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Galey L, Olanrewaju A, Nabi H, Paquette JS, Pouliot F, Audet-Walsh É. PSA, an outdated biomarker for prostate cancer: In search of a more specific biomarker, citrate takes the spotlight. J Steroid Biochem Mol Biol 2024; 243:106588. [PMID: 39025336 DOI: 10.1016/j.jsbmb.2024.106588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 07/12/2024] [Accepted: 07/14/2024] [Indexed: 07/20/2024]
Abstract
The prevailing biomarker employed for prostate cancer (PCa) screening and diagnosis is the prostate-specific antigen (PSA). Despite excellent sensitivity, PSA lacks specificity, leading to false positives, unnecessary biopsies and overdiagnosis. Consequently, PSA is increasingly less used by clinicians, thus underscoring the imperative for the identification of new biomarkers. An emerging biomarker in this context is citrate, a molecule secreted by the normal prostate, which has been shown to be inversely correlated with PCa. Here, we discuss about PSA and its usage for PCa diagnosis, its lack of specificity, and the various conditions that can affect its levels. We then provide our vision about what we think would be a valuable addition to our PCa diagnosis toolkit, citrate. We describe the unique citrate metabolic program in the prostate and how this profile is reprogrammed during carcinogenesis. Finally, we summarize the evidence that supports the usage of citrate as a biomarker for PCa diagnosis, as it can be measured in various patient samples and be analyzed by several methods. The unique relationship between citrate and PCa, combined with the stability of citrate levels in other prostate-related conditions and the simplicity of its detection, further accentuates its potential as a biomarker.
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Affiliation(s)
- Lucas Galey
- Endocrinology - Nephrology Research Axis, Centre de recherche du CHU de Québec - Université Laval, Québec City, Canada; Department of Molecular Medicine, Faculty of Medicine, Université Laval, Québec City, Canada; Centre de recherche sur le cancer de l'Université Laval, Québec City, Canada
| | - Ayokunle Olanrewaju
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA; Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Hermann Nabi
- Centre de recherche sur le cancer de l'Université Laval, Québec City, Canada
| | - Jean-Sébastien Paquette
- Laboratoire de recherche et d'innovation en médecine de première ligne (ARIMED), Groupe de médecine de famille universitaire de Saint-Charles-Borromée, CISSS Lanaudière, Saint-Charles-Borromée, QC, Canada; VITAM Research Centre for Sustainable Health, Québec, QC, Canada; Department of Family Medicine and Emergency Medicine, Faculty of Medicine, Université Laval, Québec, QC, Canada
| | - Frédéric Pouliot
- Centre de recherche sur le cancer de l'Université Laval, Québec City, Canada; Department of Family Medicine and Emergency Medicine, Faculty of Medicine, Université Laval, Québec, QC, Canada; Department of surgery, Faculty of Medicine, Université Laval, Québec, QC, Canada
| | - Étienne Audet-Walsh
- Endocrinology - Nephrology Research Axis, Centre de recherche du CHU de Québec - Université Laval, Québec City, Canada; Department of Molecular Medicine, Faculty of Medicine, Université Laval, Québec City, Canada; Centre de recherche sur le cancer de l'Université Laval, Québec City, Canada.
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Starobinets O, Simko JP, Gibbons M, Kurhanewicz J, Carroll PR, Noworolski SM. The impact of benign tissue within cancerous regions in the prostate: Characterizing sparse and dense prostate cancers on whole-mount histopathology and on multiparametric MRI. Magn Reson Imaging 2024; 114:110233. [PMID: 39260625 DOI: 10.1016/j.mri.2024.110233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 08/22/2024] [Accepted: 09/06/2024] [Indexed: 09/13/2024]
Abstract
PURPOSE To establish the incidence, size, zonal location and Gleason Score(GS)/Gleason Grade Group(GG) of sparse versus dense prostate cancer (PCa) lesions and to identify the imaging characteristics of sparse versus dense cancers on multiparametric MRI (mpMRI). METHODS Seventy-six men with untreated PCa were scanned prior to prostatectomy with endorectal-coil 3 T MRI including T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced MRI. Cancerous regions were outlined and graded on the whole-mount, processed specimens, with tissue compositions estimated. Regions with cancer comprising <50 % and ≥ 50 % of the tissue were considered sparse and dense respectively. Regions of interest (ROI) were manually drawn on T2-weighted MRI. Within each patient, area-weighted ROI averages were calculated for each imaging measure for each tissue type, GS/GG, and sparse/dense composition. RESULTS A large number of cancer regions were identified on histopathology (n = 1193: 939 (peripheral zone (PZ)) and 254 (transition zone (TZ))). Thirty-seven percent of these lesions were sparse. Sparse lesions were primarily low-grade with the majority of PZ and 100 % of TZ sparse lesions ≤GS3 + 3/GG1. Dense lesions were significantly larger than sparse lesions in both PZ and TZ, p < 0.0001. On imaging, 246/45 PZ and 109/8 TZ dense/sparse 2D cancerous ROIs were drawn. Sparse GS3 + 3 and sparse ≥GS3 + 4 cancers did not have significantly different MRI intensities to dense GS3 + 3 cancers, while sparse GS3 + 3/GG1 cancers differed from benign, p < 0.05. CONCLUSION Histopathologically identified prostate cancer lesions were sparse in 37 % of cases. Sparse cancers were entirely low grade in TZ and predominantly low-grade in PZ and generally small, thus likely posing lower risk for spread and progression than dense lesions. Sparse lesions were not distinguishable from dense lesions on mpMRI, but could be distinguished from benign tissues.
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Affiliation(s)
- Olga Starobinets
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143, USA; The Graduate Group in Bioengineering, University of California, San Francisco and Berkeley, Berkeley, CA 94720, USA
| | - Jeffry P Simko
- Department of Pathology, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Urology, University of California, San Francisco, San Francsico, CA 94143, USA; UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA.
| | - Matthew Gibbons
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143, USA.
| | - John Kurhanewicz
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143, USA; The Graduate Group in Bioengineering, University of California, San Francisco and Berkeley, Berkeley, CA 94720, USA; UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA.
| | - Peter R Carroll
- Department of Urology, University of California, San Francisco, San Francsico, CA 94143, USA; UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA.
| | - Susan M Noworolski
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143, USA; The Graduate Group in Bioengineering, University of California, San Francisco and Berkeley, Berkeley, CA 94720, USA; UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA.
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Schouten D, van der Laak J, van Ginneken B, Litjens G. Full resolution reconstruction of whole-mount sections from digitized individual tissue fragments. Sci Rep 2024; 14:1497. [PMID: 38233535 PMCID: PMC10794243 DOI: 10.1038/s41598-024-52007-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 01/12/2024] [Indexed: 01/19/2024] Open
Abstract
Whole-mount sectioning is a technique in histopathology where a full slice of tissue, such as a transversal cross-section of a prostate specimen, is prepared on a large microscope slide without further sectioning into smaller fragments. Although this technique can offer improved correlation with pre-operative imaging and is paramount for multimodal research, it is not commonly employed due to its technical difficulty, associated cost and cumbersome integration in (digital) pathology workflows. In this work, we present a computational tool named PythoStitcher which reconstructs artificial whole-mount sections from digitized tissue fragments, thereby bringing the benefits of whole-mount sections to pathology labs currently unable to employ this technique. Our proposed algorithm consists of a multi-step approach where it (i) automatically determines how fragments need to be reassembled, (ii) iteratively optimizes the stitch using a genetic algorithm and (iii) efficiently reconstructs the final artificial whole-mount section on full resolution (0.25 µm/pixel). PythoStitcher was validated on a total of 198 cases spanning five datasets with a varying number of tissue fragments originating from different organs from multiple centers. PythoStitcher successfully reconstructed the whole-mount section in 86-100% of cases for a given dataset with a residual registration mismatch of 0.65-2.76 mm on automatically selected landmarks. It is expected that our algorithm can aid pathology labs unable to employ whole-mount sectioning through faster clinical case evaluation and improved radiology-pathology correlation workflows.
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Affiliation(s)
- Daan Schouten
- Department of Pathology, Radboud University Medical Centre, Nijmegen, The Netherlands.
| | - Jeroen van der Laak
- Department of Pathology, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Bram van Ginneken
- Department of Radiology, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Geert Litjens
- Department of Pathology, Radboud University Medical Centre, Nijmegen, The Netherlands
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Møller JM, Boesen L, Hansen AE, Kettles K, Løgager V. Quantification of cross-vendor variation in ADC measurements in vendor-specific prostate MRI-protocols. Eur J Radiol 2023; 165:110942. [PMID: 37364483 DOI: 10.1016/j.ejrad.2023.110942] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/14/2023] [Accepted: 06/19/2023] [Indexed: 06/28/2023]
Abstract
PURPOSE The purpose of this study was to quantify the variability of Apparent Diffusion Coefficient (ADC) and test if there were statistically significant differences in ADC between MRI systems and sequences. METHOD With a two-chamber cylindrical ADC phantom with fixed ADC values (1,000 and 1,600x10-6 mm2/s) a single-shot (ss) Echo Planar Imaging (EPI), a multi-shot EPI, a reduced field of view DWI (zoom) and a Turbo Spin Echo DWI sequence were tested in six MRI systems from three vendors at 1.5 T and 3 T. Technical parameters were according to Prostate Imaging Reporting and Data System Version 2.1. ADC maps were calculated by vendor specific algorithms. Absolute and relative differences in ADC from the phantom-ADC were calculated and differences between sequences were tested. RESULTS At 3 T absolute differences from phantom given ADC (∼1,000 and ∼ 1,600x10-6 mm2/s) were -83 - 42x10-6 mm2/s (-8.3%-4.2%) and -48 - 15x10-6 mm2/s (-3%-0.9%), respectively and at 1.5 T absolute differences were -81 - 26x10-6 mm2/s (-2.6%-8.1%) and -74 - 67x10-6 mm2/s (-4.6%-4.2%), respectively. Significant statistical differences in ADC measurements were identified between vendors in all sequences except for ssEPI and zoom at 3 T in the 1,600x10-6 mm2/s phantom chamber. Significant differences were also identified between ADC measurements at 1.5 T and 3 T in some of the sequences and vendors, but not all. CONCLUSION The variation of ADC between different MRI systems and prostate specific DWI sequences is limited in this phantom study and without apparent clinical relevance. However, prospective multicenter studies of prostate cancer patients are needed for further investigation.
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Affiliation(s)
- Jakob M Møller
- Dep. of Radiology, Copenhagen University Hospital, Herlev-Gentofte, Denmark, Borgmester Ib Juuls vej 17, DK-2730 Herlev, Denmark.
| | - Lars Boesen
- Dep. of Urology, Copenhagen University Hospital, Herlev-Gentofte, Denmark
| | - Adam Espe Hansen
- Dep of radiology, Copenhagen University Hospital, Rigshospitalet and dep. of clinical medicine Copenhagen University, Copenhagen, Denmark
| | | | - Vibeke Løgager
- Dep. of Radiology, Copenhagen University Hospital, Herlev-Gentofte, Denmark
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Gibbons M, Simko JP, Carroll PR, Noworolski SM. Prostate cancer lesion detection, volume quantification and high-grade cancer differentiation using cancer risk maps derived from multiparametric MRI with histopathology as the reference standard. Magn Reson Imaging 2023; 99:48-57. [PMID: 36641104 PMCID: PMC11229728 DOI: 10.1016/j.mri.2023.01.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 01/08/2023] [Indexed: 01/13/2023]
Abstract
Multi-parametric MRI (mpMRI) has proven itself a clinically useful tool to assess prostate cancer (PCa). Our objective was to generate PCa risk maps to quantify the volume and location of both all PCa and high grade (Gleason grade group ≥ 3) PCa. Such capabilities would aid physicians and patients in treatment decisions, targeting biopsy, and planning focal therapy. A cohort of men with biopsy proven prostate cancer and pre-prostatectomy mpMRI were studied. PCa and benign ROIs (1524) were identified on mpMRI and histopathology with histopathology serving as the reference standard. Logistic regression models were created to differentiate PCa from benign tissues. The MRI images were registered to ensure correct overlay. The cancer models were applied to each image voxel within prostates to create probability maps of cancer and of high-grade cancer. Use of an optimum probability threshold quantified PCa volume for all lesions >0.1 cc. Accuracies were calculated using area under the curve (AUC) for the receiver operating characteristic (ROC). The PCa models utilized apparent diffusion coefficient (ADC), T2 weighted (T2W), dynamic contrast-enhanced MRI (DCE MRI) enhancement slope, and DCE MRI washout as the statistically significant MRI scans. Application of the PCa maps method provided total PCa volume and individual lesion volumes. The AUCs derived from lesion analysis were 0.91 for all PCa and 0.73 for high-grade PCa. At the optimum threshold, the PCa maps detected 135 / 150 (90%) histopathological lesions >0.1 cc. This study showed the feasibility of cancer risk maps, created from pre-prostatectomy, mpMR images validated with histopathology, to detect PCa lesions >0.1 cc. The method quantified the volume of cancer within the prostate. Method improvements were identified by determining root causes for over and underestimation of cancer volumes. The maps have the potential for improved non-invasive capability in quantitative detection, localization, volume estimation, and MRI characterization of PCa.
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Affiliation(s)
- Matthew Gibbons
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States.
| | - Jeffry P Simko
- Department of Urology, University of California, San Francisco, CA, United States; Department of Pathology, University of California, San Francisco, CA, United States.
| | - Peter R Carroll
- Department of Urology, University of California, San Francisco, CA, United States.
| | - Susan M Noworolski
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States.
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Liu K, Li P, Otikovs M, Ning X, Xia L, Wang X, Yang L, Pan F, Zhang Z, Wu G, Xie H, Bao Q, Zhou X, Liu C. Mutually communicated model based on multi-parametric MRI for automated segmentation and classification of prostate cancer. Med Phys 2023. [PMID: 36905102 DOI: 10.1002/mp.16343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 02/23/2023] [Accepted: 02/24/2023] [Indexed: 03/12/2023] Open
Abstract
BACKGROUND Multiparametric magnetic resonance imaging (mp-MRI) is introduced and established as a noninvasive alternative for prostate cancer (PCa) detection and characterization. PURPOSE To develop and evaluate a mutually communicated deep learning segmentation and classification network (MC-DSCN) based on mp-MRI for prostate segmentation and PCa diagnosis. METHODS The proposed MC-DSCN can transfer mutual information between segmentation and classification components and facilitate each other in a bootstrapping way. For classification task, the MC-DSCN can transfer the masks produced by the coarse segmentation component to the classification component to exclude irrelevant regions and facilitate classification. For segmentation task, this model can transfer the high-quality localization information learned by the classification component to the fine segmentation component to mitigate the impact of inaccurate localization on segmentation results. Consecutive MRI exams of patients were retrospectively collected from two medical centers (referred to as center A and B). Two experienced radiologists segmented the prostate regions, and the ground truth of the classification refers to the prostate biopsy results. MC-DSCN was designed, trained, and validated using different combinations of distinct MRI sequences as input (e.g., T2-weighted and apparent diffusion coefficient) and the effect of different architectures on the network's performance was tested and discussed. Data from center A were used for training, validation, and internal testing, while another center's data were used for external testing. The statistical analysis is performed to evaluate the performance of the MC-DSCN. The DeLong test and paired t-test were used to assess the performance of classification and segmentation, respectively. RESULTS In total, 134 patients were included. The proposed MC-DSCN outperforms the networks that were designed solely for segmentation or classification. Regarding the segmentation task, the classification localization information helped to improve the IOU in center A: from 84.5% to 87.8% (p < 0.01) and in center B: from 83.8% to 87.1% (p < 0.01), while the area under curve (AUC) of PCa classification was improved in center A: from 0.946 to 0.991 (p < 0.02) and in center B: from 0.926 to 0.955 (p < 0.01) as a result of the additional information provided by the prostate segmentation. CONCLUSION The proposed architecture could effectively transfer mutual information between segmentation and classification components and facilitate each other in a bootstrapping way, thus outperforming the networks designed to perform only one task.
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Affiliation(s)
- Kewen Liu
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China.,School of Information Engineering, Wuhan University of Technology, Wuhan, P.R. China
| | - Piqiang Li
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China.,School of Information Engineering, Wuhan University of Technology, Wuhan, P.R. China
| | - Martins Otikovs
- Weizmann Institute of Science, Department of Chemical and Biological Physics, Rehovot, Israel
| | - Xinzhou Ning
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China
| | - Liyang Xia
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China.,School of Information Engineering, Wuhan University of Technology, Wuhan, P.R. China
| | - Xiangyu Wang
- First Affiliated Hospital of Shenzhen University, Shenzhen, P.R. China
| | - Lian Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Feng Pan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, P.R. China
| | - Zhi Zhang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China
| | - Guangyao Wu
- Shenzhen University General Hospital, Shenzhen, P.R. China
| | - Han Xie
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China
| | - Qingjia Bao
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China
| | - Xin Zhou
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China.,University of Chinese Academy of Sciences, Beijing, P.R. China.,Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology-Optics Valley Laboratory, Wuhan, P.R. China
| | - Chaoyang Liu
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, P.R. China.,University of Chinese Academy of Sciences, Beijing, P.R. China.,Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology-Optics Valley Laboratory, Wuhan, P.R. China
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Joy A, Nagarajan R, Saucedo A, Iqbal Z, Sarma MK, Wilson N, Felker E, Reiter RE, Raman SS, Thomas MA. Dictionary learning compressed sensing reconstruction: pilot validation of accelerated echo planar J-resolved spectroscopic imaging in prostate cancer. MAGNETIC RESONANCE MATERIALS IN PHYSICS, BIOLOGY AND MEDICINE 2022; 35:667-682. [PMID: 35869359 PMCID: PMC9363346 DOI: 10.1007/s10334-022-01029-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 07/01/2022] [Accepted: 07/03/2022] [Indexed: 11/28/2022]
Abstract
Objectives This study aimed at developing dictionary learning (DL) based compressed sensing (CS) reconstruction for randomly undersampled five-dimensional (5D) MR Spectroscopic Imaging (3D spatial + 2D spectral) data acquired in prostate cancer patients and healthy controls, and test its feasibility at 8x and 12x undersampling factors. Materials and methods Prospectively undersampled 5D echo-planar J-resolved spectroscopic imaging (EP-JRESI) data were acquired in nine prostate cancer (PCa) patients and three healthy males. The 5D EP-JRESI data were reconstructed using DL and compared with gradient sparsity-based Total Variation (TV) and Perona-Malik (PM) methods. A hybrid reconstruction technique, Dictionary Learning-Total Variation (DLTV), was also designed to further improve the quality of reconstructed spectra. Results The CS reconstruction of prospectively undersampled (8x and 12x) 5D EP-JRESI data acquired in prostate cancer and healthy subjects were performed using DL, DLTV, TV and PM. It is evident that the hybrid DLTV method can unambiguously resolve 2D J-resolved peaks including myo-inositol, citrate, creatine, spermine and choline. Conclusion Improved reconstruction of the accelerated 5D EP-JRESI data was observed using the hybrid DLTV. Accelerated acquisition of in vivo 5D data with as low as 8.33% samples (12x) corresponds to a total scan time of 14 min as opposed to a fully sampled scan that needs a total duration of 2.4 h (TR = 1.2 s, 32 \documentclass[12pt]{minimal}
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