Health technology assessment of 3 Tesla vs 1.5 Tesla MRI to evaluate the impact of advanced imaging technology

Health technology assessment of 3 Tesla vs 1.5 Tesla MRI to evaluate the impact of advanced imaging technology | 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 Health technology assessment of 3 Tesla vs 1.5 Tesla MRI to evaluate the impact of advanced imaging technology Ayshath Shakeela, Somu G, Priya P S, Akshay Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6925003/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction : Magnetic Resonance Imaging (MRI) is a cornerstone of modern diagnostic radiology, offering detailed visualization of anatomical structures without ionizing radiation. Rooted in nuclear magnetic resonance (NMR) principles pioneered by Bloch and Purcell, and clinically advanced by Dr. Raymond Damadian, MRI has evolved significantly since its inception. While both 1.5 Tesla (T) and 3T systems are widely used, the latter promises superior imaging performance, albeit with higher operational demands and costs. This Health Technology Assessment (HTA) compares the diagnostic, technical, economic, and perceptual parameters of 1.5T and 3T MRI systems, particularly in the context of stroke imaging. Methodology : A mixed-method approach was employed. Quantitatively, 400 brain stroke MRI cases (200 for each modality) were analyzed using the T2 FLAIR sequence, focusing on Signal-to-Noise Ratio (SNR) and scan time. Non-parametric statistical methods (Wilcoxon and Shapiro-Wilk tests) were applied due to data non-normality. Additionally, a validated Likert-scale questionnaire was administered to 49 MRI technicians to assess perceptions on 10 parameters, using snowball sampling. A focus group discussion (FGD) with five radiologists explored qualitative insights regarding diagnostic value, workflow, and safety across modalities. Results : Statistical analysis revealed significantly higher SNR (mean 20.0 vs. 17.8; p < 0.001) and shorter scan times (mean 10.2 vs. 17.6 minutes; p < 0.001) with 3T MRI. Monthly scan volumes increased by 22.5% post-upgrade from 1.5T to 3T. Power consumption and operational costs were also higher with 3T, requiring enhanced electrical infrastructure and cooling systems. Technician feedback revealed high agreement on superior image quality and faster scan times with 3T MRI. However, lower scores were noted for patient comfort, contrast usage, and downtime. Median scores for key parameters such as diagnostic confidence and SNR were ≥ 4, indicating strong preference for 3T MRI. Thematic analysis of the FGD underscored diagnostic advantages of 3T in neurology and musculoskeletal imaging, tempered by concerns about RF heating, noise, and higher costs. Discussion : 3T MRI systems offer notable clinical advantages—higher image resolution, greater diagnostic accuracy, and improved efficiency. These are especially valuable in acute neuroimaging, where clarity is crucial. Despite higher installation and operational demands, the overall return on investment is supported by increased throughput and diagnostic confidence. Technician and radiologist feedback corroborate objective findings, though safety in patients with implants, and operational challenges like noise and RF heating, remain concerns. Conclusion The HTA supports the adoption of 3T MRI in tertiary care settings for its superior diagnostic and operational performance over 1.5T systems. While higher energy consumption, cost, and patient discomfort are valid considerations, the benefits in image quality and scan efficiency justify its strategic integration into advanced neuroimaging protocols. Balanced deployment, with continued use of 1.5T for certain patient subsets, is recommended to optimize clinical outcomes and resource utilization. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Introduction A key element of modern medical diagnostics, magnetic resonance imaging (MRI) has revolutionized how doctors view and comprehend intricate anatomical structures and disease conditions in the human body. Magnetic resonance imaging (MRI), which has its roots in nuclear magnetic resonance, is a sophisticated imaging method that relies on the way atomic nuclei react to high magnetic fields and radiofrequency pulses.( 1 ) Theoretical contributions to nuclear magnetic resonance spectroscopy were made by Felix Bloch and Edward Purcell, who were awarded the 1952 Nobel Prize in Physics for their work in the subject. Later developments in magnetic resonance imaging were based on their pioneering work. In the beginning technique was called "NMRI" (Nuclear Magnetic Resonance Imaging). Nevertheless, the technique's name was changed due to public outcry over the word "nuclear," and it was later used in the medical field as Magnetic Resonance Imaging.( 2 ) Dr. Raymond Damadian is generally considered the father of magnetic resonance imaging (MRI) and is largely responsible for its development as a clinical diagnostic tool. He laid the groundwork for technology in 1971 with his ground-breaking study showing that nuclear magnetic resonance (NMR) could distinguish between healthy and malignant tissues.( 3 ) Nikola Tesla (1856–1943) was a Serbian American inventor, engineer, and physicist, renowned for his revolutionary work in alternating current (AC) electricity, electromagnetism, and wireless energy transmission. Although Tesla did not invent MRI, the unit of magnetic field strength used in MRI—the Tesla (T)—is named after him to honor his immense contributions to electromagnetism.( 4 ) The primary distinction between 1.5T and 3T is field strength. When compared with Earth’s magnetic field, a 1.5T MR system is 30,000 times stronger, while a 3T MR system is twice as strong than 1.5 T. For MR imaging, three Tesla systems enable both better SNR and enhanced image resolution capabilities. Unfortunately, gradient noise is typically louder at 3T than at 1.5T, and the specific absorption rate rises as field strengths increase.( 5 ) MRI is completely unavailable to 70% of the world's population, in contrast to CT, X-ray, and ultrasound. The cause is unreasonably high capital costs, a challenge in emerging nations such as India. According to current estimates, there are 4800 MRIs installed nationwide in India, which is three times fewer than CT. This could be because MRIs are expensive and dependent on imports.( 8 ) Dr. Jitendra Singh stated that the current yearly demand for machines is less than 350, but that it is anticipated to more than double by 2030 due to rising awareness and various government initiatives to increase healthcare access and inclusivity, such as the flagship Ayushman Bharat initiative.( 9 ) Methodology To evaluate the Health Technology Assessment (HTA) comparing 3T and 1.5T MRI A 45-minute online focus group discussion was conducted with radiologists having ≥ 2 years’ experience with both modalities to assess diagnostic accuracy and interobserver variability, using convenience sampling. For technical comparison, the signal-to-noise ratio (SNR) of T2 FLAIR sequences in brain stroke patients was compared between 1.5T and 3T MRI using non-probability purposive sampling. Additionally, a Likert scale-based questionnaire was administered to MRI technicians to evaluate their perceptions and preferences of 3T vs. 1.5T MRI, applying snowball sampling for participant selection. The questionnaire is validated. This approach will provide both quantitative performance data and qualitative user insights for a comprehensive HTA. Sample Size : The patients who will undergo brain MRI for stroke detection in the hospital will be included in the study. At least 400 MRI cases (200 in each MRI modality) will be included so that there will be enough data for comparison analysis. The sample size was calculated using the formula: Inclusion Criteria : Patients 18 years and older who are going for MRI brain for stroke detection. Radiologists with a minimum of two years of experience reading MRI scans. Questionnaire for the radiology technician worked under 3T and 1.5 Tn MRI with more than 1 year experience. Exclusion Criteria : All Pregnant and less than 18 years old patient Results and Discussion The MRI machine installation process : The installation of an MRI machine involves a multi-step process to ensure safe, accurate setup and functionality. In Step 1 (Arrival and Unloading), the MRI equipment arrives at the hospital site in containers, followed by a careful unloading process. Step 2 (Mechanical Installation) begins with Schiller unit positioning and RF cabin assembly, including fixing the RF cabin door and completing the enclosure. The MRI machine is mechanically installed, involving shifting, positioning, and fixing the patient table. The cold head and UPS system are turned on during this phase. In Step 3 (Interior Work), the site is temporarily handed over to the hospital team to complete the interior work in the MRI room. Step 4 (Electrical Installation and Inspection) includes the installation of the MRI console, detailed electrical wiring checks for the cabinet, console, and MRI system. The machine is powered on, and environmental parameters are checked—humidity must be within 45–60% RH (often observed above 65%, requiring adjustment), and the air conditioning temperature is set and maintained at 20–22°C. Finally, Step 5 (Ramping and Shimming Process) is carried out. The MRI system is turned on and undergoes a ramping process, which takes approximately 3 hours. This is followed by the shimming process, where the magnetic field strength is measured using float measurement techniques. Based on these measurements, weights are added to balance the field. The machine is ramped down for around 30 minutes, and then ramped up again for another 3-hour cycle, with continued shimming to fine-tune the magnet’s homogeneity. 3 Tesla MRI consumes over 2X the power of 1.5T during operation. Higher power demand requires stronger electrical infrastructure, including thicker cables and better earthing. Hospitals planning an upgrade need to ensure power supply can support 3T.( 10 ) To support above statement, data here shows that 3 Tesla (3T) MRI machines consistently consume more electricity than 1.5 Tesla (1.5T) systems. The percentage difference in unit consumption ranges from 9.49–37.65%, with the highest in June 2024. This higher energy use leads to increased electricity bills, with 3T MRI consistently costing more each month. Notably, June 2024 also showed the highest billing difference at 37.79%. As illustrated in the bar chart (“% Difference (Total Bill) vs. Month”), the cost gap peaks in June and remains significant across other months. These findings highlight that 3T MRI machines have greater operational energy costs, which must be factored into life cycle costing and financial planning in imaging departments. Similarly battery and UPS requirements, but 3T generates more heat, which requires improved cooling/ventilation. Battery room placement guidelines emphasize separating battery racks from other equipment to reduce overheating risks. Temperature requirements are the same for both MRI types, but 3T has stricter humidity control needs.3T MRI rooms need better air circulation & cooling to handle extra heat from higher power usage. According to Siemens guidelines, minimum distances must be maintained between MRI magnets to avoid magnetic interference. For example, a 0.2T magnet should be placed 10 meters away from 0.35T, 3.0T, or another 0.2T magnet, but only 5–6 meters from 1.0T and 1.5T systems. Similarly, a 3.0T magnet requires 6–10 meters of separation depending on the other magnet’s strength. The 7.0T system requires a 10-meter distance from all other magnets. These guidelines are essential for safe installation and operation in multi-MRI environments. Signal-to-Noise Ratio (SNR) An essential metric for assessing the quality of MRI pictures is the SNR. SNR contrasts a picture's valuable signal with background noise, which gives the image a grainy appearance. Molecular mobility in the body and electrical resistance in machine parts like coils and cables are the main sources of noise in MRIs. Noise is influenced by the pulse sequence's bandwidth as well as the kind and size of coil. One ROI is positioned on a homogeneous tissue region to capture the signal, and another ROI is positioned in the background to record the noise, in order to assess signal-to-noise ratio (SNR). Next, the SNR is computed using the following formula: SNR = signal / noise. This parameter aids in the comparison of the image quality of 1.5T and 3T MRI systems as part of the evaluation of health technology.( 11 ) Diagnostic performance and operational efficiency of 3 Tesla MRI compared to 1.5 Tesla MRI were evaluated using the Signal-to-Noise Ratio (SNR) and scan time as key metrics. Data were collected from 400 patients undergoing brain stroke MRI using the T2 FLAIR sequence, with 200 patients scanned on each system. Analysis was conducted using JAMOVI software (version 2.6). Descriptive Analysis showed SNR: Mean SNR for 3T MRI: 20.0 Mean SNR for 1.5T MRI: 17.8 The SNR was significantly higher in 3T MRI, indicating better image quality. Scan Time: Mean scan time for 3T MRI: 10.2 minutes Mean scan time for 1.5T MRI: 17.6 minutes 3T MRI demonstrated a significantly shorter scan time, enhancing workflow efficiency. For Statistical Results: Wilcoxon Paired Samples Test showed statistically significant differences for both variables: SNR difference (3T vs 1.5T): p < 0.001 Scan time difference (3T vs 1.5T): p < 0.001 Normality testing (Shapiro-Wilk) indicated that the data violated normal distribution assumptions, justifying the use of non-parametric tests. So this interpret that The higher SNR in 3T MRI supports its superior diagnostic capability, particularly beneficial for conditions like acute stroke, where detail and clarity are critical. The reduced scan time in 3T MRI offers operational advantages, including increased throughput, improved patient comfort, and cost-efficiency in high-volume settings. These findings strongly support the technological advancement and clinical value of 3T MRI over 1.5T in neuroimaging applications. This HTA analysis confirms that 3 Tesla MRI offers better image quality (higher SNR) and faster scan times compared to 1.5 Tesla MRI for brain stroke protocols. These results provide strong evidence in Favor of adopting 3T MRI for advanced neuroimaging, aligning with the goals of cost-effective, high-quality diagnostic care. To support the above statement the graph comparing monthly scan volumes shows a significant increase after upgrading to 3T MRI. In 2024, the average monthly scans rose to 871 compared to 711 in 2019 with 1.5T — a 22.5% increase. Except for March and November, 3T consistently outperformed 1.5T, with peak volumes in May (924) and September (926). Even in its lowest month (June), 3T surpassed 1.5T scan counts. This rise reflects improved throughput, faster workflows, and supports the adoption of 3T MRI as a cost-effective, high-performance imaging solution. Demographic Analysis of patient undergone scan : A total of 400 patients undergoing MRI brain stroke protocol were included in this study — with 200 patients each scanned using 1.5 Tesla and 3 Tesla MRI systems. Patient demographics were analysed to explore age distribution, gender-wise stroke prevalence, and patient admission status (OP/IP). Age Distribution : The Mean age: 60.7 years with Standard Deviation: 13.2 years, Age group most affected: 61–80 years (52.5% of total cases). Range: 22–91 years This indicates that older adults constitute the majority of stroke cases, aligning with existing epidemiological data. A Kolmogorov-Smirnov test for age distribution showed non-normality (p < 0.05), justifying the use of non-parametric tests for further statistical analysis. Table 1 Gender Distribution Gender Frequency (n) Percentage (%) Male 260 65.0% Female 140 35.0% Total 400 100% Note: Created by the author Stroke prevalence in this study was significantly higher in males. (As shown in Table 1 ) A Chi-square test of independence between gender and stroke occurrence yielded: indicating a statistically significant association between male gender and higher stroke incidence in the sampled population. Table 2 Outpatient vs. Inpatient Status Type Frequency (n) Percentage (%) Outpatient 236 59.0% Inpatient 164 41.0% Total 400 100% Note: Created by the author Stroke evaluations were more commonly performed in outpatients, suggesting increased use of MRI in minor or follow-up stroke cases. (As shown in Table 2 ) A Chi-square test between scan type (OP/IP) and MRI Tesla strength (3T vs 1.5T) showed no significant difference: confirming that MRI scan distribution was balanced between inpatients and outpatients across both modalities. Health Technology Assessment (HTA) – Technician Perception Analysis : As part of the Health Technology Assessment (HTA) comparing 1.5 Tesla and 3 Tesla MRI systems, a structured questionnaire was administered to 49 radiology technicians using a 5-point Likert scale. The objective was to gather frontline insights on parameters such as image quality, scan time, artefacts, contrast usage, diagnostic confidence, downtime, patient comfort, adaptability, cost justification, and overall preference. The data was analysed using Jamovi software (v2.6) to extract both descriptive and inferential insights. Statistical Analysis showed that, Descriptive Statistics were calculated for each item, including: Mean, Median, Standard Deviation, Min/Max, Skewness, and Kurtosis. Most items had a median score of 4, indicating a strong leaning toward agreement with the superiority of 3 Tesla MRI. Shapiro-Wilk Test for Normality showed statistically significant non-normality (p < 0.001) for all items. This confirmed that the data were non-normally distributed, and that non-parametric interpretation methods were appropriate. Frequency Distribution tables were generated for each item to understand response trends: A significant majority of technicians rated SNR and scan time ≥ 4, affirming that they perceive 3T MRI to offer higher image quality and faster scans. study shows that radiology technicians strongly prefer 3 Tesla MRI over 1.5 Tesla, particularly for its better image quality (SNR), faster scan time, and higher diagnostic confidence. Though some concerns were noted about patient comfort, contrast use, and downtime, overall technician feedback aligns with the objective benefits observed. Their hands-on experience offers valuable input to HTA, supporting the adoption of 3T MRI as a clinically and operationally superior choice for tertiary care centres. Focus Group Discussion (FGD) : The FGD served as a qualitative HTA tool, utilizing thematic analysis to extract structured insights from expert radiologists on clinical, operational, and economic aspects of 3T vs. 1.5T MRI systems. The session involved five radiologists, each with more than two years of experience using both systems and was conducted over 45 minutes in an online format, thematic representation was used. The FGD confirms that 3T MRI delivers clear diagnostic advantages, especially in high-resolution imaging contexts like neuro and MSK studies. However, its higher cost, sensitivity to artifacts, and safety concerns in specific patient groups highlight the importance of balanced use. 1.5T MRI remains valuable, particularly for patients with implants or in postoperative evaluations. Thematic analysis complemented the objective data on SNR and scan time, providing a holistic HTA perspective. Conclusion HTA compared 3 Tesla MRI and 1.5 Tesla MRI using both quantitative and qualitative methods, SNR analysis, scan time evaluation, patient demographic profiling, technician perception through a structured questionnaire, and radiologist insights via focus group discussion (FGD). SNR Comparison showed Statistically significant findings show that 3T MRI delivers higher SNR compared to 1.5T, offering better image clarity, particularly in stroke evaluation using T2 FLAIR sequences. Scan Time showed 3T MRI was shown to significantly reduce scan duration, with an average of 10.2 minutes versus 17.6 minutes in 1.5T MRI, enhancing patient throughput and workflow efficiency. demographics in Stroke Patients showed Mean age: 60.7 years, with stroke more prevalent in the 61–80 years group. Gender: 65% male, 35% female – showing higher stroke burden among men. OP vs IP: 59% were outpatients, indicating greater use of MRI for early or follow-up stroke detection. Technician-Based HTA : Technicians showed a strong preference for 3T MRI, citing better SNR, faster scans, and higher diagnostic confidence. Some concerns were noted regarding patient comfort, downtime, and contrast use, but the overall perception was favourable toward 3T. Radiologist FGD Findings : Radiologists unanimously agreed on the superior diagnostic value of 3T MRI in neuro and MSK cases. However, 1.5T was still preferred for implanted patients and abdominal/post-op imaging. Cost-effectiveness and safety concerns were discussed, along with the need for training and workflow adaptation. The study findings indicate that the annual scan volume is sufficient to justify the installation and operation of a 3 Tesla MRI at a tertiary care teaching hospital. The 3T MRI demonstrated superior diagnostic performance, enabled faster scan times, and achieved financial break-even within 1.5 years, confirming both clinical utility and economic viability. Technician-Based HTA : Technicians showed a strong preference for 3T MRI, citing better SNR, faster scans, and higher diagnostic confidence. Some concerns were noted regarding patient comfort, downtime, and contrast use, but the overall perception was favourable toward 3T. Radiologist FGD Findings : Radiologists unanimously agreed on the superior diagnostic value of 3T MRI in neuro and MSK cases. However, 1.5T was still preferred for implanted patients and abdominal/post-op imaging. Cost-effectiveness and safety concerns were discussed, along with the need for training and workflow adaptation. Declarations Funding No specific funding was received for conducting this study. Conflict of interest The authors have no conflict of interest to declare that are relevant to the content of this article. Ethical declarations Ethics approval and consent to participate Ethical standards were rigorously followed in conducting this study. The study protocol was officially approved by the Institutional Ethics Committee of the Kasturba Medical College (approval number IEC2: 443/2023). All data were fully anonymized before publication. All relevant national and international guidelines and regulations were followed and study was adhered to the Declaration of Helsinki. Informed Consent: Informed consent was obtained from the participants including parents/ legally authorized representatives of subjects who are under 16. Clinical Trial Number: Not Applicable Author Contribution Ayshath Shakeela (AS): Contributed in preparing the manuscript, data collection, interpretation and analysis.Somu G (SG): Contributed in terms of conceptualization of the research work and supervision of the project along with inputs for manuscript preparation.Priya PS (PP): Contributed in data collection, and guiding in throughout the project and by providing administrative support.Akshay Kumar (AK): Contributed in preparing the final manuscript and reviewing it and data collection and data interpretation. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Geva T. Magnetic resonance imaging: historical perspective. J Cardiovasc Magn Reson Off J Soc Cardiovasc Magn Reson. 2006;8(4):573–80. Smith HJ. The history of magnetic resonance imaging and its reflections in Acta Radiologica. Acta Radiol Stockh Swed 1987. 2021 Nov;62(11):1481–98. Raymond Damadian [Internet]. Alumni Park. [cited 2025 May 17]. Available from: https://www.alumnipark.com/exhibits/featured/raymond-damadian/ Roguin A. Nikola Tesla: The man behind the magnetic field unit. J Magn Reson Imaging. 2004;19(3):369–74. Technology Trends: MRI: Considerations for the Move from 1.5T to 3T [Internet]. [cited 2025 May 17]. Available from: https://www.radiologytoday.net/archive/rt0216p22.shtml Questions and Answers in MRI [Internet]. [cited 2025 May 21]. MRI Questions & Answers; MR imaging physics & technology. Available from: http://mriquestions.com/ History of MRI • Magnetic Resonance in Medicine – The Basics – by Peter A. Rinck | NMR MR MRI | Essentials, introduction, basic principles, facts, history | The primer of EMRF/TRTF. [Internet]. [cited 2025 May 21]. Available from: https://www.magnetic-resonance.org/ch/20-01.html List Of MRI centers in India [Internet]. [cited 2025 May 17]. Available from: https://rentechdigital.com/smartscraper/business-report-details/list-of-mri-centers-in-india Union Minister Dr Jitendra Singh launches India’s first Indigenously developed, Affordable, lightweight, Ultrafast, High Field (1.5 Tesla), Next Generation Magnetic Resonance Imaging (MRI) Scanner in New Delhi [Internet]. [cited 2025 May 17]. Available from: https://www.pib.gov.in/www.pib.gov.in/Pressreleaseshare.aspx?PRID=1944717 Lopez L. MRI Installation Guide [Internet]. medicalimagingsource.com. 2022 [cited 2025 May 17]. Available from: https://www.medicalimagingsource.com/mri-installation Signal-to-Noise Ratio (SNR) in MRI | Factors affecting SNR [Internet]. mrimaster. [cited 2025 May 17]. Available from: https://mrimaster.com/snr/ Additional Declarations No competing interests reported. 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07:58:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":76529,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEnvironmental and Cooling Requirements\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6925003/v1/be8b82c082e2a2dcc985b116.png"},{"id":87705722,"identity":"b9002ae5-b27a-40db-b974-d179aff3511c","added_by":"auto","created_at":"2025-07-28 07:58:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":37737,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSafety Distances Between MRI Magnets\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6925003/v1/ee7ba377dee824715bc7c8c6.png"},{"id":87706611,"identity":"beb1db5d-d6c2-4e01-afdf-2e12097e3bff","added_by":"auto","created_at":"2025-07-28 08:06:34","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":68127,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMRI Scan Trend Analysis: 3T (2024) Compared to 1.5T (2019)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6925003/v1/5e6435ee64c8a0fa3975cb13.png"},{"id":87705725,"identity":"bdf9c401-82c4-46f0-b8d1-442ade713dbc","added_by":"auto","created_at":"2025-07-28 07:58:33","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":19745,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDescriptive Statistics of Age, Gender, Appointment Type\u003c/em\u003e\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6925003/v1/bf3fdb25c63a254b42ef311b.png"},{"id":87705723,"identity":"7dd2b1c9-14b6-4022-8411-17680dc69703","added_by":"auto","created_at":"2025-07-28 07:58:33","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":34049,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePerception of MRI Technicians on 3T MRI Performance Parameters\u003c/em\u003e\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6925003/v1/7297cf7397d671a3bfd5c2a5.png"},{"id":87705730,"identity":"cbbd4538-7a51-438f-b530-5be2a7546c1f","added_by":"auto","created_at":"2025-07-28 07:58:34","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":8618,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHistogram of Technicians' Agreement Level: 3T vs 1.5T MRI\u003c/em\u003e\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6925003/v1/2e9a48a2b7174fa9182c7c9e.png"},{"id":87705737,"identity":"dd9576c5-d385-4d43-ba70-5033dac6cc1a","added_by":"auto","created_at":"2025-07-28 07:58:34","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":134000,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStatistical Summary of Perceptions: 3T MRI vs 1.5T MRI (N = 49)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-6925003/v1/1973aa76273e35ba1f84c664.png"},{"id":87705738,"identity":"9b9901f3-b9c6-4b1c-b636-24500285b030","added_by":"auto","created_at":"2025-07-28 07:58:34","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":26227,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFocus Group Insights\u003c/em\u003e\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-6925003/v1/d033beab95498ade7d5d83ee.png"},{"id":87706608,"identity":"5e99479c-10c7-4f6c-acbf-cf4c4c544aea","added_by":"auto","created_at":"2025-07-28 08:06:34","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":89501,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThematic Analysis of FGD\u003c/em\u003e\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-6925003/v1/2748ec500863f48ebbd8450c.png"},{"id":89879742,"identity":"fef832c1-b166-428d-adb0-728e4e602522","added_by":"auto","created_at":"2025-08-26 05:33:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1414776,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6925003/v1/33c4bdce-3125-4f74-95a3-e39d9b0e1516.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Health technology assessment of 3 Tesla vs 1.5 Tesla MRI to evaluate the impact of advanced imaging technology","fulltext":[{"header":"Introduction","content":"\u003cp\u003eA key element of modern medical diagnostics, magnetic resonance imaging (MRI) has revolutionized how doctors view and comprehend intricate anatomical structures and disease conditions in the human body. Magnetic resonance imaging (MRI), which has its roots in nuclear magnetic resonance, is a sophisticated imaging method that relies on the way atomic nuclei react to high magnetic fields and radiofrequency pulses.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eTheoretical contributions to nuclear magnetic resonance spectroscopy were made by Felix Bloch and Edward Purcell, who were awarded the 1952 Nobel Prize in Physics for their work in the subject. Later developments in magnetic resonance imaging were based on their pioneering work. In the beginning technique was called \"NMRI\" (Nuclear Magnetic Resonance Imaging). Nevertheless, the technique's name was changed due to public outcry over the word \"nuclear,\" and it was later used in the medical field as Magnetic Resonance Imaging.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eDr. Raymond Damadian is generally considered the father of magnetic resonance imaging (MRI) and is largely responsible for its development as a clinical diagnostic tool. He laid the groundwork for technology in 1971 with his ground-breaking study showing that nuclear magnetic resonance (NMR) could distinguish between healthy and malignant tissues.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eNikola Tesla (1856–1943) was a Serbian American inventor, engineer, and physicist, renowned for his revolutionary work in alternating current (AC) electricity, electromagnetism, and wireless energy transmission. Although Tesla did not invent MRI, the unit of magnetic field strength used in MRI—the Tesla (T)—is named after him to honor his immense contributions to electromagnetism.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe primary distinction between 1.5T and 3T is field strength. When compared with Earth’s magnetic field, a 1.5T MR system is 30,000 times stronger, while a 3T MR system is twice as strong than 1.5 T. For MR imaging, three Tesla systems enable both better SNR and enhanced image resolution capabilities. Unfortunately, gradient noise is typically louder at 3T than at 1.5T, and the specific absorption rate rises as field strengths increase.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eMRI is completely unavailable to 70% of the world's population, in contrast to CT, X-ray, and ultrasound. The cause is unreasonably high capital costs, a challenge in emerging nations such as India. According to current estimates, there are 4800 MRIs installed nationwide in India, which is three times fewer than CT. This could be because MRIs are expensive and dependent on imports.(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eDr. Jitendra Singh stated that the current yearly demand for machines is less than 350, but that it is anticipated to more than double by 2030 due to rising awareness and various government initiatives to increase healthcare access and inclusivity, such as the flagship Ayushman Bharat initiative.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003eTo evaluate the Health Technology Assessment (HTA) comparing 3T and 1.5T MRI\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA 45-minute online focus group discussion was conducted with radiologists having ≥ 2 years’ experience with both modalities to assess diagnostic accuracy and interobserver variability, using convenience sampling. For technical comparison, the signal-to-noise ratio (SNR) of T2 FLAIR sequences in brain stroke patients was compared between 1.5T and 3T MRI using non-probability purposive sampling.\u003c/p\u003e\u003cp\u003eAdditionally, a Likert scale-based questionnaire was administered to MRI technicians to evaluate their perceptions and preferences of 3T vs. 1.5T MRI, applying snowball sampling for participant selection. The questionnaire is validated. This approach will provide both quantitative performance data and qualitative user insights for a comprehensive HTA.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSample Size\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThe patients who will undergo brain MRI for stroke detection in the hospital will be included in the study. At least 400 MRI cases (200 in each MRI modality) will be included so that there will be enough data for comparison analysis. The sample size was calculated using the formula:\u003c/p\u003e\u003cp\u003e\u003cimg 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\" 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\" width=\"210\" height=\"312\"\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInclusion Criteria\u003c/b\u003e:\u003c/p\u003e\u003cp\u003ePatients 18 years and older who are going for MRI brain for stroke detection. Radiologists with a minimum of two years of experience reading MRI scans.\u003c/p\u003e\u003cp\u003eQuestionnaire for the radiology technician worked under 3T and 1.5 Tn MRI with more than 1 year experience.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExclusion Criteria\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eAll Pregnant and less than 18 years old patient\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003eThe MRI machine installation process\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe installation of an MRI machine involves a multi-step process to ensure safe, accurate setup and functionality. In Step 1 (Arrival and Unloading), the MRI equipment arrives at the hospital site in containers, followed by a careful unloading process. Step 2 (Mechanical Installation) begins with Schiller unit positioning and RF cabin assembly, including fixing the RF cabin door and completing the enclosure. The MRI machine is mechanically installed, involving shifting, positioning, and fixing the patient table. The cold head and UPS system are turned on during this phase.\u003c/p\u003e\n\u003cp\u003eIn Step 3 (Interior Work), the site is temporarily handed over to the hospital team to complete the interior work in the MRI room. Step 4 (Electrical Installation and Inspection) includes the installation of the MRI console, detailed electrical wiring checks for the cabinet, console, and MRI system. The machine is powered on, and environmental parameters are checked\u0026mdash;humidity must be within 45\u0026ndash;60% RH (often observed above 65%, requiring adjustment), and the air conditioning temperature is set and maintained at 20\u0026ndash;22\u0026deg;C.\u003c/p\u003e\n\u003cp\u003eFinally, Step 5 (Ramping and Shimming Process) is carried out. The MRI system is turned on and undergoes a ramping process, which takes approximately 3 hours. This is followed by the shimming process, where the magnetic field strength is measured using float measurement techniques. Based on these measurements, weights are added to balance the field. The machine is ramped down for around 30 minutes, and then ramped up again for another 3-hour cycle, with continued shimming to fine-tune the magnet\u0026rsquo;s homogeneity.\u003c/p\u003e\n\u003cp\u003e3 Tesla MRI consumes over 2X the power of 1.5T during operation. Higher power demand requires stronger electrical infrastructure, including thicker cables and better earthing. Hospitals planning an upgrade need to ensure power supply can support 3T.(\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eTo support above statement, data here shows that 3 Tesla (3T) MRI machines consistently consume more electricity than 1.5 Tesla (1.5T) systems. The percentage difference in unit consumption ranges from 9.49\u0026ndash;37.65%, with the highest in June 2024. This higher energy use leads to increased electricity bills, with 3T MRI consistently costing more each month. Notably, June 2024 also showed the highest billing difference at 37.79%.\u003c/p\u003e\n\u003cp\u003eAs illustrated in the bar chart (\u0026ldquo;% Difference (Total Bill) vs. Month\u0026rdquo;), the cost gap peaks in June and remains significant across other months. These findings highlight that 3T MRI machines have greater operational energy costs, which must be factored into life cycle costing and financial planning in imaging departments.\u003c/p\u003e\n\u003cp\u003eSimilarly battery and UPS requirements, but 3T generates more heat, which requires improved cooling/ventilation. Battery room placement guidelines emphasize separating battery racks from other equipment to reduce overheating risks.\u003c/p\u003e\n\u003cp\u003eTemperature requirements are the same for both MRI types, but 3T has stricter humidity control needs.3T MRI rooms need better air circulation \u0026amp; cooling to handle extra heat from higher power usage.\u003c/p\u003e\n\u003cp\u003eAccording to Siemens guidelines, minimum distances must be maintained between MRI magnets to avoid magnetic interference. For example, a 0.2T magnet should be placed 10 meters away from 0.35T, 3.0T, or another 0.2T magnet, but only 5\u0026ndash;6 meters from 1.0T and 1.5T systems. Similarly, a 3.0T magnet requires 6\u0026ndash;10 meters of separation depending on the other magnet\u0026rsquo;s strength. The 7.0T system requires a 10-meter distance from all other magnets. These guidelines are essential for safe installation and operation in multi-MRI environments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignal-to-Noise Ratio (SNR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn essential metric for assessing the quality of MRI pictures is the SNR. SNR contrasts a picture\u0026apos;s valuable signal with background noise, which gives the image a grainy appearance. Molecular mobility in the body and electrical resistance in machine parts like coils and cables are the main sources of noise in MRIs. Noise is influenced by the pulse sequence\u0026apos;s bandwidth as well as the kind and size of coil. One ROI is positioned on a homogeneous tissue region to capture the signal, and another ROI is positioned in the background to record the noise, in order to assess signal-to-noise ratio (SNR). Next, the SNR is computed using the following formula: SNR\u0026thinsp;=\u0026thinsp;signal / noise. This parameter aids in the comparison of the image quality of 1.5T and 3T MRI systems as part of the evaluation of health technology.(\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eDiagnostic performance and operational efficiency of 3 Tesla MRI compared to 1.5 Tesla MRI were evaluated using the Signal-to-Noise Ratio (SNR) and scan time as key metrics. Data were collected from 400 patients undergoing brain stroke MRI using the T2 FLAIR sequence, with 200 patients scanned on each system. Analysis was conducted using JAMOVI software (version 2.6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescriptive Analysis showed\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eSNR:\u003cul type=\"circle\"\u003e\n \u003cli\u003eMean SNR for 3T MRI: 20.0\u003c/li\u003e\n \u003cli\u003eMean SNR for 1.5T MRI: 17.8\u003c/li\u003e\n \u003cli\u003eThe SNR was significantly higher in 3T MRI, indicating better image quality.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003eScan Time:\u003cul type=\"circle\"\u003e\n \u003cli\u003eMean scan time for 3T MRI: 10.2 minutes\u003c/li\u003e\n \u003cli\u003eMean scan time for 1.5T MRI: 17.6 minutes\u003c/li\u003e\n \u003cli\u003e3T MRI demonstrated a significantly shorter scan time, enhancing workflow efficiency.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor Statistical Results: Wilcoxon Paired Samples Test showed statistically significant differences for both variables:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cul type=\"circle\"\u003e\n \u003cli\u003eSNR difference (3T vs 1.5T): p \u0026lt; 0.001\u003c/li\u003e\n \u003cli\u003eScan time difference (3T vs 1.5T): p \u0026lt; 0.001\u003c/li\u003e\n \u003c/ul\u003e\n\u003c/ul\u003e\n\u003cp\u003eNormality testing (Shapiro-Wilk) indicated that the data violated normal distribution assumptions, justifying the use of non-parametric tests.\u003c/p\u003e\n\u003cp\u003eSo this interpret that The higher SNR in 3T MRI supports its superior diagnostic capability, particularly beneficial for conditions like acute stroke, where detail and clarity are critical.\u003c/p\u003e\n\u003cp\u003eThe reduced scan time in 3T MRI offers operational advantages, including increased throughput, improved patient comfort, and cost-efficiency in high-volume settings.\u003c/p\u003e\n\u003cp\u003eThese findings strongly support the technological advancement and clinical value of 3T MRI over 1.5T in neuroimaging applications.\u003c/p\u003e\n\u003cp\u003eThis HTA analysis confirms that 3 Tesla MRI offers better image quality (higher SNR) and faster scan times compared to 1.5 Tesla MRI for brain stroke protocols. These results provide strong evidence in Favor of adopting 3T MRI for advanced neuroimaging, aligning with the goals of cost-effective, high-quality diagnostic care.\u003c/p\u003e\n\u003cp\u003eTo support the above statement the graph comparing monthly scan volumes shows a significant increase after upgrading to 3T MRI. In 2024, the average monthly scans rose to 871 compared to 711 in 2019 with 1.5T \u0026mdash; a 22.5% increase. Except for March and November, 3T consistently outperformed 1.5T, with peak volumes in May (924) and September (926). Even in its lowest month (June), 3T surpassed 1.5T scan counts. This rise reflects improved throughput, faster workflows, and supports the adoption of 3T MRI as a cost-effective, high-performance imaging solution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic Analysis of patient undergone scan\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eA total of \u003cstrong\u003e400 patients\u003c/strong\u003e undergoing MRI brain stroke protocol were included in this study \u0026mdash; with 200 patients each scanned using 1.5 Tesla and 3 Tesla MRI systems. Patient demographics were analysed to explore age distribution, gender-wise stroke prevalence, and patient admission status (OP/IP).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge Distribution\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe Mean age: 60.7 years with Standard Deviation: 13.2 years, Age group most affected: 61\u0026ndash;80 years (52.5% of total cases). Range: 22\u0026ndash;91 years\u003c/p\u003e\n\u003cp\u003eThis indicates that older adults constitute the majority of stroke cases, aligning with existing epidemiological data.\u003c/p\u003e\n\u003cp\u003eA Kolmogorov-Smirnov test for age distribution showed non-normality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), justifying the use of non-parametric tests for further statistical analysis.\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eGender Distribution\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e400\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e100%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eNote: Created by the author\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eStroke prevalence in this study was significantly higher in males. (As shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eA Chi-square test of independence between gender and stroke occurrence yielded:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"311\" height=\"57\"\u003e\u003c/p\u003e\n\u003cp\u003eindicating a statistically significant association between male gender and higher stroke incidence in the sampled population.\u003c/p\u003e\n\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eOutpatient vs. Inpatient Status\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOutpatient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInpatient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e400\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e100%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eNote: Created by the author\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eStroke evaluations were more commonly performed in outpatients, suggesting increased use of MRI in minor or follow-up stroke cases. (As shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"261\" height=\"56\"\u003e\u003c/p\u003e\n\u003cp\u003eA Chi-square test between scan type (OP/IP) and MRI Tesla strength (3T vs 1.5T) showed no significant difference:\u003c/p\u003e\n\u003cp\u003econfirming that MRI scan distribution was balanced between inpatients and outpatients across both modalities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealth Technology Assessment (HTA) \u0026ndash; Technician Perception Analysis\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eAs part of the Health Technology Assessment (HTA) comparing 1.5 Tesla and 3 Tesla MRI systems, a structured questionnaire was administered to 49 radiology technicians using a 5-point Likert scale. The objective was to gather frontline insights on parameters such as image quality, scan time, artefacts, contrast usage, diagnostic confidence, downtime, patient comfort, adaptability, cost justification, and overall preference. The data was analysed using Jamovi software (v2.6) to extract both descriptive and inferential insights.\u003c/p\u003e\n\u003cp\u003eStatistical Analysis showed that, Descriptive Statistics were calculated for each item, including: Mean, Median, Standard Deviation, Min/Max, Skewness, and Kurtosis. Most items had a median score of 4, indicating a strong leaning toward agreement with the superiority of 3 Tesla MRI.\u003c/p\u003e\n\u003cp\u003eShapiro-Wilk Test for Normality showed statistically significant non-normality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for all items. This confirmed that the data were non-normally distributed, and that non-parametric interpretation methods were appropriate.\u003c/p\u003e\n\u003cp\u003eFrequency Distribution tables were generated for each item to understand response trends: A significant majority of technicians rated SNR and scan time\u0026thinsp;\u0026ge;\u0026thinsp;4, affirming that they perceive 3T MRI to offer higher image quality and faster scans.\u003c/p\u003e\n\u003cp\u003estudy shows that radiology technicians strongly prefer 3 Tesla MRI over 1.5 Tesla, particularly for its better image quality (SNR), faster scan time, and higher diagnostic confidence. Though some concerns were noted about patient comfort, contrast use, and downtime, overall technician feedback aligns with the objective benefits observed. Their hands-on experience offers valuable input to HTA, supporting the adoption of 3T MRI as a clinically and operationally superior choice for tertiary care centres.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFocus Group Discussion (FGD)\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe FGD served as a qualitative HTA tool, utilizing thematic analysis to extract structured insights from expert radiologists on clinical, operational, and economic aspects of 3T vs. 1.5T MRI systems. The session involved five radiologists, each with more than two years of experience using both systems and was conducted over 45 minutes in an online format, thematic representation was used.\u003c/p\u003e\n\u003cp\u003eThe FGD confirms that 3T MRI delivers clear diagnostic advantages, especially in high-resolution imaging contexts like neuro and MSK studies. However, its higher cost, sensitivity to artifacts, and safety concerns in specific patient groups highlight the importance of balanced use. 1.5T MRI remains valuable, particularly for patients with implants or in postoperative evaluations. Thematic analysis complemented the objective data on SNR and scan time, providing a holistic HTA perspective.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHTA compared 3 Tesla MRI and 1.5 Tesla MRI using both quantitative and qualitative methods, SNR analysis, scan time evaluation, patient demographic profiling, technician perception through a structured questionnaire, and radiologist insights via focus group discussion (FGD).\u003c/p\u003e\u003cp\u003eSNR Comparison showed Statistically significant findings show that 3T MRI delivers higher SNR compared to 1.5T, offering better image clarity, particularly in stroke evaluation using T2 FLAIR sequences.\u003c/p\u003e\u003cp\u003eScan Time showed 3T MRI was shown to significantly reduce scan duration, with an average of 10.2 minutes versus 17.6 minutes in 1.5T MRI, enhancing patient throughput and workflow efficiency.\u003c/p\u003e\u003cp\u003edemographics in Stroke Patients showed Mean age: 60.7 years, with stroke more prevalent in the 61\u0026ndash;80 years group.\u003c/p\u003e\u003cp\u003eGender: 65% male, 35% female \u0026ndash; showing higher stroke burden among men.\u003c/p\u003e\u003cp\u003eOP vs IP: 59% were outpatients, indicating greater use of MRI for early or follow-up stroke detection.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTechnician-Based HTA\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eTechnicians showed a strong preference for 3T MRI, citing better SNR, faster scans, and higher diagnostic confidence. Some concerns were noted regarding patient comfort, downtime, and contrast use, but the overall perception was favourable toward 3T.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRadiologist FGD Findings\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eRadiologists unanimously agreed on the superior diagnostic value of 3T MRI in neuro and MSK cases. However, 1.5T was still preferred for implanted patients and abdominal/post-op imaging. Cost-effectiveness and safety concerns were discussed, along with the need for training and workflow adaptation.\u003c/p\u003e\u003cp\u003eThe study findings indicate that the annual scan volume is sufficient to justify the installation and operation of a 3 Tesla MRI at a tertiary care teaching hospital. The 3T MRI demonstrated superior diagnostic performance, enabled faster scan times, and achieved financial break-even within 1.5 years, confirming both clinical utility and economic viability.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTechnician-Based HTA\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eTechnicians showed a strong preference for 3T MRI, citing better SNR, faster scans, and higher diagnostic confidence. Some concerns were noted regarding patient comfort, downtime, and contrast use, but the overall perception was favourable toward 3T.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRadiologist FGD Findings\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eRadiologists unanimously agreed on the superior diagnostic value of 3T MRI in neuro and MSK cases. However, 1.5T was still preferred for implanted patients and abdominal/post-op imaging. Cost-effectiveness and safety concerns were discussed, along with the need for training and workflow adaptation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo specific funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical standards were rigorously followed in conducting this study. The study protocol was officially approved by the Institutional Ethics Committee of the Kasturba Medical College (approval number IEC2: 443/2023). All data were fully anonymized before publication. All relevant national and international guidelines and regulations were followed and study was adhered to the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from the participants including parents/ legally authorized representatives of subjects who are under 16.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u003c/strong\u003e Not Applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAyshath Shakeela (AS): Contributed in preparing the manuscript, data collection, interpretation and analysis.Somu G (SG): Contributed in terms of conceptualization of the research work and supervision of the project along with inputs for manuscript preparation.Priya PS (PP): Contributed in data collection, and guiding in throughout the project and by providing administrative support.Akshay Kumar (AK): Contributed in preparing the final manuscript and reviewing it and data collection and data interpretation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGeva T. Magnetic resonance imaging: historical perspective. J Cardiovasc Magn Reson Off J Soc Cardiovasc Magn Reson. 2006;8(4):573\u0026ndash;80. \u003c/li\u003e\n\u003cli\u003eSmith HJ. The history of magnetic resonance imaging and its reflections in Acta Radiologica. Acta Radiol Stockh Swed 1987. 2021 Nov;62(11):1481\u0026ndash;98. \u003c/li\u003e\n\u003cli\u003eRaymond Damadian [Internet]. Alumni Park. [cited 2025 May 17]. Available from: https://www.alumnipark.com/exhibits/featured/raymond-damadian/\u003c/li\u003e\n\u003cli\u003eRoguin A. Nikola Tesla: The man behind the magnetic field unit. J Magn Reson Imaging. 2004;19(3):369\u0026ndash;74. \u003c/li\u003e\n\u003cli\u003eTechnology Trends: MRI: Considerations for the Move from 1.5T to 3T [Internet]. [cited 2025 May 17]. Available from: https://www.radiologytoday.net/archive/rt0216p22.shtml\u003c/li\u003e\n\u003cli\u003eQuestions and Answers in MRI [Internet]. [cited 2025 May 21]. MRI Questions \u0026amp; Answers; MR imaging physics \u0026amp; technology. Available from: http://mriquestions.com/\u003c/li\u003e\n\u003cli\u003eHistory of MRI \u0026bull; Magnetic Resonance in Medicine \u0026ndash; The Basics \u0026ndash; by Peter A. Rinck | NMR MR MRI | Essentials, introduction, basic principles, facts, history | The primer of EMRF/TRTF. [Internet]. [cited 2025 May 21]. Available from: https://www.magnetic-resonance.org/ch/20-01.html\u003c/li\u003e\n\u003cli\u003eList Of MRI centers in India [Internet]. [cited 2025 May 17]. Available from: https://rentechdigital.com/smartscraper/business-report-details/list-of-mri-centers-in-india\u003c/li\u003e\n\u003cli\u003eUnion Minister Dr Jitendra Singh launches India\u0026rsquo;s first Indigenously developed, Affordable, lightweight, Ultrafast, High Field (1.5 Tesla), Next Generation Magnetic Resonance Imaging (MRI) Scanner in New Delhi [Internet]. [cited 2025 May 17]. Available from: https://www.pib.gov.in/www.pib.gov.in/Pressreleaseshare.aspx?PRID=1944717\u003c/li\u003e\n\u003cli\u003eLopez L. MRI Installation Guide [Internet]. medicalimagingsource.com. 2022 [cited 2025 May 17]. Available from: https://www.medicalimagingsource.com/mri-installation\u003c/li\u003e\n\u003cli\u003eSignal-to-Noise Ratio (SNR) in MRI | Factors affecting SNR [Internet]. mrimaster. [cited 2025 May 17]. Available from: https://mrimaster.com/snr/\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6925003/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6925003/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eIntroduction\u003c/b\u003e: Magnetic Resonance Imaging (MRI) is a cornerstone of modern diagnostic radiology, offering detailed visualization of anatomical structures without ionizing radiation. Rooted in nuclear magnetic resonance (NMR) principles pioneered by Bloch and Purcell, and clinically advanced by Dr. Raymond Damadian, MRI has evolved significantly since its inception. While both 1.5 Tesla (T) and 3T systems are widely used, the latter promises superior imaging performance, albeit with higher operational demands and costs. This Health Technology Assessment (HTA) compares the diagnostic, technical, economic, and perceptual parameters of 1.5T and 3T MRI systems, particularly in the context of stroke imaging.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethodology\u003c/b\u003e: A mixed-method approach was employed. Quantitatively, 400 brain stroke MRI cases (200 for each modality) were analyzed using the T2 FLAIR sequence, focusing on Signal-to-Noise Ratio (SNR) and scan time. Non-parametric statistical methods (Wilcoxon and Shapiro-Wilk tests) were applied due to data non-normality. Additionally, a validated Likert-scale questionnaire was administered to 49 MRI technicians to assess perceptions on 10 parameters, using snowball sampling. A focus group discussion (FGD) with five radiologists explored qualitative insights regarding diagnostic value, workflow, and safety across modalities.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e: Statistical analysis revealed significantly higher SNR (mean 20.0 vs. 17.8; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and shorter scan times (mean 10.2 vs. 17.6 minutes; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with 3T MRI. Monthly scan volumes increased by 22.5% post-upgrade from 1.5T to 3T. Power consumption and operational costs were also higher with 3T, requiring enhanced electrical infrastructure and cooling systems. Technician feedback revealed high agreement on superior image quality and faster scan times with 3T MRI. However, lower scores were noted for patient comfort, contrast usage, and downtime. Median scores for key parameters such as diagnostic confidence and SNR were \u0026ge;\u0026thinsp;4, indicating strong preference for 3T MRI. Thematic analysis of the FGD underscored diagnostic advantages of 3T in neurology and musculoskeletal imaging, tempered by concerns about RF heating, noise, and higher costs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiscussion\u003c/b\u003e: 3T MRI systems offer notable clinical advantages\u0026mdash;higher image resolution, greater diagnostic accuracy, and improved efficiency. These are especially valuable in acute neuroimaging, where clarity is crucial. Despite higher installation and operational demands, the overall return on investment is supported by increased throughput and diagnostic confidence. Technician and radiologist feedback corroborate objective findings, though safety in patients with implants, and operational challenges like noise and RF heating, remain concerns.\u003c/p\u003e\u003cp\u003eConclusion\u003c/p\u003e\u003cp\u003e The HTA supports the adoption of 3T MRI in tertiary care settings for its superior diagnostic and operational performance over 1.5T systems. While higher energy consumption, cost, and patient discomfort are valid considerations, the benefits in image quality and scan efficiency justify its strategic integration into advanced neuroimaging protocols. Balanced deployment, with continued use of 1.5T for certain patient subsets, is recommended to optimize clinical outcomes and resource utilization.\u003c/p\u003e","manuscriptTitle":"Health technology assessment of 3 Tesla vs 1.5 Tesla MRI to evaluate the impact of advanced imaging technology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 07:58:29","doi":"10.21203/rs.3.rs-6925003/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a200db8-8317-4f2d-9631-05649ebadb91","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-26T05:08:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-28 07:58:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6925003","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6925003","identity":"rs-6925003","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}