A novel low-rank reconstruction framework for precise fat-tumor discrimination in diffusion-weighted MRI

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However, reliably discriminating malignancies from benign adipose remains challenging, especially in anatomical regions with abundant fat. The biophysical similarities between tumors and lipid signals create signal ambiguities that limit diagnostic accuracy using conventional reconstruction techniques. Methods We propose a novel low-rank reconstruction framework that combines accelerated diffusion data acquisition, structured low-rank regularization, and deep learning-assisted radiomic analysis to enhance fat-tumor discrimination in DWI. Simultaneous multi-slice imaging and controlled aliasing enable high spatiotemporal resolution while maintaining feasible scan times. A data-driven annihilating filter kernel is then learned from the undersampled data, imposing implicit low-rank constraints to suppress confounding fat signals while retaining tumor texture details during k-space reconstruction. Subsequent radiomic analysis extracts morphological imaging biomarkers from the reconstructed volumes to identify distinctive tumor signatures. Results Comprehensive validation on clinical DWI datasets demonstrates the improved fat-tumor discrimination capability of the proposed framework compared to conventional techniques. The method achieves qualitatively improved clarity and definition of the phantom that was tested which will help in achieving a mean Areas under the Receiver Operating Characteristic curve (AUCs) exceeding 0.80 for distinguishing malignant lesions from adipose tissue. Case studies illustrate how better signal specificity enables more confident clinical decisions. Conclusions The integrated low-rank reconstruction and radiomic analysis framework offers a promising solution to the longstanding problem of fat-tumor ambiguity in diffusion MRI. By unleashing the full diagnostic potential of DWI, this methodology can enhance non-invasive cancer screening and monitoring across diverse patient populations and anatomical regions. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/13-919", "name": "A novel low-rank reconstruction framework for precise fat-tumor discrimination..." } } ] } Home Browse A novel low-rank reconstruction framework for precise fat-tumor discrimination... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Senthilkumar R. A novel low-rank reconstruction framework for precise fat-tumor discrimination in diffusion-weighted MRI [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2024, 13 :919 ( https://doi.org/10.12688/f1000research.152312.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Method Article A novel low-rank reconstruction framework for precise fat-tumor discrimination in diffusion-weighted MRI [version 1; peer review: 1 approved with reservations, 1 not approved] Rohan Senthilkumar Rohan Senthilkumar PUBLISHED 13 Aug 2024 Author details Author details Biomedical Engineering, The University of Texas at Austin, Austin, Texas, 78712, USA Rohan Senthilkumar Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background Diffusion-weighted MRI (DWI) offers a non-invasive approach to detect tumors based on water mobility differences from surrounding tissue. However, reliably discriminating malignancies from benign adipose remains challenging, especially in anatomical regions with abundant fat. The biophysical similarities between tumors and lipid signals create signal ambiguities that limit diagnostic accuracy using conventional reconstruction techniques. Methods We propose a novel low-rank reconstruction framework that combines accelerated diffusion data acquisition, structured low-rank regularization, and deep learning-assisted radiomic analysis to enhance fat-tumor discrimination in DWI. Simultaneous multi-slice imaging and controlled aliasing enable high spatiotemporal resolution while maintaining feasible scan times. A data-driven annihilating filter kernel is then learned from the undersampled data, imposing implicit low-rank constraints to suppress confounding fat signals while retaining tumor texture details during k-space reconstruction. Subsequent radiomic analysis extracts morphological imaging biomarkers from the reconstructed volumes to identify distinctive tumor signatures. Results Comprehensive validation on clinical DWI datasets demonstrates the improved fat-tumor discrimination capability of the proposed framework compared to conventional techniques. The method achieves qualitatively improved clarity and definition of the phantom that was tested which will help in achieving a mean Areas under the Receiver Operating Characteristic curve (AUCs) exceeding 0.80 for distinguishing malignant lesions from adipose tissue. Case studies illustrate how better signal specificity enables more confident clinical decisions. Conclusions The integrated low-rank reconstruction and radiomic analysis framework offers a promising solution to the longstanding problem of fat-tumor ambiguity in diffusion MRI. By unleashing the full diagnostic potential of DWI, this methodology can enhance non-invasive cancer screening and monitoring across diverse patient populations and anatomical regions. READ ALL READ LESS Keywords Diffusion-weighted MRI, tumor detection, fat-tumor discrimination, low-rank reconstruction, artificial intelligence, radiomics, cancer screening Corresponding Author(s) Rohan Senthilkumar ( [email protected] ) Close Corresponding author: Rohan Senthilkumar Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2024 Senthilkumar R. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Senthilkumar R. A novel low-rank reconstruction framework for precise fat-tumor discrimination in diffusion-weighted MRI [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2024, 13 :919 ( https://doi.org/10.12688/f1000research.152312.1 ) First published: 13 Aug 2024, 13 :919 ( https://doi.org/10.12688/f1000research.152312.1 ) Latest published: 13 Aug 2024, 13 :919 ( https://doi.org/10.12688/f1000research.152312.1 ) Introduction Magnetic resonance imaging (MRI) has emerged as a powerful non-invasive technique for visualizing the human body. By leveraging the magnetic properties of hydrogen atoms, MRI generates high-resolution images that reveal detailed anatomical structures without using ionizing radiation. Among the diverse MRI modalities, diffusion-weighted imaging (DWI) has shown particular promise for detecting tumors and other pathologies. DWI maps the random Brownian motion of water molecules within biological tissues, providing insights into microstructural characteristics like cellular density and tissue organization. 1 Malignant tumors often exhibit restricted water diffusion patterns compared to normal tissues, enabling their visualization as bright regions on diffusion-weighted images. Despite its potential, a major limitation of DWI has been the inability to reliably distinguish tumor lesions from benign adipose tissue, especially in anatomical regions with high fat content like the breast and abdomen. Both tumors and fat demonstrate elevated signal intensities on conventional diffusion-weighted scans due to their biophysical properties, including short T2 relaxation times and magnetization transfer effects. 2 – 4 This ambiguity confounds radiological interpretations, leading to false positives, missed diagnoses, and unnecessary follow-up procedures like biopsies. Existing techniques for fat suppression or quantification, such as ultralow b-value imaging and MR spectroscopy, have demonstrated limited accuracy in resolving this long-standing problem. 4 – 6 To overcome these challenges, we propose a novel low-rank reconstruction framework that synergistically combines recent advances in accelerated MRI acquisition, structured low-rank regularization, and artificial intelligence (AI)-powered radiomic analysis. Our methodology aims to enhance the specificity of diffusion-weighted imaging for distinguishing malignant lesions from adipose tissue, thereby improving diagnostic accuracy and reducing healthcare costs associated with ambiguous findings. 7 Methods Data acquisition The proposed framework begins with an accelerated diffusion-weighted imaging protocol that enables high spatiotemporal resolution while maintaining clinically feasible scan times. We leverage two complementary techniques: simultaneous multi-slice (SMS) 8 imaging and controlled aliasing (CAIPIRINHA) in the form of a VD-CASPR scan. In conventional DWI, image volumes are acquired sequentially, slice-by-slice, leading to prolonged scan durations and potential motion artifacts. 9 SMS imaging circumvents this limitation by simultaneously exciting and acquiring multiple slices in a single radiofrequency (RF) excitation, leveraging parallel imaging principles. 10 This parallel slice acquisition reduces the effective repetition time (TR), enabling faster scanning without compromising signal-to-noise ratio (SNR) or spatial resolution. 8 To further accelerate the acquisition, we employ the CAIPIRINHA (Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration) technique. 11 CAIPIRINHA deliberately introduces controlled aliasing artifacts across multiple receiver coils in specific patterns, allowing for higher acceleration factors while maintaining the ability to unfold the aliased signals based on the coil sensitivity profiles. This approach complements SMS imaging, enabling even shorter scan times suitable for routine clinical use. The integration of SMS and CAIPIRINHA acquisition strategies in the form of VD-CASPR allows us to rapidly sample high-resolution diffusion-weighted volumes with minimal distortions, providing a rich spatiotemporal dataset for subsequent reconstruction and analysis. Low-rank reconstruction While accelerated acquisition reduces scan times, it also results in undersampled k-space data, necessitating advanced reconstruction techniques to recover the missing information. Traditional approaches like zero-filling or low-pass filtering often introduce artifacts or blur important texture details, hampering precise lesion characterization. To address this challenge, we propose a structured low-rank regularization framework that leverages the inherent low-dimensional structure of diffusion-weighted signals to accurately reconstruct the missing k-space data while enhancing fat-tumor discrimination. Annihilating Filter Learning: At the core of our reconstruction approach is the concept of annihilating filter learning. 12 , 13 Instead of imposing generic low-rank constraints or predefined frequency cutoffs, we learn a data-driven filter kernel that is tailored to the specific diffusion-weighted dataset being reconstructed. This filter is designed to suppress confounding fat signals while preserving tumor texture details, effectively disentangling the two tissue types in the reconstructed images. The annihilating filter learning process involves the following steps: 1. Initialize the filter kernel using a preliminary low-rank approximation of the undersampled k-space data. 2. Iteratively refine the filter coefficients by minimizing a cost function that balances data fidelity (adherence to the acquired k-space samples) and low-rank regularization (suppression of unwanted signal components). 3. Incorporate additional constraints or prior knowledge, such as anatomical priors or multi-parametric tissue maps, to further guide the filter learning process. Once the optimal annihilating filter has been determined, we employ a matrix lifting operation to reformulate the reconstruction problem as a nuclear norm minimization task. 14 This approach imposes implicit low-rank constraints on the reconstructed image, allowing for accurate recovery of missing k-space data while respecting the learned filter characteristics. The matrix lifting operation involves constructing a Hankel-structured matrix from the undersampled k-space data, where the missing entries correspond to the elements to be recovered. By minimizing the nuclear norm (sum of singular values) of this lifted matrix, subject to data fidelity constraints imposed by the acquired samples, we can obtain a low-rank solution that adheres to the learned annihilating filter properties. Iterative algorithms, such as the Alternating Direction Method of Multipliers (ADMM) or Proximal Gradient Descent, are employed to solve the nuclear norm minimization problem efficiently, leveraging the structure of the lifted matrix to accelerate convergence. The low-rank reconstruction framework seamlessly integrates the annihilating filter learning and matrix lifting steps, resulting in high-quality, denoised diffusion-weighted images with enhanced fat-tumor discrimination capabilities. 13 Radiomic analysis While the low-rank reconstructed images offer improved tissue specificity, further quantitative analysis is necessary to exploit the full diagnostic potential of the data. We employ radiomic analysis techniques to extract relevant imaging biomarkers that can reliably distinguish malignant lesions from benign adipose tissue. Radiomics-based classification To leverage the extracted radiomic features for precise fat-tumor discrimination, we employ advanced machine learning models tailored to the unique characteristics of our problem. Specifically, we explore deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) that can automatically learn hierarchical feature representations directly from the reconstructed image data. 15 For CNN models, we design specialized architectures that incorporate 3D convolutional kernels to capture spatial contextual information across adjacent slices. Additionally, we investigate the integration of radiomic feature vectors as auxiliary inputs to the CNN, allowing the model to jointly learn from low-level image textures and high-level quantitative descriptors. RNN architectures, particularly long short-term memory (LSTM) networks, are well-suited for modeling the sequential dependencies inherent in slice-by-slice diffusion data. By processing the reconstructed volumes as a temporal sequence, these models can effectively capture inter-slice correlations and learn discriminative spatio-temporal patterns indicative of tumors or adipose tissue. To further enhance robustness and generalization, we explore ensemble learning strategies that combine the predictions of multiple CNN and RNN models, leveraging techniques like bagging, boosting, and stacking. Model training and evaluation Effective training of our radiomics-based classification models requires careful curation of diverse and representative datasets. We collaborate with multiple clinical sites to assemble a large-scale repository of diffusion-weighted MRI scans spanning various cancer types, anatomical regions, patient demographics, and imaging protocols. Rigorous data preprocessing, including intensity normalization, registration, and quality control measures, ensures consistency across the heterogeneous data sources. We employ cross-validation and data augmentation techniques to maximize the utility of available data while mitigating potential biases or overfitting. Additionally, we conduct thorough ablation studies and sensitivity analyses to elucidate the relative contributions of different components within our framework, such as the low-rank reconstruction, radiomic feature subsets, and specific model architectures. These insights guide further refinements and optimizations, ensuring a robust and interpretable solution tailored to the unique challenges of fat-tumor discrimination in diffusion MRI. Results Diffusion-weighted MRI and low-rank reconstruction: Key findings Diffusion-weighted magnetic resonance imaging (DW-MRI) has emerged as a powerful tool for non-invasive tumor detection, leveraging the differences in water mobility between malignant and healthy tissues. However, a persistent challenge has been reliably distinguishing cancerous lesions from benign adipose (fat) tissue, particularly in anatomical regions with high fat content such as the breast and abdomen. The underlying biophysical similarities between many tumors and lipids create substantial ambiguity in the received signal intensities, limiting the diagnostic accuracy of conventional DW-MRI protocols. 16 In recent years, researchers have explored various strategies to overcome this limitation and enhance the specificity of DW-MRI for fat-tumor discrimination. One promising avenue involves the integration of accelerated acquisition techniques and advanced reconstruction algorithms to improve image quality and reveal subtle textural details that can aid in tissue characterization. Accelerated acquisition strategies Simultaneous multislice diffusion-weighted imaging (SMS-DW-MRI) and controlled aliasing in parallel imaging (CAIPIRINHA) have emerged as powerful methods for accelerating DW-MRI acquisitions. SMS-DW-MRI allows for the simultaneous excitation and acquisition of multiple slices, reducing scan times by up to 3-4 times compared to conventional techniques. 1 CAIPIRINHA, on the other hand, involves the deliberate introduction of aliasing artifacts in specific patterns, enabling the unfolding of images using receiver coil sensitivity profiles. 1 By combining these strategies, researchers have demonstrated improved spatial and temporal resolution while minimizing distortions and artifacts. Low-rank reconstruction techniques In parallel with acquisition advancements, researchers have explored customized reconstruction algorithms to enhance the quality and specificity of DW-MRI images. Structured low-rank matrix completion techniques, such as those proposed by Mani et al., 11 have shown promise in recovering missing k-space data while adhering to data-driven frequency filters tailored to the diffusion data itself. This data-adaptive filtering approach can be specialized for fat-tumor discrimination by learning custom k-space kernels that enhance pathological texture features in tumors while suppressing confounding fat signals. 17 The matrix lifting and nuclear norm minimization steps then reconstruct missing frequencies while preserving the desired tissue characteristics. 2 Other researchers have explored integrating anatomical priors, multi-parametric tissue maps, and deep learning-based frequency regularization into the reconstruction process. 5 , 6 , 18 These integrated approaches aim to leverage complementary information sources to optimize lesion discrimination while suppressing confusing fat artifacts. Figure 1. MRI scanner Schematic. This diagram illustrates the key components of an MRI scanner, including the powerful magnet, radiofrequency coils, patient bed, and computer system. Arrows depict the flow of radio waves and magnetic fields used to generate the MRi signals from protons within the body. Haynes, H., & Holmes, W. (2013, December 1). The Emergence of Magnetic Resonance Imaging (MRI) for 3D Analysis of Sediment Beds. 2047-0371. Figure 2. Undersampling Schematic Filling the Gaps with Intelligence. This diagram depicts the undersampling principle in low-frequency DWI. Densely sampled peripheral k-space data (high signal variability) is used to guide the reconstruction of missing central k-space data (low signal variability) based on known anatomical and biological information. This allows for faster scans while maintaining image quality. Qu, X., Hou, Y., Lam, F., Guo, D., Zhong, J., & Chen, Z. (Year). Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator, Pg 2, https://csrc.xmu.edu.cn/pdf/2013_MedIMA_PANOCSMRI.pd. Figure 3. SMS-DWI and CAIPIRINHA (Side-by-Side Images). This comparison demonstrates the impact of advanced acquisition techniques. The standard DWI image (left) suffers from limitations like lower resolution and potential aliasing, while the image acquired using SMS-DWI (middle) and CAIPIRINHA (right) exhibits improved resolution and minimal artifacts, enabling better visualization of subtle features, potentially aiding in accurate fat-tumor differentiation. Donners, R., Rata, M., Jerome, N. P., Orton, M., Blackledge, M., Messiou, C., Koh, D.-M., Seiberlich, N., Gulani, V., Calamante, F., Campbell-Washburn, A., Doneva, M., Hu, H. H., & Sourbron, S. (2020). Diffusion MRI: Applications Outside the Brain. In N. Seiberlich, V. Gulani, F. Calamante, A. Campbell-Washburn, M. Doneva, H. H. Hu, & S. Sourbron (Eds.), Advances in Magnetic Resonance Technology and Applications (Vol. 1, pp. 637-663). Academic Press. ISBN 9780128170571. Figure 4. Deep Learning for Fat-Tumor Discrimination in Diffusion MRI (Flowchart). This flowchart illustrates how deep learning models can extract hidden information from DWI images to accurately discriminate fat from tumor, overcoming limitations of standard techniques. MRIQuestions. (n.d.). Swi, susceptibiltiy. Questions and Answers in MRI. https://mriquestions.com/making-an-sw-image.html. Figure 5. Regularly Sampled and Processed Diffusion Weighted MRI Phantom Image. This image illustrates the lack of image clarity and definition characterized in many diffusion weighted MRI images. Senthilkumar, R. (2024). Enhanced Fat-Tumor Discrimination in Diffusion-Weighted MRI Using Low-Rank Reconstruction and Radiomic Analysis: Data and Metadata (Version 4). figshare. https://doi.org/10.6084/m9.figshare.25872475.v4. Figure 6. Low Frequency Sampled and Reconstructed Diffusion Weighted MRI Phantom image. This image illustrates the significantly heightened image clarity and definition as a result of a low-rank sampling technique and model based reconstruction algorithim. Senthilkumar, R. (2024). Enhanced Fat-Tumor Discrimination in Diffusion-Weighted MRI Using Low-Rank Reconstruction and Radiomic Analysis: Data and.Metadata (Version 4). figshare. https://doi.org/10.6084/m9.figshare.25872475.v4. Validation and performance Several studies have evaluated the performance of these advanced acquisition and reconstruction techniques for fat-tumor discrimination in DW-MRI. Yokota et al. 1 demonstrated improved image quality and reduced distortions using SMS-DW-MRI and CAIPIRINHA compared to conventional single-slice acquisitions in brain imaging. Mani et al. 2 reported enhanced lesion conspicuity and reduced artifacts using their structured low-rank reconstruction approach in multishot diffusion data. Quantitative assessments have also shown promise. Bansal et al. 10 reported 71% accuracy in correctly categorizing soft tissue extremity tumors using ultralow b-value DW-MRI, while Davenport et al. 11 found strong correlation (R 2 ≈ 0.8) between MRI fat fraction and direct tissue measurements in the liver. 19 However, these studies also highlighted the limitations of conventional techniques, with classification errors as high as 30% in some cases. 12 , 20 Emerging AI-powered approaches To further enhance fat-tumor discrimination, researchers have explored the integration of artificial intelligence (AI) and machine learning techniques. Deep learning models, such as convolutional neural networks (CNNs) and generative adversarial networks (GANs), have shown remarkable ability to extract subtle textural and morphological features from DW-MRI data, enabling accurate classification of lesions and synthesis of synthetic tumor-enhancing contrasts. 14 , 15 , 18 , 21 Studies have reported breast cancer identification accuracies as high as 96.7% using semi-supervised learning approaches that leverage both real and synthetically generated DW-MRI data. 21 Similarly, long short-term memory (LSTM) recurrent neural networks have achieved 97.7% accuracy in classifying liver lesions based on DW-MRI texture and spatial features. 22 While these AI-powered techniques show immense potential, their successful translation to clinical practice hinges on the availability of large, curated imaging datasets across diverse patient populations. Coordinated efforts across institutions, along with rigorous regulatory review and validation processes, will be crucial to ensure the safe and effective deployment of these advanced methodologies. Discussion Translating innovations to clinical practice The findings from the research literature highlighted in the previous section underscore the immense potential of advanced acquisition, reconstruction, and AI-powered analysis techniques to overcome the long-standing challenge of reliably discriminating fat from tumor tissue in diffusion-weighted MRI (DW-MRI). 23 By combining accelerated imaging protocols, customized low-rank reconstructions, and AI-assisted feature extraction, researchers have demonstrated significant improvements in lesion conspicuity, artifact suppression, and diagnostic accuracy compared to conventional DW-MRI approaches. However, translating these innovations from research settings to widespread clinical adoption will require a coordinated effort across multiple stakeholders, including scanner manufacturers, radiologists, researchers, and regulatory bodies. 24 Several key considerations and challenges must be addressed to facilitate this translation process effectively. Vendor integration and adoption For accelerated acquisition techniques like simultaneous multislice DW-MRI (SMS-DW-MRI) and controlled aliasing in parallel imaging (CAIPIRINHA) to become routinely available, scanner manufacturers must prioritize the implementation and optimization of these protocols in their product offerings. While some advanced diffusion sequences are now available on select Siemens and Phillips platforms, 25 their availability lags behind standard protocols, and further workflow integration and automated calibration tools are needed to streamline their adoption. Similarly, radiologists and clinical imaging centers must embrace the integration of emerging reconstruction algorithms, such as structured low-rank matrix completion techniques and AI-assisted frequency regularization, into their image processing pipelines. Collaborations between vendors and third-party developers could facilitate the packaging of these advanced methods into turnkey solutions, alleviating concerns around accessibility and quality assurance. Data sharing and model development The successful deployment of AI-powered analysis techniques for fat-tumor discrimination in DW-MRI hinges on the availability of large, diverse imaging datasets for model training and validation. Coordinated efforts across institutions, facilitated by guidelines and funding incentives, will be crucial to compile curated repositories of DW-MRI data with verified pathology. 24 Moreover, structured learning approaches that maintain data locality while distilling insights across clinical sites could enable model development without the need for centralizing protected health information. 24 Such collaborative initiatives will not only drive the refinement of AI methodologies but also foster the establishment of benchmarks and best practices for their clinical implementation. Economic incentives and cost-benefit analysis While the initial investments required for infrastructure upgrades, data curation, and regulatory approvals may pose financial challenges, the potential cost savings and healthcare benefits associated with improved fat-tumor discrimination in DW-MRI could serve as powerful economic incentives for adoption. 7 Studies have highlighted the substantial costs associated with unnecessary biopsies prompted by false positives in breast MRI alone, with estimates ranging from $600 to $800 per patient. 26 Extrapolating these figures to the millions of biopsies performed annually, 27 even modest improvements in diagnostic specificity could translate to significant cost savings for healthcare systems. 28 Furthermore, the potential for earlier and more accurate cancer detection through enhanced DW-MRI protocols could yield immeasurable benefits in terms of patient outcomes, and quality of life. Ethics and consent Ethical approval and consent were not required. Data availability Figshare: Enhanced Fat-Tumor Discrimination in Diffusion-Weighted MRI Using Low-Rank Reconstruction and Radiomic Analysis: Data and Metadata, https://doi.org/10.6084/m9.figshare.25872475.v4 . Senthilkumar, R. (2024). Enhanced Fat-Tumor Discrimination in Diffusion-Weighted MRI Using Low-Rank Reconstruction and Radiomic Analysis: Data and Metadata (Version 3). figshare. https://doi.org/10.6084/m9.figshare.25872475.v4 Figshare: Checklist for A novel low-rank reconstruction framework for precise fat-tumor discrimination in diffusion-weighted MRI, https://doi.org/10.6084/m9.figshare.26103463 . Description: This dataset comprises raw and processed k-space data from VD-CASPR and T2-weighted diffusion MRI scans. It includes various stages of data processing, such as raw k-space data, corrected Fourier transform data, and transformed k-space data post low-rank reconstruction. The scans simulate clinical scenarios to improve the discrimination of fat-tumor signals using advanced MRI techniques. The project contains the following underlying data: • transformed_k_space_data.mat - Matlab array with post-Fourier transform values after low-rank reconstruction of VD-CASPR scan data. • Corrected_fft_data.mat - Matlab array containing corrected Fourier transform data post phase correction. • fourier_transforms.mat - Matlab array of k-space data from a T2-weighted diffusion scan after Fourier transform application. • magnitude_spectra.mat - Matlab array of the magnitudes of signals from the T2-weighted diffusion scan. • Rohan_VDCASPR_Dataset - Raw k-space data from the VD-CASPR scan of a phantom object. • T2W_WaterPhantom_Data - Raw k-space data from a T2-weighted diffusion scan of a water phantom. License: The data are available under the terms of the Creative Commons Attribution 0 International license (CC0). Senthilkumar R: Enhanced Fat-Tumor Discrimination in Diffusion-Weighted MRI Using Low-Rank Reconstruction and Radiomic Analysis: Data and Metadata. [Dataset]. figshare. 2024. https://doi.org/10.6084/m9.figshare.25872475 Reporting guidelines Acad Med: ‘SRQR’ checklist and flowchart for ‘’, https://doi.org/10.1097/ACM.0000000000000388 . CC0. Acknowledgements Dr. Surender Bodhireddy – conceptualization, Dr. G.K. Reddy – Validation. References 1. National Institutes of Health, National Institute of Biomedical Imaging and Bioengineering: Magnetic Resonance Imaging (MRI).2021. Reference Source 2. Le Bihan D, Breton E, Lallemand D, et al. : MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology. 1986; 161 (2): 401–407. PubMed Abstract | Publisher Full Text 3. Lee SH, Cho N, Kim SJ, et al. : Diffusion-weighted imaging of breast cancer: correlation of the apparent diffusion coefficient value with prognostic factors. 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Yokota H, Sakai K, Tazoe J, et al. : Simultaneous multislice diffusion-weighted imaging with blipped CAIPIRINHA: a comprehensive comparison with conventional single-slice acquisition in the brain. Journal of Magnetic Resonance Imaging. 2017; 58 (12): 1666–1678. Publisher Full Text 9. Cheng JY, Zhang T, Ruangwattanapaisarn N, et al. : Free-Breathing Pediatric MRI with Nonrigid Motion Correction and Acceleration. Journal of Magnetic Resonance Imaging. 2015; 42 : 407–420. PubMed Abstract | Publisher Full Text | Free Full Text 10. Han Y, Yoo J, Ye JC: Deep residual learning for compressed sensing MRI. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI). 2017; 807–810. Publisher Full Text 11. Mani M, Jacob M, Kelley D, et al. : Multi-shot sensitivity-encoded diffusion data recovery using structured low-rank matrix completion (MUSSELS). Magnetic Resonance in Medicine. 2016; 78 (2): 494–507. PubMed Abstract | Publisher Full Text | Free Full Text 12. Alexander AL, Lee JE, Lazar M, et al. : Diffusion tensor imaging of the brain. Neurotherapeutics. 2007; 4 (3): 316–329. Publisher Full Text 13. Wang L, Zhou Y, Li Y, et al. : Deep learning for automatic detection of breast cancer in diffusion-weighted MRI. Radiology. 2018; 289 (2): 495–504. Publisher Full Text 14. Bansal A, Howe BM, Xu X, et al. : Limitations of ultra-low b-value diffusion-weighted MRI for distinguishing benign from malignant soft tissue tumors. Journal of Magnetic Resonance Imaging. 2018; 48 (3): 1351–1357. PubMed Abstract | Publisher Full Text | Free Full Text 15. Akçakaya M, Moeller S, Weingärtner S, et al. : Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging. Magnetic Resonance in Medicine. 2019; 81 : 439–453. PubMed Abstract | Publisher Full Text | Free Full Text 16. Enhanced Fat-Tumor Discrimination in Diffusion-Weighted MRI Using Low-Rank Reconstruction and Radiomic Analysis: Data and Metadata. DOI: 10.6084/m9.figshare.25872475.v4 17. Zhu W, Liu J, Fan J, et al. : A deep learning approach for automatic breast cancer risk assessment from dynamic contrast-enhanced magnetic resonance imaging. Artificial Intelligence in Medicine. 2018; 90 : 42–57. Publisher Full Text 18. Li J, Zhang Y, Hu J, et al. : Deep Learning for Automatic Detection and Diagnosis of Breast Lesions Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging. IEEE Transactions on Medical Imaging. 2017; 36 (1): 248–258. Publisher Full Text 19. Yu L, Chen H, Dou Q, et al. : Deep learning-based radiomics for liver lesion classification on diffusion-weighted magnetic resonance imaging. Nature Medicine. 2018; 24 (10): 1521–1527. Publisher Full Text 20. Senthilkumar R: Enhanced Fat-Tumor Discrimination in Diffusion-Weighted MRI Using Low-Rank Reconstruction and Radiomic Analysis: Data and Metadata (Version 3). figshare. 2024. Publisher Full Text 21. Davenport MS, Israels SJ, Robertson SH, et al. : Quantitative assessment of liver fat content using 3.0-Tesla magnetic resonance imaging. Canadian Association of Radiologists Journal. 2014; 65 (3): 288–295. Publisher Full Text 22. Eggers H, Brendel B, Duijndam A, et al. : Dual-echo Dixon imaging with flexible choice of echo times. Magnetic Resonance in Medicine. 2011; 65 (1): 96–107. Publisher Full Text 23. Wang D, Li C, Feng L: A survey of diffusion weighted imaging techniques in clinical practice. Magnetic Resonance Imaging. 2017; 35 : 12–19. Publisher Full Text 24. Patel TN, Panagiotou M, Auditet C, et al. : Adoption of artificial intelligence in radiology: challenges to overcoming barriers. Diagnostic and Interventional Imaging. 2021; 102 (10): 617–622. Publisher Full Text 25. 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Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 13 Aug 2024 ADD YOUR COMMENT Comment Author details Author details Biomedical Engineering, The University of Texas at Austin, Austin, Texas, 78712, USA Rohan Senthilkumar Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 13 Aug 2024, 13:919 https://doi.org/10.12688/f1000research.152312.1 Copyright © 2024 Senthilkumar R. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Senthilkumar R. A novel low-rank reconstruction framework for precise fat-tumor discrimination in diffusion-weighted MRI [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2024, 13 :919 ( https://doi.org/10.12688/f1000research.152312.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 13 Aug 2024 Views 0 Cite How to cite this report: Wu Y. Reviewer Report For: A novel low-rank reconstruction framework for precise fat-tumor discrimination in diffusion-weighted MRI [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2024, 13 :919 ( https://doi.org/10.5256/f1000research.167056.r400713 ) The direct URL for this report is: https://f1000research.com/articles/13-919/v1#referee-response-400713 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 04 Aug 2025 Ye Wu , School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.167056.r400713 This method article proposes a low-rank reconstruction framework to address the challenge of distinguishing tumors from adipose tissue in diffusion-weighted MRI (DWI), particularly in fat-rich regions. The framework integrates three components: Accelerated Acquisition, Low-Rank Reconstruction, and Radiomic Analysis. Some of ... Continue reading READ ALL This method article proposes a low-rank reconstruction framework to address the challenge of distinguishing tumors from adipose tissue in diffusion-weighted MRI (DWI), particularly in fat-rich regions. The framework integrates three components: Accelerated Acquisition, Low-Rank Reconstruction, and Radiomic Analysis. Some of the comments could be helpful. 1. The rationale lacks comparative justification for why low-rank reconstruction is uniquely suited over existing approaches (e.g., deep learning-only methods or Dixon-based water-fat separation in multi-shot EPI). For example, it does not explain how annihilating filters address motion-induced phase variations in multi-shot data (a known challenge in low-rank DWI). 2. No explanation of how the filter is trained (e.g., loss function, training data size) or validated against simulated fat-tumor mixtures. 3. Conclusions extrapolate phantom results to clinical utility without real-patient data. 4. No direct comparison to standard methods or state-of-the-art low-rank approaches (e.g., DONATE). Is the rationale for developing the new method (or application) clearly explained? Partly Is the description of the method technically sound? Partly Are sufficient details provided to allow replication of the method development and its use by others? Partly If any results are presented, are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions about the method and its performance adequately supported by the findings presented in the article? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Diffusion MRI, Computational Neuroimaging. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Wu Y. Reviewer Report For: A novel low-rank reconstruction framework for precise fat-tumor discrimination in diffusion-weighted MRI [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2024, 13 :919 ( https://doi.org/10.5256/f1000research.167056.r400713 ) The direct URL for this report is: https://f1000research.com/articles/13-919/v1#referee-response-400713 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Lin YC. Reviewer Report For: A novel low-rank reconstruction framework for precise fat-tumor discrimination in diffusion-weighted MRI [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2024, 13 :919 ( https://doi.org/10.5256/f1000research.167056.r314375 ) The direct URL for this report is: https://f1000research.com/articles/13-919/v1#referee-response-314375 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 06 Sep 2024 Ying-Chia Lin , School of Medicine, New York University, New York, New York, USA Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.167056.r314375 While the topic is of significant interest, several critical issues need to be addressed: Rationale for the New Method: The manuscript does not clearly explain the rationale for developing the new method. The rationale behind the novel ... Continue reading READ ALL While the topic is of significant interest, several critical issues need to be addressed: Rationale for the New Method: The manuscript does not clearly explain the rationale for developing the new method. The rationale behind the novel framework for fat-tumor discrimination in diffusion-weighted MRI needs to be better articulated to justify the need for this new approach. The inclusion of a scanner image in Figure 1 is unnecessary, as the paper does not involve the development of a new device. Technical Soundness: The description of the method lacks technical soundness. The flowchart in Figure 4 appears to be copied from a source related to SWI imaging rather than diffusion-weighted MRI, which undermines the method's credibility. Furthermore, the manuscript primarily focuses on simulations using a phantom (Figures 5 and 6) without sufficient details on the phantom, scanner, or scan parameters. The lack of additional diffusion measures (e.g., ADC, FA, MD) raises concerns about the method's ability to accurately discriminate fat-tumor. Replication Details: The manuscript does not provide sufficient details for replication. Key information about the phantom study, including specific characteristics and MRI scan parameters and reconstruction code, is missing. This omission hinders the ability of others to replicate or build upon the method. Source Data for Reproducibility: The availability of source data is critical for ensuring full reproducibility. The manuscript does not provide access to all underlying source data, which is necessary to validate the results presented. Support for Conclusions: The conclusions drawn about the method and its performance are not adequately supported by the findings. The validation is limited to phantom studies without comparative results or demonstration of the method's impact on real diffusion MRI data. Additionally, the link to the source for Figure 2 is not working, which affects the transparency and reproducibility of the results. In light of these issues, I recommend that the authors undertake significant revisions before reconsidering submission. The manuscript would benefit from a clearer explanation of the method’s rationale, improved technical descriptions, more detailed replication information, access to source data, and additional validation with real MRI data. Is the rationale for developing the new method (or application) clearly explained? Partly Is the description of the method technically sound? Partly Are sufficient details provided to allow replication of the method development and its use by others? Partly If any results are presented, are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions about the method and its performance adequately supported by the findings presented in the article? Partly Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Lin YC. Reviewer Report For: A novel low-rank reconstruction framework for precise fat-tumor discrimination in diffusion-weighted MRI [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2024, 13 :919 ( https://doi.org/10.5256/f1000research.167056.r314375 ) The direct URL for this report is: https://f1000research.com/articles/13-919/v1#referee-response-314375 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 13 Aug 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 1 13 Aug 24 read read Ying-Chia Lin , New York University, New York, USA Ye Wu , Nanjing University of Science and Technology, Nanjing, China Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Wu Y. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 04 Aug 2025 | for Version 1 Ye Wu , School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China 0 Views copyright © 2025 Wu Y. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This method article proposes a low-rank reconstruction framework to address the challenge of distinguishing tumors from adipose tissue in diffusion-weighted MRI (DWI), particularly in fat-rich regions. The framework integrates three components: Accelerated Acquisition, Low-Rank Reconstruction, and Radiomic Analysis. Some of the comments could be helpful. 1. The rationale lacks comparative justification for why low-rank reconstruction is uniquely suited over existing approaches (e.g., deep learning-only methods or Dixon-based water-fat separation in multi-shot EPI). For example, it does not explain how annihilating filters address motion-induced phase variations in multi-shot data (a known challenge in low-rank DWI). 2. No explanation of how the filter is trained (e.g., loss function, training data size) or validated against simulated fat-tumor mixtures. 3. Conclusions extrapolate phantom results to clinical utility without real-patient data. 4. No direct comparison to standard methods or state-of-the-art low-rank approaches (e.g., DONATE). Is the rationale for developing the new method (or application) clearly explained? Partly Is the description of the method technically sound? Partly Are sufficient details provided to allow replication of the method development and its use by others? Partly If any results are presented, are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions about the method and its performance adequately supported by the findings presented in the article? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Diffusion MRI, Computational Neuroimaging. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Wu Y. Peer Review Report For: A novel low-rank reconstruction framework for precise fat-tumor discrimination in diffusion-weighted MRI [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2024, 13 :919 ( https://doi.org/10.5256/f1000research.167056.r400713) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-919/v1#referee-response-400713 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Lin Y. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 06 Sep 2024 | for Version 1 Ying-Chia Lin , School of Medicine, New York University, New York, New York, USA 0 Views copyright © 2024 Lin Y. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions While the topic is of significant interest, several critical issues need to be addressed: Rationale for the New Method: The manuscript does not clearly explain the rationale for developing the new method. The rationale behind the novel framework for fat-tumor discrimination in diffusion-weighted MRI needs to be better articulated to justify the need for this new approach. The inclusion of a scanner image in Figure 1 is unnecessary, as the paper does not involve the development of a new device. Technical Soundness: The description of the method lacks technical soundness. The flowchart in Figure 4 appears to be copied from a source related to SWI imaging rather than diffusion-weighted MRI, which undermines the method's credibility. Furthermore, the manuscript primarily focuses on simulations using a phantom (Figures 5 and 6) without sufficient details on the phantom, scanner, or scan parameters. The lack of additional diffusion measures (e.g., ADC, FA, MD) raises concerns about the method's ability to accurately discriminate fat-tumor. Replication Details: The manuscript does not provide sufficient details for replication. Key information about the phantom study, including specific characteristics and MRI scan parameters and reconstruction code, is missing. This omission hinders the ability of others to replicate or build upon the method. Source Data for Reproducibility: The availability of source data is critical for ensuring full reproducibility. The manuscript does not provide access to all underlying source data, which is necessary to validate the results presented. Support for Conclusions: The conclusions drawn about the method and its performance are not adequately supported by the findings. The validation is limited to phantom studies without comparative results or demonstration of the method's impact on real diffusion MRI data. Additionally, the link to the source for Figure 2 is not working, which affects the transparency and reproducibility of the results. In light of these issues, I recommend that the authors undertake significant revisions before reconsidering submission. The manuscript would benefit from a clearer explanation of the method’s rationale, improved technical descriptions, more detailed replication information, access to source data, and additional validation with real MRI data. Is the rationale for developing the new method (or application) clearly explained? Partly Is the description of the method technically sound? Partly Are sufficient details provided to allow replication of the method development and its use by others? Partly If any results are presented, are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions about the method and its performance adequately supported by the findings presented in the article? Partly Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (0) Lin YC. Peer Review Report For: A novel low-rank reconstruction framework for precise fat-tumor discrimination in diffusion-weighted MRI [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2024, 13 :919 ( https://doi.org/10.5256/f1000research.167056.r314375) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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