[ 21 ] applied tailored tube voltage of 80 kVp and 110 kVp for patients BMI ≤ 21 kg/m 2 and > 21 kg/m 2 , respectively, to obtain a low-dose CT-guided hook wire localization for pulmonary nodules. However, the resulting average ED for patients whose BMI were more than 21 kg/m 2 was 2.33 mSv, indicating that the sub-mSv imaging was only achieved among the population with small body sizes, limited the clinical application value of this protocol. Even if BMI was the most frequent index for group divisions, the potential selection bias was unavoidable because of the subjective decision made during kVp selection. By contrast, our study utilized Auto prescription technique, which automatically gave tube voltage recommendation on the basis of anteroposterior (AP) and lateral scout images, so that the individual tube voltage could be given objectively in actual, avoiding manual grouping bias[ 22 ]. In addition, the above-mentioned studies only achieved personalized CT imaging, but their average EDs were higher than 1 mSv. Moreover, preset ASIR-V could impact tube current (milliampere, mA) without affecting the CT value, using automatic tube current modulation[ 23 ]. According to a research by Zhu et al. [ 24 ], the image quality of non-contrast CT abdomen could be maintained when the post-set ASIR-V strength was equal to or higher than that of the preset ASIR-V. Thus, in the Auto-kVp group, the preset and post-set ASIR-V weights were increased to 60% and 80%, respectively. Overall, preset ASIR-V can prospectively reduce the radiation dose, whereas post-set ASIR-V can substantially improve the quality of image by its strong denoising ability, so the combined changes of preset and post-set ASIR-V weight were applied in our study. Additionally, NI is a parameter for maintaining the balance between image quality and radiation dose. Since a One-Size-Fits-All method to scan all patients was technically inappropriate, different noise indices were used in our study for the Auto-kVp group. According to Zhao et al.’s study[ 25 ], NI was increased to 15 for patients whose BMI ≥ 24 kg/m 2 in hepatic CTA. Although NI could be increased even larger to achieve a considerable dose reduction[ 26 ], the image quality would be heavily deteriorated, such as affecting the delineation of colorectal tumor and the margin sharpness, which led to low subjective scores. Therefore, our study only increased NI to 15 for patients scanned by 100 kVp, not only to match the individual ED to a sub-mSv level, but keeping the overall image quality at a diagnostic acceptable level as well. An interesting point appeared in our study was that prone and supine CTC images reported the same result in defining colonic tumor locations. In other words, our results indicated that the supine ULDCTC images had comparable performance with the prone SDCTC images. Furthermore, our study divided colon and rectum into nine segments, which was more detailed than four or seven-segment division in prior studies, fulfilling the preoperative requirement of precise tumor localization[ 6 , 27 ]. In addition, it is worth noting that 16 out of 90 patients (17.7%) in our study had incomplete colonoscopy due to occlusive CRC, leaving 87/810 (10.7%) colon segments inaccessible to colonoscopy. CTC provided an additional opportunity to observe the colon proximal to the tumor, which revealed the clinical significance of CTC, even if no synchronous proximal polyp or cancer was detected among the inaccessible colon segments. Despite of the good results, our study has several limitations that should be acknowledged. Firstly, this was a study from a single center with a relatively small study population. Therefore, multi-center studies with larger number of participants should be conducted for further investigation. Secondly, the bowel preparation was relatively suboptimal due to no fecal tagging. However, this may be due to the different standards of bowel preparation in different nations. Secondly, for image reconstruction, we selected the same post-set ASIR-V weight for both 80 kVp and 100 kVp images, even though they used different NI. The reason is that the more essential focus of our study was to explore how to realize ULDCTC rather than to explore the effect of iterative reconstruction algorithm on the image quality. Thus, a more specific comparison of utilizing variable strengths of post-set ASIR-V should be considered in the future ULDCTC research. In addition, our study was unable to apply deep learning reconstruction algorithms (DL) to the ULDCTC images, due to the software upgrade issue. Hence, comparing the denoising effect of DL with post-set ASIR-V in ULDCTC is a potential aspect to be explored. Conclusion In conclusion, the combination of automatic tube voltage selection, 60% preset ASIR-V and noise index at 13 or 15 can achieve ULDCTC, with the mean effective dose less than 1 mSv in the patient population within a wide range of BMI values. The diagnostic performance was revealed by the high detection rate and good consistency of ULDCTC in locating colorectal cancer with surgical results. Therefore, the modified sub-mSv CTC protocol has the potential to be recommended in clinical practice. Declarations Author Contribution J.Z wrote the main manuscript text. M.H. and Q.C. were responsible for methodology and visualization. S.W. and J.L. reviewed and edited the manuscript. Y.L. was responsible for the conceptualization and supervision of the study. Y.Z. was responsible for the validation and supervision. W.W. was responsible for data curation, software and manuscript reviewing. All authors reviewed the manuscript. Data Availability Data is provided within the manuscript. References Valletta R, Faccioli N, Bonatti M et al (2022) Role of CT colonography in differentiating sigmoid cancer from chronic diverticular disease. Jpn J Radiol 40(1):48-55. doi: 10.1007/s11604-021-01176-8. 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Updates Surg 74(1):137-144. doi:10.1007/s13304-021-01180-7 Liu JJ, Xue HD, Liu W et al (2021) CT colonography with spectral filtration and advanced modeled iterative reconstruction in the third-generation dual-source CT: image quality, radiation dose and performance in clinical utility. Acad Radiol 28(5):e127-e136. doi: 10.1016/j.acra.2020.03.040. Yasuda T, Honda T, Utano K et al (2022) Diagnostic accuracy of ultra-low-dose CT colonography for the detection of colorectal polyps: a feasibility study. Jpn J Radiol 40(8):831-839. doi: 10.1007/s11604-022-01266-1. Cianci R, Delli Pizzi A, Esposito G et al (2019) Ultra-low dose CT colonography with automatic tube current modulation and sinogram-affirmed iterative reconstruction: Effects on radiation exposure and image quality. 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Zhao Y, Li D, Liu Z, Geng X, Zhang T, Xu Y (2021) Comparison of image quality and radiation dose using different pre-ASiR-V and post-ASiR-V levels in coronary computed tomography angiography. J Xray Sci Technol 29(1):125-134. doi: 10.3233/XST-200754. Zhu Z, Zhao Y, Zhao X et al (2021) Impact of preset and postset adaptive statistical iterative reconstruction-V on image quality in nonenhanced abdominal-pelvic CT on wide-detector revolution CT. Quant Imaging Med Surg 11(1):264-275. doi:10.21037/qims-19-945 Zhao S, Liu ZC, Zhao YX, Zhang TL, Zuo ZW (2023) A feasibility study of different GSI noise indexes and concentrations of contrast medium in hepatic CT angiography of overweight patients: image quality, radiation dose, and iodine intake. Jpn J Radiol 41(6):669-679. doi: 10.1007/s11604-022-01384-w. Cao L, Liu X, Li J et al (2021) A study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions. Br J Radiol 94(1118):20201086. doi: 10.1259/bjr.20201086. Taguchi N, Oda S, Imuta M et al (2018) Model-based Iterative Reconstruction in Low-radiation-dose Computed Tomography Colonography: Preoperative Assessment in Patients with Colorectal Cancer. Acad Radiol 25(4):415-422. doi: 10.1016/j.acra.2017.10.008. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Sep, 2024 Read the published version in Abdominal Radiology → Version 1 posted Editorial decision: Revision requested 15 Aug, 2024 Reviews received at journal 11 Aug, 2024 Reviewers agreed at journal 30 Jun, 2024 Reviews received at journal 27 Jun, 2024 Reviewers agreed at journal 17 Jun, 2024 Reviewers invited by journal 16 Jun, 2024 Editor assigned by journal 14 Jun, 2024 Submission checks completed at journal 14 Jun, 2024 First submitted to journal 13 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4578840","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":320921277,"identity":"c9d2e132-096e-4765-a041-a1803920c73e","order_by":0,"name":"Jingyi Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingyi","middleName":"","lastName":"Zhang","suffix":""},{"id":320921278,"identity":"b644c326-fe94-4c0f-a275-df0cca66c1a2","order_by":1,"name":"Mengting Hu","email":"","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengting","middleName":"","lastName":"Hu","suffix":""},{"id":320921279,"identity":"54b5ada1-fd43-4903-972d-58689947043c","order_by":2,"name":"Qiye Cheng","email":"","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiye","middleName":"","lastName":"Cheng","suffix":""},{"id":320921280,"identity":"1c4bc875-34f4-4615-8ad7-a248a1e533bd","order_by":3,"name":"Shigeng Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shigeng","middleName":"","lastName":"Wang","suffix":""},{"id":320921281,"identity":"364cd1e9-1120-4332-afd6-5567126de8a5","order_by":4,"name":"Yijun Liu","email":"","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yijun","middleName":"","lastName":"Liu","suffix":""},{"id":320921282,"identity":"35545750-c1bd-46ed-85fd-e82bdff9bdf6","order_by":5,"name":"Yujing Zhou","email":"","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yujing","middleName":"","lastName":"Zhou","suffix":""},{"id":320921283,"identity":"ec250551-058e-43b6-b504-99f52673cc72","order_by":6,"name":"Jianying Li","email":"","orcid":"","institution":"CT Research, GE Healthcare, Dalian","correspondingAuthor":false,"prefix":"","firstName":"Jianying","middleName":"","lastName":"Li","suffix":""},{"id":320921284,"identity":"4444b613-6cad-4525-a7d5-d05524021f36","order_by":7,"name":"Wei Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIie2ROwrCQBBAJwQSi0XbiOgZRoQgGBSPsgTWJoVHCAiTxgPYeQWPYAxq4wECEVQC1ikVUpgItuuWgvuqKebNF0Cj+UmMEABNsOxFfC3QG6srTXbw+6u58FVbmdBzArfDil1dQQ4eF+QU8xEnR/gDD7cm2Ml+I1VOMbVXOOPE8jgP8NwEJkQqU9yUU8Yw4WRXXQK8m+AwV65cbpSVtQLVLkNMjPCrkhqUQa00KgVUlMmJR88lzgZUH3mJwre+7dKOjjk+ylF3HVWvfJTeuGUnB6nyYRp+Iksl/T2gaqJGo9H8IS+Cx1BWNWWlDwAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Dalian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2024-06-14 02:02:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4578840/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4578840/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00261-024-04557-5","type":"published","date":"2024-09-14T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60186370,"identity":"5307cec3-a5f3-4912-90b4-d837cf4ca1ca","added_by":"auto","created_at":"2024-07-12 18:51:35","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69441,"visible":true,"origin":"","legend":"\u003cp\u003eStudy pipeline\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4578840/v1/0af212b2944936431294ed50.jpg"},{"id":60185765,"identity":"b1939f39-4079-406c-af7f-aacc4ce81b90","added_by":"auto","created_at":"2024-07-12 18:43:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48701,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of tumor SNR and CNR\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSNR\u003c/em\u003e signal-to-noise ratio, \u003cem\u003eCNR\u003c/em\u003econtrast-to-noise ratio\u003c/p\u003e\n\u003cp\u003e***\u003cem\u003eP\u003c/em\u003e＜0.05, indicates significant\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4578840/v1/a170495bb7c7a90623cf939b.jpg"},{"id":60185767,"identity":"5bd6ac98-602f-4dd6-ab5e-5d403138ea59","added_by":"auto","created_at":"2024-07-12 18:43:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51466,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of subjective scores\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4578840/v1/3611bdc4aa9e0620a73dd4b6.jpg"},{"id":60185769,"identity":"22d25749-3bfc-49fa-bb9c-e533fa2c5145","added_by":"auto","created_at":"2024-07-12 18:43:35","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":97712,"visible":true,"origin":"","legend":"\u003cp\u003eA 70-year-old female with a 3.5-cm tubular adenocarcinoma in the distal rectum (BMI 22.89 kg/m\u003csup\u003e2\u003c/sup\u003e). The tumor is clearly depicted (red arrow) on the (a) 2D MPR reconstructed by 30% post-set ASIR-V, (b) 3D CTVC, and (c) 3D Raysum image, with an ED of 2.41 mSv. In the supine position, the tumor is well visualized (red arrow) on the (d) 2D MPR reconstructed by 80% post-set ASIR-V, (e) 3D CTVC, and (f) 3D Raysum images, with an ED of 0.70 mSv. After reducing the radiation dose by 70.95%, the image qualities of the supine images were still comparable to those of prone images.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4578840/v1/3269230fdc66ad273a868c7f.jpg"},{"id":60185770,"identity":"02025459-9d95-4ef6-bbef-b49f1b68ac24","added_by":"auto","created_at":"2024-07-12 18:43:35","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":117205,"visible":true,"origin":"","legend":"\u003cp\u003eA 67-year-old male with a 4.0-cm tubular adenocarcinoma in the sigmoid colon (BMI 27.55 kg/m\u003csup\u003e2\u003c/sup\u003e). The tumor is clearly depicted (red arrow) on the (a) 2D MPR reconstructed by 30% post-set ASIR-V, (b) 3D CTVC, and (c) 3D Raysum image, with an ED of 3.96 mSv. In the supine position, the tumor is well visualized (red arrow) on the (d) 2D MPR, (e) 3D CTVC, and (f) 3D Raysum images, with an ED of 0.93 mSv. After reducing the radiation dose by 76.52%, the image qualities of the supine images were still comparable to those of prone images.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4578840/v1/54722c91ddc66257a52d14b9.jpg"},{"id":64619862,"identity":"93c6ab44-d3f9-4d02-a384-c09099ac3031","added_by":"auto","created_at":"2024-09-16 16:17:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":961034,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4578840/v1/69169031-35a8-4d08-bf46-620e53036264.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Achieving sub-milliSievert CT colonography for accurate colorectal tumor detection using smart examination protocols: a prospective self-controlled study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) has become the third leading cause of death in patients diagnosed with malignant neoplasms in the digestive system[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the recent decades, the decrease in CRC mortality could be attributed to the improvements in the early detection[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Optical colonoscopy (OC), serving as the gold standard, plays an important role in the reliable diagnosis of CRC[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, the invasive nature of the procedure and high requirement of the physician\u0026rsquo;s experience narrow the application of OC in some circumstances[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Nowadays, CT Colonography (CTC), also known as virtual colonoscopy, becomes a compensation for incomplete colonoscopy (IC), because of its ability to provide the entire colon evaluation, especially for the proximal colon obstructed by occlusive CRC[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, an increasing number of organizations, including American College of Gastroenterology, the European Society of Gastrointestinal Endoscopy and European Society of Gastrointestinal and Abdominal Radiology, have involved CTC as a routine screening test for CRC, which highlighted the increasing clinical value of CTC[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the routine CTC requires two scans in prone and supine positions, causing the risk of radiation exposure a non-negligible issue. Since the potential harm from radiation has raised great awareness, exploring effective dose reduction approaches has always been a hot topic in the field of medical imaging.\u003c/p\u003e \u003cp\u003eWith the continuous development of CT technology, various tools have been introduced to decrease radiation dose without sacrificing image quality. One of them is the automated kilovoltage peak selection (Auto-kVp), been known as Auto-prescription technique, provides the patient with the most appropriate tube voltage based on the x-ray attenuation features derived from patient\u0026rsquo;s body habitus[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Taking advantage of Auto-kVp can realize individualized low-dose imaging, which has been studied in the thoracoabdominal aorta and portal vein[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Another dose reduction approach, been frequently neglected, is adjusting the preset strength of adaptive statistical iterative reconstruction algorithm-V (preset ASIR-V). As the weight of preset ASIR-V increases, the radiation dose required to achieve the same noise level for a given patient decreases exponentially[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, increasing noise index (NI) can also decrease radiation dose. The increasing image noise caused by the higher NI can be dealt with applying high strength of post-set ASIR-V, which has great denoising capacity to compensate for the image degradation[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. To the best of our knowledge, combining the above-mentioned dose-reduction techniques in CRC patients has not been paid much attention.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to assess the feasibility of combining Auto-prescription technique, higher preset ASIR-V and higher NI to realize individualized sub-mSv CTC, as well as evaluating its diagnostic performance by the detection rate of CRC under ultra-low dose protocol and the accuracy of defining tumor location in comparison with the results of surgery.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e This prospective study was approved by the Institutional Review Board of our Hospital. Written informed consent for study participation was obtained from all involved patients.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eDuring the period of December 2023 to April 2024, a total of 110 consecutive patients who were scheduled to undergo CTC examination with clinically suspected CRC in our hospital were prospectively enrolled in this study. The exclusion criteria were as follows: patients with poor bowel preparation, unable to tolerate gas injection, metal artifacts from lumbar implants or total hip replacement, and patients who were recommended to use 120 kVp tube voltage by Auto prescription technique. Among the 110 patients, 11 patients who had no pathological report, 5 patients who had maximum tumor diameter less than 10 mm, and another 4 patients who underwent chemotherapy for treatment were further excluded from analysis. Thus, 90 patients finally made up of our study population (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSub-milliSievert CT Colonography\u003c/h2\u003e \u003cp\u003ePrior to the CTC, a clear liquid diet was restricted to all patients three days before the examination. A standard bowel preparation was conducted on all patients with a polyethylene glycol electrolyte powder (Hygecon, Jiangxi Hygecon Pharmecutical) as laxative, and without fecal tagging. On the day of CTC examination, all patients had fasted overnight and lied in the left lateral decubitus position on the CT examination table. A senior radiologist assisted by a dedicated nurse manually inflated CO\u003csub\u003e2\u003c/sub\u003e into the colon via a flexible rectal tube, until the patient experienced abdominal discomfort. The CTC scanning started with patients in prone position with care taken to avoid abdominal compression by putting a pillow under the chest. After the patients rotated into the supine position, the colon distension was checked based on the supine scout image. If the residual air was inadequate, further inflation would be performed before the second scan to ensure the ULDCTC image quality.\u003c/p\u003e \u003cp\u003eAll CTC examinations were performed with a 256-row spectral CT scanner (Revolution CT, \u003cem\u003eGE HealthCare, Milwaukee, WI, USA\u003c/em\u003e)using the following parameters: detector width, 80 mm; pitch, 0.992; rotation time, 0.5 s; slice thickness, 5 mm; tube voltage, 120 kVp in the prone position [standard dose CTC (SDCTC)] and Auto-prescription kVp selection in the supine position [ultra-low dose CTC (ULDCTC)] with subgroups of 80 kVp or 100 kVp; tube current, Smart mA mode (range, 20\u0026ndash;450 mA); preset ASIR-V, 30% for the SDCTC and 60% for the ULDCTC. Radiation doses were controlled by using noise index (NI) setting: 13 for the 120 kVp group and 80 kVp subgroup and 15 for the 100 kVp subgroup. All raw data were reconstructed using a STND kernel with 30% and 80% post-set ASIR-V for the SDCTC and the ULDCTC, respectively. The reconstruction thickness and increment were both 0.625 mm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eRadiation dose metrics\u003c/h2\u003e \u003cp\u003eThe volume CT dose index (CTDI\u003csub\u003evol\u003c/sub\u003e) and dose-length product (DLP) were recorded for comparison. Moreover, the scan length was obtained from the starting and ending points for comparison between the prone and supine positions. Furthermore, the effective dose (ED) was calculated from DLP using 0.015 mSv/ (mGy\u0026middot;cm) as the conversion factor in abdomen, recommended by the European guidelines on quality criteria[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative evaluation\u003c/h2\u003e \u003cp\u003eAll axial images were transferred to an AW 4.7 (\u003cem\u003eGE HealthCare, Milwaukee, WI, USA)\u003c/em\u003e workstation for the following reformatted images: two-dimensional (2D) Multi-planar Reformation (MPR), three-dimensional (3D) endoluminal view from CT Virtual Colonoscopy (CTVC) and 3D Raysum. On the axial images, the circular regions of interest (ROIs) were drawn on the homogenous area of the colorectal tumor and intraluminal air to measure the CT value and the standard deviation (SD) values, in order to calculate the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) of tumors. The formulas were as follows: SNR=CT \u003csub\u003etumor\u003c/sub\u003e/SD \u003csub\u003etumor\u003c/sub\u003e, CNR༝(CT \u003csub\u003etumor\u003c/sub\u003e-CT \u003csub\u003eintraluminal air\u003c/sub\u003e)/SD \u003csub\u003eintraluminal air\u003c/sub\u003e. The tumor ROIs were depicted on the slice of maximum diameter, and were manually placed on the prone and supine images, keeping the ROI size accounting for 70%-80% of the tumor. The measurements on three consecutive slices were recorded and averaged to avoid measuring bias, and the ROI sizes ranged from 50\u0026ndash;100 mm\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eQualitative evaluation\u003c/h2\u003e \u003cp\u003eTwo abdominal radiologists (one junior radiologist with 5-year experience, and one senior radiologist with 8-year experience in abdominal imaging), who were blinded to the group division, independently performed subjective image quality assessment on the workstation. The 2D MPR images and 3D CTVC images were graded using a five-point scale. The images scored greater than or equal to 3 points were considered diagnostically acceptable. The marking criteria were demonstrated as follows:\u003c/p\u003e \u003cp\u003e5 points: clear image, virtually no image noise, excellent confidence to detect colorectal lesions;\u003c/p\u003e \u003cp\u003e4 points: relatively clear image, low image noise, good confidence to detect colorectal lesions;\u003c/p\u003e \u003cp\u003e3 points: fair image, moderate image noise, fair confidence to detect colorectal lesions;\u003c/p\u003e \u003cp\u003e2 points: slightly blurry image, large image noise, low confidence to detect colorectal lesions;\u003c/p\u003e \u003cp\u003e1 point: blurry image, heavy image noise, uninterpretable\u003c/p\u003e \u003cp\u003eDifferent criteria were applied to 3D images:\u003c/p\u003e \u003cp\u003e5 points: smooth endoluminal wall, clear lesion morphology;\u003c/p\u003e \u003cp\u003e4 points: relatively smooth endoluminal wall, distinguishable lesion morphology;\u003c/p\u003e \u003cp\u003e3 points: irregular endoluminal wall, relatively distinguishable lesion morphology;\u003c/p\u003e \u003cp\u003e2 points: rough endoluminal wall, barely distinguishable lesion morphology;\u003c/p\u003e \u003cp\u003e1 point: highly rough endoluminal wall, indistinguishable lesion morphology\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTumor detection and location\u003c/h2\u003e \u003cp\u003eThe colonic and rectal tumor locations were recorded by the same radiologists mentioned above while assessing the image quality. The colon and rectum were divided into nine segments[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]: ileocecal junction, ascending colon, hepatic flexure, transverse colon, splenic flexure, descending colon, sigmoid colon, rectosigmoid junction, and rectum. If divergent opinions appeared, a consensus reading was performed to determine a final conclusion. An intergroup comparison was made between the tumor locations reported by the two radiologists and by a surgeon, using the operation result as the reference standard.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData analysis was performed with SPSS 26.0 statistical software. For the normally distributed continuous variables, expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, the Paired sample t test was used, whereas for the non-normally distributed variables, expressed as median (Interquartile range, IQR), Wilcoxon sign rank test was used. The comparison of subjective scores between the control group and Auto-kVp groups used Wilcoxon sign rank test. The \u003cem\u003eKappa\u003c/em\u003e test was used to compare the inter-reader consistency and tumor location consistency between CTC and surgery: \u003cem\u003eKappa\u003c/em\u003e value\u0026thinsp;\u0026ge;\u0026thinsp;0.75, good consistency, 0.75\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eKappa\u003c/em\u003e value\u0026thinsp;\u0026gt;\u0026thinsp;0.4, moderate consistency, \u003cem\u003eKappa\u003c/em\u003e value\u0026thinsp;\u0026le;\u0026thinsp;0.4, poor consistency. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 represented a statistically significant difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePatient demographics\u003c/h2\u003e \u003cp\u003eAfter applying the inclusion and exclusion criteria, a total of 90 patients (44 male and 46 females; age range, 37\u0026ndash;85 years; mean age 65.80\u0026thinsp;\u0026plusmn;\u0026thinsp;8.90 years) were finally included in our study, with an average body mass index (BMI) of 23.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.96 kg/m\u003csup\u003e2\u003c/sup\u003e (range, 16.67\u0026ndash;30.11 kg/m\u003csup\u003e2\u003c/sup\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient demographics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSDCTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eULDCTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSDCTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eULDCTC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTube voltage (kVp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e66.32\u0026thinsp;\u0026plusmn;\u0026thinsp;9.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e65.15\u0026thinsp;\u0026plusmn;\u0026thinsp;8.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal body weight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e59.86\u0026thinsp;\u0026plusmn;\u0026thinsp;7.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e69.68\u0026thinsp;\u0026plusmn;\u0026thinsp;8.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e21.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e25.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLocation (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRectum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMaximum diameter (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4.0 (3.5, 5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3.0 (2.4, 4.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLesion morphology (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eulcerative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprotruding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAge, total body weight and BMI (body mass index) are expressed as mean values\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations, maximum diameter is expressed as median (interquartile range, IQR). \u003cem\u003eSDCTC\u003c/em\u003e standard dose CT colonography; \u003cem\u003eULDCTC\u003c/em\u003e ultra-low dose CT colonography\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRadiation dose\u003c/h2\u003e \u003cp\u003eThe scan lengths (in mm) of the prone and supine images, respectively representing the SDCTC (120 kVp) and ULDCTC (80 or 100 kVp), had statistically insignificant difference (427.00\u0026thinsp;\u0026plusmn;\u0026thinsp;36.61 vs 420.92\u0026thinsp;\u0026plusmn;\u0026thinsp;30.85, 441.65\u0026thinsp;\u0026plusmn;\u0026thinsp;40.22 vs 434.38\u0026thinsp;\u0026plusmn;\u0026thinsp;46.36, both \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05). The mean CTDI\u003csub\u003evol\u003c/sub\u003e and DLP with 80 kVp decreased by 70.46% and 71.43%, respectively compared with the same patient of 120 kVp (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The average ED for 80 kVp was 0.70 mSv, compared to 2.45 mSv for 120 kVp, resulting in a dose reduction of 71.43% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). All cases (50/50) had an ED less than 1 mSv, which all met the ultra-low dose requirement. When it comes to the 100 kVp subgroup, the average CTDI\u003csub\u003evol\u003c/sub\u003e, DLP, and ED were 1.29 mGy, 65.19 mGy\u0026middot;cm, and 0.98 mSv, compared to the 4.63 mGy, 241.78 mGy\u0026middot;cm, and 3.63 mSv for 120 kVp, resulting in a dose reduction of 72.14%, 73.04%, and 73.00%, respectively (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). 40/40 cases had an ED less than 1.10 mSv, which was comparable to the dose of 80 kVp group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRadiation dose comparison\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003cp\u003e(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTube voltage (kVp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCTDI\u003csub\u003evol\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(mGy)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDLP (mGy\u0026middot;cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eED\u003c/p\u003e \u003cp\u003e(mSv)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDCTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e21.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e163.10\u0026thinsp;\u0026plusmn;\u0026thinsp;35.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eULDCTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.59\u0026thinsp;\u0026plusmn;\u0026thinsp;7.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSDCTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e25.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e241.78\u0026thinsp;\u0026plusmn;\u0026thinsp;51.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eULDCTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.19\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSDCTC\u003c/em\u003e standard dose CT colonography; \u003cem\u003eULDCTC\u003c/em\u003e ultra-low dose CT colonography\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative image analysis\u003c/h2\u003e \u003cp\u003eThe CT values of tumors with 80 kVp were significantly higher than those with 120 kVp, and the mean image noise of 80 kVp was significantly lower than that of 120 kVp (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), because the higher postset ASIR-V weight of 80 kVp significantly reduced image noise. Hence, the tumour SNR and CNR of 80 kVp were higher than those of 120 kVp (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In addition, the same results could be found in the comparison between 100 kVp and 120 kVp. The image standard deviations of tumors were significantly lower and their CT values, SNR and CNR values were significantly higher with 100 kVp than with 120 kVp (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eQualitative image analysis\u003c/h2\u003e \u003cp\u003eNo significant difference was observed in the subjective score between the ULDCTC and SDCTC, no matter in 2D or 3D images (all \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05). A good consistency of subjective image quality existed among the two reviewers (\u003cem\u003eKappa value\u003c/em\u003e 0.805ཞ0.923, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). In both groups, no images were found to be non-diagnostic (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Representative examples are presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eGreen represents subjective score 5, blue represents subjective score 4, and yellow represents subjective score 3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic performance of SDCTC and ULDCTC\u003c/h2\u003e \u003cp\u003eThe locations of colorectal tumors reported in CTC and surgery are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. A total of 91 colorectal tumors were reported in surgery, including 82 CRC with one patient had two cancers (one in the ascending colon, and one in the rectum), 8 premalignant tubulovillous adenomas with high-grade dysplasia, and 1 benign tubular adenoma. Compared with surgical results, SDCTC and ULDCTC both detected 90 out of 91 tumors (98.9%), including the patient who had two tumors in the colon. The percentage of colorectal tumors detected in ULDCTC-80 kVp group were 100%, whereas only one tumor was missed in ULDCTC-100 kVp group (97.5%), which was confirmed to be a benign tubular adenoma in ileocecal junction by pathology. The minimal tumor diameters reported by surgery were 1.6 cm and 1.7 cm in 80 kVp and 100 kVp subgroups, respectively, which were two premalignant tubular adenomas been successfully detected on the ULDCTC as well. Both the SDCTC and ULDCTC had good consistency in determining tumor location with surgery reports (\u003cem\u003eKappa value\u003c/em\u003e 0.718ཞ0.989, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of tumor location\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eileocecal\u003c/p\u003e \u003cp\u003ejunction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eascending\u003c/p\u003e \u003cp\u003ecolon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehepatic\u003c/p\u003e \u003cp\u003eflexure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003etransverse\u003c/p\u003e \u003cp\u003ecolon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003esplenic\u003c/p\u003e \u003cp\u003eflexure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003edescending\u003c/p\u003e \u003cp\u003ecolon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003esigmoid\u003c/p\u003e \u003cp\u003ecolon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003erectosigmoid junction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003erectum\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e120 kVp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80 kVp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKappa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003e0.837(95% CI: 0.718ཞ0.957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;0.001\u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e120 kVp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100 kVp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKappa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003e0.868(95% CI:0.747ཞ0.989)\u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;0.001\u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e1 represents the comparison between 120 kVp and surgery, 2 represents the comparison between 80 kVp or 100 kVp and surgery.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study realized individualized ultra-low dose CTC (ULDCTC) without degrading image quality and tumor diagnosis. The air injected during CTC examination, serving as a negative contrast, not only increased the contrast between the intestinal tract and lesions, but also provided a great potential for a dramatic dose reduction, making sub-mSv CTC technically feasible[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our results demonstrated that for the CTC patients given 80 kVp, the ULDCTC could be achieved by combining 60% preset ASIR-V with a NI of 13, whereas for the subgroup of 100 kVp, the target could be achieved by combining 60% preset ASIR-V with a NI of 15.\u003c/p\u003e \u003cp\u003eIn our study, few patients were recommended 120 kVp by the Auto-prescription technique, might be due to the relatively small body habitus of the Asian population compared with patients in the western countries. Therefore, those patients given 120 kVp were excluded in order to ensure the ULDCTC image quality. Even though a few studies indeed realized a sub-mSv CTC under 120 kVp, their limitations were also obvious. For example, although a previous study by Yasuda et al.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] achieved an even lower average ED of 0.64 mSv, its maximum ED reached up to 2.64 mSv, implying that a significant proportion of patients in their study received the radiation dosage higher than 1 mSv. Another case in point was that Cianci et al.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] decreased tube current of CTC from a a quality reference of 55 milliamper second (mAs) to an ultra-low level of 25 mAs, resulting in a 63.2% effective dose reduction. However, 14 out of 82 patients (17.1%) in Cianci\u0026rsquo;s study received a median ED of 1.31 mSv, demonstrating that the ED in patients with large body sizes failed to reach an ultra-low level. On the contrary, the outperforming advantage of our study is that not only the mean effective dose (ED) reached the standard of ultra-low dose, but the ED for each patient in both 80 kVp and 100 kVp groups was lower than 1 mSv. At this point, our study realized an actual sub-mSv CTC imaging, with a personalized radiation dose below 1 mSv, which was superior to other previous research[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, the body mass indices of our enrolled patients ranged from 16.67 to 30.11 kg/m\u003csup\u003e2\u003c/sup\u003e, which covered a relatively large population with different body sizes, indicating that our ULDCTC protocol had a great general applicability.\u003c/p\u003e \u003cp\u003eIn the context of individualized low-dose imaging, Li et al.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] manually selected tube voltage based on the CTC patients\u0026rsquo; BMI, resulting in a 63.6%, 44.6% and 32.1% ED reduction in patients scanned by 70 kVp, 80 kVp, and 100 kVp, respectively. Another study by Wei et al.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] applied tailored tube voltage of 80 kVp and 110 kVp for patients BMI\u0026thinsp;\u0026le;\u0026thinsp;21 kg/m\u003csup\u003e2\u003c/sup\u003e and \u0026gt;\u0026thinsp;21 kg/m\u003csup\u003e2\u003c/sup\u003e, respectively, to obtain a low-dose CT-guided hook wire localization for pulmonary nodules. However, the resulting average ED for patients whose BMI were more than 21 kg/m\u003csup\u003e2\u003c/sup\u003e was 2.33 mSv, indicating that the sub-mSv imaging was only achieved among the population with small body sizes, limited the clinical application value of this protocol. Even if BMI was the most frequent index for group divisions, the potential selection bias was unavoidable because of the subjective decision made during kVp selection. By contrast, our study utilized Auto prescription technique, which automatically gave tube voltage recommendation on the basis of anteroposterior (AP) and lateral scout images, so that the individual tube voltage could be given objectively in actual, avoiding manual grouping bias[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, the above-mentioned studies only achieved personalized CT imaging, but their average EDs were higher than 1 mSv.\u003c/p\u003e \u003cp\u003eMoreover, preset ASIR-V could impact tube current (milliampere, mA) without affecting the CT value, using automatic tube current modulation[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. According to a research by Zhu et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], the image quality of non-contrast CT abdomen could be maintained when the post-set ASIR-V strength was equal to or higher than that of the preset ASIR-V. Thus, in the Auto-kVp group, the preset and post-set ASIR-V weights were increased to 60% and 80%, respectively. Overall, preset ASIR-V can prospectively reduce the radiation dose, whereas post-set ASIR-V can substantially improve the quality of image by its strong denoising ability, so the combined changes of preset and post-set ASIR-V weight were applied in our study. Additionally, NI is a parameter for maintaining the balance between image quality and radiation dose. Since a One-Size-Fits-All method to scan all patients was technically inappropriate, different noise indices were used in our study for the Auto-kVp group. According to Zhao et al.\u0026rsquo;s study[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], NI was increased to 15 for patients whose BMI\u0026thinsp;\u0026ge;\u0026thinsp;24 kg/m\u003csup\u003e2\u003c/sup\u003e in hepatic CTA. Although NI could be increased even larger to achieve a considerable dose reduction[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], the image quality would be heavily deteriorated, such as affecting the delineation of colorectal tumor and the margin sharpness, which led to low subjective scores. Therefore, our study only increased NI to 15 for patients scanned by 100 kVp, not only to match the individual ED to a sub-mSv level, but keeping the overall image quality at a diagnostic acceptable level as well.\u003c/p\u003e \u003cp\u003eAn interesting point appeared in our study was that prone and supine CTC images reported the same result in defining colonic tumor locations. In other words, our results indicated that the supine ULDCTC images had comparable performance with the prone SDCTC images. Furthermore, our study divided colon and rectum into nine segments, which was more detailed than four or seven-segment division in prior studies, fulfilling the preoperative requirement of precise tumor localization[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In addition, it is worth noting that 16 out of 90 patients (17.7%) in our study had incomplete colonoscopy due to occlusive CRC, leaving 87/810 (10.7%) colon segments inaccessible to colonoscopy. CTC provided an additional opportunity to observe the colon proximal to the tumor, which revealed the clinical significance of CTC, even if no synchronous proximal polyp or cancer was detected among the inaccessible colon segments.\u003c/p\u003e \u003cp\u003eDespite of the good results, our study has several limitations that should be acknowledged. Firstly, this was a study from a single center with a relatively small study population. Therefore, multi-center studies with larger number of participants should be conducted for further investigation. Secondly, the bowel preparation was relatively suboptimal due to no fecal tagging. However, this may be due to the different standards of bowel preparation in different nations. Secondly, for image reconstruction, we selected the same post-set ASIR-V weight for both 80 kVp and 100 kVp images, even though they used different NI. The reason is that the more essential focus of our study was to explore how to realize ULDCTC rather than to explore the effect of iterative reconstruction algorithm on the image quality. Thus, a more specific comparison of utilizing variable strengths of post-set ASIR-V should be considered in the future ULDCTC research. In addition, our study was unable to apply deep learning reconstruction algorithms (DL) to the ULDCTC images, due to the software upgrade issue. Hence, comparing the denoising effect of DL with post-set ASIR-V in ULDCTC is a potential aspect to be explored.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the combination of automatic tube voltage selection, 60% preset ASIR-V and noise index at 13 or 15 can achieve ULDCTC, with the mean effective dose less than 1 mSv in the patient population within a wide range of BMI values. The diagnostic performance was revealed by the high detection rate and good consistency of ULDCTC in locating colorectal cancer with surgical results. Therefore, the modified sub-mSv CTC protocol has the potential to be recommended in clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.Z wrote the main manuscript text. M.H. and Q.C. were responsible for methodology and visualization. S.W. and J.L. reviewed and edited the manuscript. Y.L. was responsible for the conceptualization and supervision of the study. Y.Z. was responsible for the validation and supervision. W.W. was responsible for data curation, software and manuscript reviewing. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eValletta R, Faccioli N, Bonatti M et al (2022) Role of CT colonography in differentiating sigmoid cancer from chronic diverticular disease. Jpn J Radiol 40(1):48-55. doi: 10.1007/s11604-021-01176-8.\u003c/li\u003e\n\u003cli\u003eSun K, Han R, Han Y, Shi X, Hu J, Lu B (2018) Accuracy of Combined Computed Tomography Colonography and Dual Energy Iiodine Map Imaging for Detecting Colorectal masses using High-pitch Dual-source CT. Sci Rep 8(1):3790. doi: 10.1038/s41598-018-22188-x. \u003c/li\u003e\n\u003cli\u003eLatos W, Aebisher D, Latos M et al (2022) Colonoscopy: Preparation and Potential Complications. Diagnostics (Basel) 12(3):747. doi: 10.3390/diagnostics12030747.\u003c/li\u003e\n\u003cli\u003eChervenkov L, Sirakov N, Georgiev A et al (2023) High Concordance of CT Colonography and Colonoscopy Allows for the Distinguishing and Diagnosing of Intestinal Diseases. Life (Basel) 13(9):1906. doi: 10.3390/life13091906.\u003c/li\u003e\n\u003cli\u003eWesp P, Grosu S, Graser A et al (2022) Deep learning in CT colonography: differentiating premalignant from benign colorectal polyps. Eur Radiol 32(7):4749-4759. doi: 10.1007/s00330-021-08532-2.\u003c/li\u003e\n\u003cli\u003eHorvat N, Raj A, Ward JM, Smith JJ, Markowitz AJ, Gollub MJ (2018) Clinical Value of CT Colonography Versus Preoperative Colonoscopy in the Surgical Management of Occlusive Colorectal Cancer. AJR Am J Roentgenol 210(2):333-340. doi: 10.2214/AJR.17.18144.\u003c/li\u003e\n\u003cli\u003ede Kanter C, Dhaliwal S, Hawks M (2022) Colorectal Cancer Screening: Updated Guidelines From the American College of Gastroenterology. Am Fam Physician 105(3):327-329.\u003c/li\u003e\n\u003cli\u003eSpada C, Hassan C, Bellini D et al (2021) Imaging alternatives to colonoscopy: CT colonography and colon capsule. European Society of Gastrointestinal Endoscopy (ESGE) and European Society of Gastrointestinal and Abdominal Radiology (ESGAR) Guideline-Update 2020. Eur Radiol 31(5):2967-2982. doi:10.1007/s00330-020-07413-4\u003c/li\u003e\n\u003cli\u003ePapadakis AE, Damilakis J (2019) Automatic Tube Current Modulation and Tube Voltage Selection in Pediatric Computed Tomography: A Phantom Study on Radiation Dose and Image Quality. Invest Radiol 54(5):265-272. doi:10.1097/RLI.0000000000000537\u003c/li\u003e\n\u003cli\u003eEuler A, Taslimi T, Eberhard M et al (2021) Computed Tomography Angiography of the Aorta-Optimization of Automatic Tube Voltage Selection Settings to Reduce Radiation Dose or Contrast Medium in a Prospective Randomized Trial. Invest Radiol 56(5):283-291. doi: 10.1097/RLI.0000000000000740. \u003c/li\u003e\n\u003cli\u003eChoi MH, Lee YJ, Jung SE (2020) A LESSON FROM AUTOMATIC TUBE VOLTAGE SELECTION: FEASIBILITY OF 100 kVp IN PORTAL VENOUS PHASE ABDOMINAL CT. Radiat Prot Dosimetry 188(4):424-431. doi: 10.1093/rpd/ncz302. \u003c/li\u003e\n\u003cli\u003eZhao Y, Li D, Liu Z, Geng X, Zhang T, Xu Y (2021) Comparison of image quality and radiation dose using different pre-ASiR-V and post-ASiR-V levels in coronary computed tomography angiography. J Xray Sci Technol 29(1):125-134. doi: 10.3233/XST-200754.\u003c/li\u003e\n\u003cli\u003eLiu JJ, Xue HD, Liu W et al (2021) CT colonography with spectral filtration and advanced modeled iterative reconstruction in the third-generation dual-source CT: image quality, radiation dose and performance in clinical utility. Acad Radiol 28(5):e127-e136. doi:10.1016/j.acra.2020.03.040\u003c/li\u003e\n\u003cli\u003eAfadzi M, Lysvik EK, Andersen HK, Martinsen ACT (2019) Ultra-low dose chest computed tomography: Effect of iterative reconstruction levels on image quality. Eur J Radiol 114:62-68. doi: 10.1016/j.ejrad.2019.02.021.\u003c/li\u003e\n\u003cli\u003eIsrael GM, Cicchiello L, Brink J, Huda W (2010) Patient size and radiation exposure in thoracic, pelvic, and abdominal CT examinations performed with automatic exposure control. AJR Am J Roentgenol 195(6):1342-6. doi: 10.2214/AJR.09.3331.\u003c/li\u003e\n\u003cli\u003eManigrasso M, Milone M, Musella M et al (2022) Preoperative Localization in Colonic Surgery (PLoCoS Study): a multicentric experience on behalf of the Italian Society of Colorectal Surgery (SICCR). Updates Surg 74(1):137-144. doi:10.1007/s13304-021-01180-7\u003c/li\u003e\n\u003cli\u003eLiu JJ, Xue HD, Liu W et al (2021) CT colonography with spectral filtration and advanced modeled iterative reconstruction in the third-generation dual-source CT: image quality, radiation dose and performance in clinical utility. Acad Radiol 28(5):e127-e136. doi: 10.1016/j.acra.2020.03.040.\u003c/li\u003e\n\u003cli\u003eYasuda T, Honda T, Utano K et al (2022) Diagnostic accuracy of ultra-low-dose CT colonography for the detection of colorectal polyps: a feasibility study. Jpn J Radiol 40(8):831-839. doi: 10.1007/s11604-022-01266-1.\u003c/li\u003e\n\u003cli\u003eCianci R, Delli Pizzi A, Esposito G et al (2019) Ultra-low dose CT colonography with automatic tube current modulation and sinogram-affirmed iterative reconstruction: Effects on radiation exposure and image quality. J Appl Clin Med Phys 20(1):321-330. doi:10.1002/acm2.12510\u003c/li\u003e\n\u003cli\u003eLi B, Wang X, Fan Y et al (2023) Evaluation of BMI-based tube voltage selection in CT colonography: A prospective comparison of low kV versus routine 120 kV protocol. J Appl Clin Med Phys 24(5):e13955. doi:10.1002/acm2.13955\u003c/li\u003e\n\u003cli\u003eWei W, Wang SG, Zhang JY et al (2023) Implementation of Individualized Low-Dose Computed Tomography-Guided Hook Wire Localization of Pulmonary Nodules: Feasibility and Safety in the Clinical Setting. Diagnostics (Basel) 13(20):3235. doi: 10.3390/diagnostics13203235.\u003c/li\u003e\n\u003cli\u003eHe W, Chen X, Hu R, Sun W, Tan W (2022) Influence of Contrast Agent Injection Scheme Customized by Dual-Source CT Based on Automatic Tube Voltage Technology on Image Quality and Radiation Dose of Coronary Artery Imaging. Front Surg 9:862697. doi: 10.3389/fsurg.2022.862697.\u003c/li\u003e\n\u003cli\u003eZhao Y, Li D, Liu Z, Geng X, Zhang T, Xu Y (2021) Comparison of image quality and radiation dose using different pre-ASiR-V and post-ASiR-V levels in coronary computed tomography angiography. J Xray Sci Technol 29(1):125-134. doi: 10.3233/XST-200754.\u003c/li\u003e\n\u003cli\u003eZhu Z, Zhao Y, Zhao X et al (2021) Impact of preset and postset adaptive statistical iterative reconstruction-V on image quality in nonenhanced abdominal-pelvic CT on wide-detector revolution CT. Quant Imaging Med Surg 11(1):264-275. doi:10.21037/qims-19-945\u003c/li\u003e\n\u003cli\u003eZhao S, Liu ZC, Zhao YX, Zhang TL, Zuo ZW (2023) A feasibility study of different GSI noise indexes and concentrations of contrast medium in hepatic CT angiography of overweight patients: image quality, radiation dose, and iodine intake. Jpn J Radiol 41(6):669-679. doi: 10.1007/s11604-022-01384-w.\u003c/li\u003e\n\u003cli\u003eCao L, Liu X, Li J et al (2021) A study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions. Br J Radiol 94(1118):20201086. doi: 10.1259/bjr.20201086.\u003c/li\u003e\n\u003cli\u003eTaguchi N, Oda S, Imuta M et al (2018) Model-based Iterative Reconstruction in Low-radiation-dose Computed Tomography Colonography: Preoperative Assessment in Patients with Colorectal Cancer. Acad Radiol 25(4):415-422. doi: 10.1016/j.acra.2017.10.008. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Colonography, Computed Tomographic, Colorectal Neoplasms, Radiation Dosage, Image Reconstruction","lastPublishedDoi":"10.21203/rs.3.rs-4578840/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4578840/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo assess the feasibility of combining Auto-kVp selection technique, higher preset ASIR-V and noise index (NI) to realize individualized sub-mSv CT colonography (CTC) for accurate colorectal tumor detection and localization.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eNinety patients with suspected colorectal cancer (CRC) were prospectively enrolled to undergo standard dose CTC (SDCTC) in the prone and ultra-low dose CTC (ULDCTC) in the supine position. SDCTC used 120 kVp, preset ASIR-V of 30%, SmartmA for a NI of 13; ULDCTC used Auto-kVp selection technique with 80 or 100 kVp, preset ASIR-V of 60%, SmartmA for a NI of 13 for 80 kVp, and NI of 15 for 100 kVp. The effective dose (ED), image quality [signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of colorectal neoplasms] between the two protocols were compared and the accuracies of tumor locations were evaluated for CTC in comparison with the surgery results.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean ED of the 80 kVp subgroup was 0.70mSv, 71.43% lower than the 2.45mSv for the 120kVp group, while that of the 100 kVp subgroup was 0.98mSv, 73.00% lower than the 3.63mSv for the 120 kVp group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The tumor SNR and CNR of the ULDCTC were higher than those of SDCTC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while there was no difference in the subjective image quality between them with good inter-observer agreement (\u003cem\u003eKappa\u003c/em\u003e: 0.805\u0026ndash;0.923). Both SDCTC and ULDCTC groups had high detection rate of colorectal tumors, along with good consistency in determining tumor location compared with surgery reports (\u003cem\u003eKappa\u003c/em\u003e: 0.718\u0026ndash;0.989).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe combination of Auto-kVp selection, higher preset ASIR-V and NI achieves individualized sub-mSv CTC with good performance in detecting and locating CRC with surgery and consistent results between SDCTC and ULDCTC.\u003c/p\u003e","manuscriptTitle":"Achieving sub-milliSievert CT colonography for accurate colorectal tumor detection using smart examination protocols: a prospective self-controlled study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-12 18:43:30","doi":"10.21203/rs.3.rs-4578840/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-15T23:16:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-12T00:25:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32857402509217506896705856525618698135","date":"2024-06-30T10:21:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-27T08:12:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151585010883382300906621983205001272847","date":"2024-06-18T00:27:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-17T01:57:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-14T08:54:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-14T08:53:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Abdominal Radiology","date":"2024-06-14T02:01:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"abdominal-radiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aima","sideBox":"Learn more about [Abdominal Radiology](http://link.springer.com/journal/261)","snPcode":"261","submissionUrl":"https://submission.springernature.com/new-submission/261/3","title":"Abdominal Radiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fb86ce95-2dbf-402c-b4ea-5454d5beb43c","owner":[],"postedDate":"July 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-16T16:12:33+00:00","versionOfRecord":{"articleIdentity":"rs-4578840","link":"https://doi.org/10.1007/s00261-024-04557-5","journal":{"identity":"abdominal-radiology","isVorOnly":false,"title":"Abdominal Radiology"},"publishedOn":"2024-09-14 15:57:54","publishedOnDateReadable":"September 14th, 2024"},"versionCreatedAt":"2024-07-12 18:43:30","video":"","vorDoi":"10.1007/s00261-024-04557-5","vorDoiUrl":"https://doi.org/10.1007/s00261-024-04557-5","workflowStages":[]},"version":"v1","identity":"rs-4578840","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4578840","identity":"rs-4578840","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}