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Mir N, Fransen SJ, Wolterink JM, Fütterer JJ, Simonis FFJ. Recent Developments in Speeding up Prostate MRI. J Magn Reson Imaging 2024; 60:813-826. [PMID: 37982353 DOI: 10.1002/jmri.29108] [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: 07/24/2023] [Revised: 10/13/2023] [Accepted: 10/17/2023] [Indexed: 11/21/2023] Open
Abstract
The increasing incidence of prostate cancer cases worldwide has led to a tremendous demand for multiparametric MRI (mpMRI). In order to relieve the pressure on healthcare, reducing mpMRI scan time is necessary. This review focuses on recent techniques proposed for faster mpMRI acquisition, specifically shortening T2W and DWI sequences while adhering to the PI-RADS (Prostate Imaging Reporting and Data System) guidelines. Speeding up techniques in the reviewed studies rely on more efficient sampling of data, ranging from the acquisition of fewer averages or b-values to adjustment of the pulse sequence. Novel acquisition methods based on undersampling techniques are often followed by suitable reconstruction methods typically incorporating synthetic priori information. These reconstruction methods often use artificial intelligence for various tasks such as denoising, artifact correction, improvement of image quality, and in the case of DWI, for the generation of synthetic high b-value images or apparent diffusion coefficient maps. Reduction of mpMRI scan time is possible, but it is crucial to maintain diagnostic quality, confirmed through radiological evaluation, to integrate the proposed methods into the standard mpMRI protocol. Additionally, before clinical integration, prospective studies are recommended to validate undersampling techniques to avoid potentially inaccurate results demonstrated by retrospective analysis. This review provides an overview of recently proposed techniques, discussing their implementation, advantages, disadvantages, and diagnostic performance according to PI-RADS guidelines compared to conventional methods. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Nida Mir
- Magnetic Detection and Imaging, Technical Medical Centre, University of Twente, Enschede, Netherlands
| | - Stefan J Fransen
- Department of Radiology, University Medical Center Groningen, Groningen, Netherlands
| | - Jelmer M Wolterink
- Department of Applied Mathematics, Technical Medical Centre, University of Twente, Enschede, Netherlands
| | - Jurgen J Fütterer
- Robotics and Mechatronics, Technical Medical Centre, University of Twente, Enschede, Netherlands
- Minimally Invasive Image-Guided Interventions Center, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands
| | - Frank F J Simonis
- Magnetic Detection and Imaging, Technical Medical Centre, University of Twente, Enschede, Netherlands
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Li S, Fan Z, Guo J, Li D, Chen Z, Zhang X, Wang Y, Li Y, Yang G, Wang X. Compressed sensing 3D T2WI radiomics model: improving diagnostic performance in muscle invasion of bladder cancer. BMC Med Imaging 2024; 24:148. [PMID: 38886638 PMCID: PMC11181529 DOI: 10.1186/s12880-024-01318-0] [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: 02/20/2024] [Accepted: 06/03/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Preoperative discrimination between non-muscle-invasive bladder cancer (NMIBC) and the muscle invasive bladder cancer (MIBC) is a determinant of management. The purpose of this research is to employ radiomics to evaluate the diagnostic value in determining muscle invasiveness of compressed sensing (CS) accelerated 3D T2-weighted-SPACE sequence with high resolution and short acquisition time. METHODS This prospective study involved 108 participants who underwent preoperative 3D-CS-T2-weighted-SPACE, 3D-T2-weighted-SPACE and T2-weighted sequences. The cohort was divided into training and validation cohorts in a 7:3 ratio. In the training cohort, a Rad-score was constructed based on radiomic features selected by intraclass correlation coefficients, pearson correlation coefficient and least absolute shrinkage and selection operator . Multivariate logistic regression was used to develop a nomogram combined radiomics and clinical indices. In the validation cohort, the performances of the models were evaluated by ROC, calibration, and decision curves. RESULTS In the validation cohort, the area under ROC curve of 3D-CS-T2-weighted-SPACE, 3D-T2-weighted-SPACE and T2-weighted models were 0.87(95% confidence interval (CI):0.73-1.00), 0.79(95%CI:0.63-0.96) and 0.77(95%CI:0.60-0.93), respectively. The differences in signal-to-noise ratio and contrast-to-noise ratio between 3D-CS-T2-weighted-SPACE and 3D-T2-weighted-SPACE sequences were not statistically significant(p > 0.05). While the clinical model composed of three clinical indices was 0.74(95%CI:0.55-0.94) and the radiomics-clinical nomogram model was 0.88(95%CI:0.75-1.00). The calibration curves confirmed high goodness of fit, and the decision curve also showed that the radiomics model and combined nomogram model yielded higher net benefits than the clinical model. CONCLUSION The radiomics model based on compressed sensing 3D T2WI sequence, which was acquired within a shorter acquisition time, showed superior diagnostic efficacy in muscle invasion of bladder cancer. Additionally, the nomogram model could enhance the diagnostic performance.
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Affiliation(s)
- Shuo Li
- Department of Radiology, The First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Taiyuan, 030001, Shanxi Province, P.R. China
| | - Zhichang Fan
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, P.R. China
| | - Junting Guo
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, P.R. China
| | - Ding Li
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, P.R. China
| | - Zeke Chen
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, P.R. China
| | - Xiaoyue Zhang
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, P.R. China
| | - Yongfang Wang
- Department of Radiology, The First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Taiyuan, 030001, Shanxi Province, P.R. China
| | - Yan Li
- Department of Radiology, The First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Taiyuan, 030001, Shanxi Province, P.R. China
| | - Guoqiang Yang
- Department of Radiology, The First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Taiyuan, 030001, Shanxi Province, P.R. China
| | - Xiaochun Wang
- Department of Radiology, The First Hospital of Shanxi Medical University, No.85 Jiefang South Road, Taiyuan, 030001, Shanxi Province, P.R. China.
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Woernle A, Englman C, Dickinson L, Kirkham A, Punwani S, Haider A, Freeman A, Kasivisivanathan V, Emberton M, Hines J, Moore CM, Allen C, Giganti F. Picture Perfect: The Status of Image Quality in Prostate MRI. J Magn Reson Imaging 2024; 59:1930-1952. [PMID: 37804007 DOI: 10.1002/jmri.29025] [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: 07/01/2023] [Revised: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 10/08/2023] Open
Abstract
Magnetic resonance imaging is the gold standard imaging modality for the diagnosis of prostate cancer (PCa). Image quality is a fundamental prerequisite for the ability to detect clinically significant disease. In this critical review, we separate the issue of image quality into quality improvement and quality assessment. Beginning with the evolution of technical recommendations for scan acquisition, we investigate the role of patient preparation, scanner factors, and more advanced sequences, including those featuring Artificial Intelligence (AI), in determining image quality. As means of quality appraisal, the published literature on scoring systems (including the Prostate Imaging Quality score), is evaluated. Finally, the application of AI and teaching courses as ways to facilitate quality assessment are discussed, encouraging the implementation of future image quality initiatives along the PCa diagnostic and monitoring pathway. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Alexandre Woernle
- Faculty of Medical Sciences, University College London, London, UK
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
| | - Cameron Englman
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
- Division of Surgery & Interventional Science, University College London, London, UK
| | - Louise Dickinson
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
| | - Alex Kirkham
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
| | - Shonit Punwani
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
- Centre for Medical Imaging, University College London, London, UK
| | - Aiman Haider
- Department of Pathology, University College London Hospital NHS Foundation Trust, London, UK
| | - Alex Freeman
- Department of Pathology, University College London Hospital NHS Foundation Trust, London, UK
| | - Veeru Kasivisivanathan
- Division of Surgery & Interventional Science, University College London, London, UK
- Department of Urology, University College London Hospital NHS Foundation Trust, London, UK
| | - Mark Emberton
- Division of Surgery & Interventional Science, University College London, London, UK
- Department of Urology, University College London Hospital NHS Foundation Trust, London, UK
| | - John Hines
- Faculty of Medical Sciences, University College London, London, UK
- Department of Urology, University College London Hospital NHS Foundation Trust, London, UK
- North East London Cancer Alliance & North Central London Cancer Alliance Urology, London, UK
| | - Caroline M Moore
- Division of Surgery & Interventional Science, University College London, London, UK
- Department of Urology, University College London Hospital NHS Foundation Trust, London, UK
| | - Clare Allen
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
| | - Francesco Giganti
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
- Division of Surgery & Interventional Science, University College London, London, UK
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Brillat-Savarin N, Wu C, Aupin L, Thoumin C, Hamzaoui D, Renard-Penna R. 3.0 T prostate MRI: Visual assessment of 2D and 3D T2-weighted imaging sequences using PI-QUAL score. Eur J Radiol 2023; 166:110974. [PMID: 37453273 DOI: 10.1016/j.ejrad.2023.110974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 06/30/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023]
Abstract
PURPOSE Comparing 2D and 3D T2 weighted sequences in terms of image quality in 3.0 T MRI with readers of varied experiences, using PI-QUAL inspired criteria. METHODS 91 male patients with suspected prostate cancer (PCa) underwent diagnostic prostate MRI on a 3.0 T MR system using a 32-channel phased-array torso coil before prostate biopsy. MRI protocol included 3D T2w images, axial 2D T2w images, axial diffusion-weighted images (DWI) with the corresponding ADC apparent diffusion coefficient maps, and axial dynamic contrast enhanced images. 3D T2w and 2D T2w imaging were compared by 4 radiologists using a Likert scale for image quality (overall anatomy, delineation of capsule, seminal vesicles, ejaculatory ducts, sphincter muscle, artifacts), tumor delimitation and conspicuity. RESULTS No significant differences in terms of overall quality between 3D and 2D T2w images were found. However 2D T2w demonstrated higher rating than 3D T2w images as for the image quality of the external capsule, sphincter muscle and ejaculatory ducts delineation (p < 0.05). CONCLUSION 3D T2w sequence can't replace 2D T2w sequence, despite good quality images but it remains more prone to artifacts. Quality of 2D T2w sequences was substantially superior to 3D sequences for delineation of key structures as external capsule, sphincter muscle. The use of PI-QUAL criteria allows reproducible analysis of the quality of T2 weighted images.
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Affiliation(s)
- Nina Brillat-Savarin
- Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, 47 boulevard de l'Hôpital, 75013 Paris, France.
| | - Carine Wu
- Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, 47 boulevard de l'Hôpital, 75013 Paris, France.
| | - Laurène Aupin
- Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, 47 boulevard de l'Hôpital, 75013 Paris, France.
| | - Camille Thoumin
- Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, 47 boulevard de l'Hôpital, 75013 Paris, France.
| | - Dimitri Hamzaoui
- Inria Sophia Antipolis, Epione Research Project, 2004 route des Lucioles, 06902 Sophia Antipolis, France.
| | - Raphaële Renard-Penna
- Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, 47 boulevard de l'Hôpital, 75013 Paris, France; Sorbonne University, Paris, France.
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Michalet M, Riou O, Azria D, Decoene C, Crop F. News in magnetic resonance imaging use for radiation oncology. Cancer Radiother 2022; 26:784-788. [PMID: 36031496 DOI: 10.1016/j.canrad.2022.06.028] [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: 06/21/2022] [Revised: 06/28/2022] [Accepted: 06/30/2022] [Indexed: 11/29/2022]
Abstract
The purpose of this article is to give a summary of the progress of magnetic resonance imaging (MRI) in radiotherapy. MRI is an important imaging modality for treatment planning in radiotherapy. However, the registration step with the simulation scanner can be a source of errors, motivating the implementation of all-MRI simulation methods and new accelerators coupled with on-board MRI. First, practical MRI imaging for radiotherapy is detailed, but also the importance of a coherent imaging workflow incorporating all imaging modalities. Second, future evolutions and research domains such as quantitative imaging biomarkers, MRI-only pseudo computed tomography and radiomics are discussed. Finally, the application of MRI during radiotherapy treatment is reviewed: the use of MR-linear accelerators. MRI is increasingly integrated into radiotherapy. Advances in diagnostic imaging can thus benefit radiotherapy, but specific radiotherapy constraints lead to additional challenges and require close collaboration between radiologists, radiation oncologists, technologists and physicists. The integration of quantitative imaging biomarkers in the radiotherapy process will result in mutual benefit for diagnostic imaging and radiotherapy. MRI-guided radiotherapy has already been used for several years in clinical routine. Abdominopelvic neoplasias (pancreas, liver, prostate) are the preferred locations for treatment because of their favourable contrast in MRI, their movement during irradiation and their proximity to organs at risk of radiation exposure, making the tracking and daily adaptation of the plan essential. MRI has emerged as an increasingly necessary imaging modality for radiotherapy planning. Inclusion of patients in clinical trials evaluating new MRI-guided radiotherapy techniques and associated quantitative imaging biomarkers will be necessary to assess the benefits.
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Affiliation(s)
- M Michalet
- Institut du cancer de Montpellier, Fédération universitaire d'oncologie-radiothérapie d'Occitanie Méditerranée (Forom), Inserm U1194 IRCM, 208, avenue des Apothicaires, 34298 Montpellier, France.
| | - O Riou
- Institut du cancer de Montpellier, Fédération universitaire d'oncologie-radiothérapie d'Occitanie Méditerranée (Forom), Inserm U1194 IRCM, 208, avenue des Apothicaires, 34298 Montpellier, France
| | - D Azria
- Institut du cancer de Montpellier, Fédération universitaire d'oncologie-radiothérapie d'Occitanie Méditerranée (Forom), Inserm U1194 IRCM, 208, avenue des Apothicaires, 34298 Montpellier, France
| | - C Decoene
- Medical physics, centre Oscar-Lambret, 3, rue Frédéric-Combemale, 59000 Lille, France
| | - F Crop
- Medical physics, centre Oscar-Lambret, 3, rue Frédéric-Combemale, 59000 Lille, France
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6
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Hamzaoui D, Montagne S, Renard-Penna R, Ayache N, Delingette H. Automatic zonal segmentation of the prostate from 2D and 3D T2-weighted MRI and evaluation for clinical use. J Med Imaging (Bellingham) 2022; 9:024001. [PMID: 35300345 PMCID: PMC8920492 DOI: 10.1117/1.jmi.9.2.024001] [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: 09/24/2021] [Accepted: 02/23/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose: An accurate zonal segmentation of the prostate is required for prostate cancer (PCa) management with MRI. Approach: The aim of this work is to present UFNet, a deep learning-based method for automatic zonal segmentation of the prostate from T2-weighted (T2w) MRI. It takes into account the image anisotropy, includes both spatial and channelwise attention mechanisms and uses loss functions to enforce prostate partition. The method was applied on a private multicentric three-dimensional T2w MRI dataset and on the public two-dimensional T2w MRI dataset ProstateX. To assess the model performance, the structures segmented by the algorithm on the private dataset were compared with those obtained by seven radiologists of various experience levels. Results: On the private dataset, we obtained a Dice score (DSC) of 93.90 ± 2.85 for the whole gland (WG), 91.00 ± 4.34 for the transition zone (TZ), and 79.08 ± 7.08 for the peripheral zone (PZ). Results were significantly better than other compared networks' ( p - value < 0.05 ). On ProstateX, we obtained a DSC of 90.90 ± 2.94 for WG, 86.84 ± 4.33 for TZ, and 78.40 ± 7.31 for PZ. These results are similar to state-of-the art results and, on the private dataset, are coherent with those obtained by radiologists. Zonal locations and sectorial positions of lesions annotated by radiologists were also preserved. Conclusions: Deep learning-based methods can provide an accurate zonal segmentation of the prostate leading to a consistent zonal location and sectorial position of lesions, and therefore can be used as a helping tool for PCa diagnosis.
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Affiliation(s)
- Dimitri Hamzaoui
- Université Côte d'Azur, Inria, Epione Project-Team, Sophia Antipolis, Valbonne, France
| | - Sarah Montagne
- Sorbonne Université, Radiology Department, CHU La Pitié Salpétrière/Tenon, Paris, France
| | - Raphaële Renard-Penna
- Sorbonne Université, Radiology Department, CHU La Pitié Salpétrière/Tenon, Paris, France
| | - Nicholas Ayache
- Université Côte d'Azur, Inria, Epione Project-Team, Sophia Antipolis, Valbonne, France
| | - Hervé Delingette
- Université Côte d'Azur, Inria, Epione Project-Team, Sophia Antipolis, Valbonne, France
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7
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Sanders JW, Venkatesan AM, Levitt CA, Bathala T, Kudchadker RJ, Tang C, Bruno TL, Starks C, Santiago E, Wells M, Weaver CP, Ma J, Frank SJ. Fully Balanced SSFP Without an Endorectal Coil for Postimplant QA of MRI-Assisted Radiosurgery (MARS) of Prostate Cancer: A Prospective Study. Int J Radiat Oncol Biol Phys 2021; 109:614-625. [PMID: 32980498 DOI: 10.1016/j.ijrobp.2020.09.040] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 07/29/2020] [Accepted: 09/21/2020] [Indexed: 01/23/2023]
Abstract
PURPOSE To investigate fully balanced steady-state free precession (bSSFP) with optimized acquisition protocols for magnetic resonance imaging (MRI)-based postimplant quality assessment of low-dose-rate (LDR) prostate brachytherapy without an endorectal coil (ERC). METHODS AND MATERIALS Seventeen patients at a major academic cancer center who underwent MRI-assisted radiosurgery (MARS) LDR prostate cancer brachytherapy were imaged with moderate, high, or very high spatial resolution fully bSSFP MRIs without using an ERC. Between 1 and 3 signal averages (NEX) were acquired with acceleration factors (R) between 1 and 2, with the goal of keeping scan times between 4 and 6 minutes. Acquisitions with R >1 were reconstructed with parallel imaging and compressed sensing (PICS) algorithms. Radioactive seeds were identified by 3 medical dosimetrists. Additionally, some of the MRI techniques were implemented and tested at a community hospital; 3 patients underwent MARS LDR prostate brachytherapy and were imaged without an ERC. RESULTS Increasing the in-plane spatial resolution mitigated partial volume artifacts and improved overall seed and seed marker visualization at the expense of reduced signal-to-noise ratio (SNR). The reduced SNR as a result of imaging at higher spatial resolution and without an ERC was partially compensated for by the multi-NEX acquisitions enabled by PICS. Resultant image quality was superior to the current clinical standard. All 3 dosimetrists achieved near-perfect precision and recall for seed identification in the 17 patients. The 3 postimplant MRIs acquired at the community hospital were sufficient to identify 208 out of 211 seeds implanted without reference to computed tomography (CT). CONCLUSIONS Acquiring postimplant prostate brachytherapy MRI without an ERC has several advantages including better patient tolerance, lower costs, higher clinical throughput, and widespread access to precision LDR prostate brachytherapy. This prospective study confirms that the use of an ERC can be circumvented with fully bSSFP and advanced MRI scan techniques in a major academic cancer center and community hospital, potentially enabling postimplant assessment of MARS LDR prostate brachytherapy without CT.
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Affiliation(s)
- Jeremiah W Sanders
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas; Medical Physics Graduate Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas.
| | | | - Chad A Levitt
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | - Rajat J Kudchadker
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Chad Tang
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Teresa L Bruno
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Christine Starks
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Edwin Santiago
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Michelle Wells
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Carl P Weaver
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jingfei Ma
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas; Medical Physics Graduate Program, MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas
| | - Steven J Frank
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
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