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Coelho FMA, Baroni RH. Strategies for improving image quality in prostate MRI. Abdom Radiol (NY) 2024:10.1007/s00261-024-04396-4. [PMID: 38940911 DOI: 10.1007/s00261-024-04396-4] [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: 03/31/2024] [Revised: 05/15/2024] [Accepted: 05/17/2024] [Indexed: 06/29/2024]
Abstract
Prostate magnetic resonance imaging (MRI) stands as the cornerstone in diagnosing prostate cancer (PCa), offering superior detection capabilities while minimizing unnecessary biopsies. Despite its critical role, global disparities in MRI diagnostic performance persist, stemming from variations in image quality and radiologist expertise. This manuscript reviews the challenges and strategies for enhancing image quality in prostate MRI, spanning patient preparation, MRI unit optimization, and radiology team engagement. Quality assurance (QA) and quality control (QC) processes are pivotal, emphasizing standardized protocols, meticulous patient evaluation, MRI unit workflow, and radiology team performance. Additionally, artificial intelligence (AI) advancements offer promising avenues for improving image quality and reducing acquisition times. The Prostate-Imaging Quality (PI-QUAL) scoring system emerges as a valuable tool for assessing MRI image quality. A comprehensive approach addressing technical, procedural, and interpretative aspects is essential to ensure consistent and reliable prostate MRI outcomes.
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Affiliation(s)
| | - Ronaldo Hueb Baroni
- Department of Radiology, Hospital Israelita Albert Einstein, 627 Albert Einstein Ave., Sao Paulo, SP, 05652-900, Brazil.
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2
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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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3
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Al-Hayali A, Komeili A, Azad A, Sathiadoss P, Schieda N, Ukwatta E. Machine learning based prediction of image quality in prostate MRI using rapid localizer images. J Med Imaging (Bellingham) 2024; 11:026001. [PMID: 38435711 PMCID: PMC10905647 DOI: 10.1117/1.jmi.11.2.026001] [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: 01/13/2023] [Revised: 10/17/2023] [Accepted: 01/29/2024] [Indexed: 03/05/2024] Open
Abstract
Purpose Diagnostic performance of prostate MRI depends on high-quality imaging. Prostate MRI quality is inversely proportional to the amount of rectal gas and distention. Early detection of poor-quality MRI may enable intervention to remove gas or exam rescheduling, saving time. We developed a machine learning based quality prediction of yet-to-be acquired MRI images solely based on MRI rapid localizer sequence, which can be acquired in a few seconds. Approach The dataset consists of 213 (147 for training and 64 for testing) prostate sagittal T2-weighted (T2W) MRI localizer images and rectal content, manually labeled by an expert radiologist. Each MRI localizer contains seven two-dimensional (2D) slices of the patient, accompanied by manual segmentations of rectum for each slice. Cascaded and end-to-end deep learning models were used to predict the quality of yet-to-be T2W, DWI, and apparent diffusion coefficient (ADC) MRI images. Predictions were compared to quality scores determined by the experts using area under the receiver operator characteristic curve and intra-class correlation coefficient. Results In the test set of 64 patients, optimal versus suboptimal exams occurred in 95.3% (61/64) versus 4.7% (3/64) for T2W, 90.6% (58/64) versus 9.4% (6/64) for DWI, and 89.1% (57/64) versus 10.9% (7/64) for ADC. The best performing segmentation model was 2D U-Net with ResNet-34 encoder and ImageNet weights. The best performing classifier was the radiomics based classifier. Conclusions A radiomics based classifier applied to localizer images achieves accurate diagnosis of subsequent image quality for T2W, DWI, and ADC prostate MRI sequences.
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Affiliation(s)
- Abdullah Al-Hayali
- University of Guelph, School of Engineering, Guelph Imaging AI Lab, Guelph, Ontario, Canada
| | - Amin Komeili
- University of Calgary, Department of Biomedical Engineering, Calgary, Alberta, Canada
| | - Azar Azad
- A.I. Vali Inc., Toronto, Ontario, Canada
| | - Paul Sathiadoss
- University of Ottawa, Department of Radiology, Ottawa, Ontario, Canada
| | - Nicola Schieda
- University of Ottawa, Department of Radiology, Ottawa, Ontario, Canada
| | - Eranga Ukwatta
- University of Guelph, School of Engineering, Guelph Imaging AI Lab, Guelph, Ontario, Canada
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4
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Kovacs B, Netzer N, Baumgartner M, Schrader A, Isensee F, Weißer C, Wolf I, Görtz M, Jaeger PF, Schütz V, Floca R, Gnirs R, Stenzinger A, Hohenfellner M, Schlemmer HP, Bonekamp D, Maier-Hein KH. Addressing image misalignments in multi-parametric prostate MRI for enhanced computer-aided diagnosis of prostate cancer. Sci Rep 2023; 13:19805. [PMID: 37957250 PMCID: PMC10643562 DOI: 10.1038/s41598-023-46747-z] [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: 10/18/2022] [Accepted: 11/04/2023] [Indexed: 11/15/2023] Open
Abstract
Prostate cancer (PCa) diagnosis on multi-parametric magnetic resonance images (MRI) requires radiologists with a high level of expertise. Misalignments between the MRI sequences can be caused by patient movement, elastic soft-tissue deformations, and imaging artifacts. They further increase the complexity of the task prompting radiologists to interpret the images. Recently, computer-aided diagnosis (CAD) tools have demonstrated potential for PCa diagnosis typically relying on complex co-registration of the input modalities. However, there is no consensus among research groups on whether CAD systems profit from using registration. Furthermore, alternative strategies to handle multi-modal misalignments have not been explored so far. Our study introduces and compares different strategies to cope with image misalignments and evaluates them regarding to their direct effect on diagnostic accuracy of PCa. In addition to established registration algorithms, we propose 'misalignment augmentation' as a concept to increase CAD robustness. As the results demonstrate, misalignment augmentations can not only compensate for a complete lack of registration, but if used in conjunction with registration, also improve the overall performance on an independent test set.
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Affiliation(s)
- Balint Kovacs
- Division of Medical Image Computing, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany.
- Division of Radiology, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.
- Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany.
| | - Nils Netzer
- Division of Radiology, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
- Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Michael Baumgartner
- Division of Medical Image Computing, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
- Helmholtz Imaging, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
| | - Adrian Schrader
- Division of Radiology, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
- Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Fabian Isensee
- Division of Medical Image Computing, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
- Helmholtz Imaging, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
| | - Cedric Weißer
- Division of Radiology, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
- Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Ivo Wolf
- Mannheim University of Applied Sciences, Mannheim, Germany
| | - Magdalena Görtz
- Junior Clinical Cooperation Unit 'Multiparametric Methods for Early Detection of Prostate Cancer', German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
- Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Paul F Jaeger
- Helmholtz Imaging, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
- Interactive Machine Learning Group, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
| | - Victoria Schütz
- Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Ralf Floca
- Division of Medical Image Computing, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
| | - Regula Gnirs
- Division of Radiology, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
| | - Albrecht Stenzinger
- Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Markus Hohenfellner
- Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Heinz-Peter Schlemmer
- Division of Radiology, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
- German Cancer Consortium (DKTK), DKFZ, Core Center Heidelberg, Heidelberg, Germany
| | - David Bonekamp
- Division of Radiology, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
- Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
- German Cancer Consortium (DKTK), DKFZ, Core Center Heidelberg, Heidelberg, Germany
| | - Klaus H Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 223, 69120, Heidelberg, Germany
- Helmholtz Imaging, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
- German Cancer Consortium (DKTK), DKFZ, Core Center Heidelberg, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
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5
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Netzer N, Eith C, Bethge O, Hielscher T, Schwab C, Stenzinger A, Gnirs R, Schlemmer HP, Maier-Hein KH, Schimmöller L, Bonekamp D. Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability. Eur Radiol 2023; 33:7463-7476. [PMID: 37507610 PMCID: PMC10598076 DOI: 10.1007/s00330-023-09882-9] [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: 03/24/2023] [Revised: 04/24/2023] [Accepted: 04/27/2023] [Indexed: 07/30/2023]
Abstract
OBJECTIVES To evaluate a fully automatic deep learning system to detect and segment clinically significant prostate cancer (csPCa) on same-vendor prostate MRI from two different institutions not contributing to training of the system. MATERIALS AND METHODS In this retrospective study, a previously bi-institutionally validated deep learning system (UNETM) was applied to bi-parametric prostate MRI data from one external institution (A), a PI-RADS distribution-matched internal cohort (B), and a csPCa stratified subset of single-institution external public challenge data (C). csPCa was defined as ISUP Grade Group ≥ 2 determined from combined targeted and extended systematic MRI/transrectal US-fusion biopsy. Performance of UNETM was evaluated by comparing ROC AUC and specificity at typical PI-RADS sensitivity levels. Lesion-level analysis between UNETM segmentations and radiologist-delineated segmentations was performed using Dice coefficient, free-response operating characteristic (FROC), and weighted alternative (waFROC). The influence of using different diffusion sequences was analyzed in cohort A. RESULTS In 250/250/140 exams in cohorts A/B/C, differences in ROC AUC were insignificant with 0.80 (95% CI: 0.74-0.85)/0.87 (95% CI: 0.83-0.92)/0.82 (95% CI: 0.75-0.89). At sensitivities of 95% and 90%, UNETM achieved specificity of 30%/50% in A, 44%/71% in B, and 43%/49% in C, respectively. Dice coefficient of UNETM and radiologist-delineated lesions was 0.36 in A and 0.49 in B. The waFROC AUC was 0.67 (95% CI: 0.60-0.83) in A and 0.7 (95% CI: 0.64-0.78) in B. UNETM performed marginally better on readout-segmented than on single-shot echo-planar-imaging. CONCLUSION For same-vendor examinations, deep learning provided comparable discrimination of csPCa and non-csPCa lesions and examinations between local and two independent external data sets, demonstrating the applicability of the system to institutions not participating in model training. CLINICAL RELEVANCE STATEMENT A previously bi-institutionally validated fully automatic deep learning system maintained acceptable exam-level diagnostic performance in two independent external data sets, indicating the potential of deploying AI models without retraining or fine-tuning, and corroborating evidence that AI models extract a substantial amount of transferable domain knowledge about MRI-based prostate cancer assessment. KEY POINTS • A previously bi-institutionally validated fully automatic deep learning system maintained acceptable exam-level diagnostic performance in two independent external data sets. • Lesion detection performance and segmentation congruence was similar on the institutional and an external data set, as measured by the weighted alternative FROC AUC and Dice coefficient. • Although the system generalized to two external institutions without re-training, achieving expected sensitivity and specificity levels using the deep learning system requires probability thresholds to be adjusted, underlining the importance of institution-specific calibration and quality control.
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Affiliation(s)
- Nils Netzer
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
- Heidelberg University Medical School, Heidelberg, Germany
| | - Carolin Eith
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
- Heidelberg University Medical School, Heidelberg, Germany
| | - Oliver Bethge
- Medical Faculty, Department of Diagnostic and Interventional Radiology, University Dusseldorf, D-40225, Dusseldorf, Germany
| | - Thomas Hielscher
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Constantin Schwab
- Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Albrecht Stenzinger
- Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Regula Gnirs
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
| | - Heinz-Peter Schlemmer
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany
| | - Klaus H Maier-Hein
- National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany
- Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Lars Schimmöller
- Medical Faculty, Department of Diagnostic and Interventional Radiology, University Dusseldorf, D-40225, Dusseldorf, Germany
| | - David Bonekamp
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
- Heidelberg University Medical School, Heidelberg, Germany.
- German Cancer Consortium (DKTK), Heidelberg, Germany.
- National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany.
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Coelho FMA, Amaral LTW, Mitsutake LKN, Mussi TC, Baroni RH. Quality assessment of prostate MRI by PI-QUAL score: Inter-reader agreement and impact on prostate cancer local staging at 3 Tesla. Eur J Radiol 2023; 165:110921. [PMID: 37336037 DOI: 10.1016/j.ejrad.2023.110921] [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: 03/08/2023] [Revised: 05/07/2023] [Accepted: 06/05/2023] [Indexed: 06/21/2023]
Abstract
PURPOSE To evaluate whether the Prostate Imaging Quality (PI-QUAL) score impacts prostate cancer (PCa) staging on MRI. The secondary goal was to test inter-reader agreement among radiologists experienced in prostate imaging. METHOD A retrospective, single-center study with patients who underwent 3 Tesla prostate MRI scans and were submitted to radical prostatectomy (RP) between January 2018 and November 2021 and were eligible for our study. Extraprostatic extension (EPE) data were collected from original MR reports (EPEm) and pathological reports of RP specimens (EPEp). Three expert prostate radiologists (ESUR/ESUI criteria R1, R2, R3) independently evaluated all MRI exams according to PI-QUAL score for image quality (1 to 5; 1: poor, 5: excellent), blinded to original imaging reports and clinical data. We studied the diagnostic performance of MRI using pooled data from PI-QUAL scores (≤3 vs. ≥4). We also performed univariate and multivariate analyses to assess the PI-QUAL score impact on local PCa staging. Cohen's K and Tau-b Kendall tests were used to assess the inter-reader agreement for PI-QUAL score, T2WI, DWI, and DCE. RESULTS Our final cohort included 146 patients, of which 27.4% presented EPE on pathology. We observed no impact of imaging quality on accuracy for EPE prediction: AUC of 0.750 (95% CI 0.26-1) for PI-QUAL ≤ 3 and 0.705 (95% CI 0.618-0.793) for PI-QUAL ≥ 4. The multivariate analysis demonstrated a correlation of EPEm (OR 3.25, p 0.001) and ISUP grade group (OR 1.89, p 0.012) to predict EPEp. The inter-reader agreement was moderate to substantial (0.539 for R1-R2, 0.522 for R2-R3, and 0.694 for R1-R3). CONCLUSION Our clinical impact evaluation showed no direct correlation between MRI quality by PI-QUAL score and accuracy in detecting EPE in patients undergoing RP. Additionally, we had moderate to a substantial inter-reader agreement for the PI-QUAL score.
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Affiliation(s)
| | - Lucas Tadashi Wada Amaral
- Department of Radiology, Hospital Israelita Albert Einstein. 627 Albert Einstein Ave. Sao Paulo, SP 05652-900, Brazil.
| | - Leonardo Kenji Nesi Mitsutake
- Department of Radiology, Hospital Israelita Albert Einstein. 627 Albert Einstein Ave. Sao Paulo, SP 05652-900, Brazil.
| | - Thais Caldara Mussi
- Department of Radiology, Hospital Israelita Albert Einstein. 627 Albert Einstein Ave. Sao Paulo, SP 05652-900, Brazil.
| | - Ronaldo Hueb Baroni
- Department of Radiology, Hospital Israelita Albert Einstein. 627 Albert Einstein Ave. Sao Paulo, SP 05652-900, Brazil.
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7
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Lin Y, Yilmaz EC, Belue MJ, Turkbey B. Prostate MRI and image Quality: It is time to take stock. Eur J Radiol 2023; 161:110757. [PMID: 36870241 PMCID: PMC10493032 DOI: 10.1016/j.ejrad.2023.110757] [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: 01/28/2023] [Revised: 02/15/2023] [Accepted: 02/21/2023] [Indexed: 03/06/2023]
Abstract
Multiparametric magnetic resonance imaging (mpMRI) plays a vital role in prostate cancer diagnosis and management. With the increase in use of mpMRI, obtaining the best possible quality images has become a priority. The Prostate Imaging Reporting and Data System (PI-RADS) was introduced to standardize and optimize patient preparation, scanning techniques, and interpretation. However, the quality of the MRI sequences depends not only on the hardware/software and scanning parameters, but also on patient-related factors. Common patient-related factors include bowel peristalsis, rectal distension, and patient motion. There is currently no consensus regarding the best approaches to address these issues and improve the quality of mpMRI. New evidence has been accrued since the release of PI-RADS, and this review aims to explore the key strategies which aim to improve prostate MRI quality, such as imaging techniques, patient preparation methods, the new Prostate Imaging Quality (PI-QUAL) criteria, and artificial intelligence on prostate MRI quality.
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Affiliation(s)
- Yue Lin
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Enis C Yilmaz
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Mason J Belue
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States.
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8
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Kim CK. [Prostate Imaging Reporting and Data System (PI-RADS) v 2.1: Overview and Critical Points]. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2023; 84:75-91. [PMID: 36818694 PMCID: PMC9935951 DOI: 10.3348/jksr.2022.0169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/15/2023] [Accepted: 01/20/2023] [Indexed: 02/09/2023]
Abstract
The technical parameters and imaging interpretation criteria of the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) using multiparametric MRI (mpMRI) are updated in PI-RADS v2.1. These changes have been an expected improvement for prostate cancer evaluation, although some issues remain unsolved, and new issues have been raised. In this review, a brief overview of PI-RADS v2.1 is and several critical points are discussed as follows: the need for more detailed protocols of mpMRI, lack of validation of the revised transition zone interpretation criteria, the need for clarification for the revised diffusion-weighted imaging and dynamic contrast-enhanced imaging criteria, anterior fibromuscular stroma and central zone assessment, assessment of background signal and tumor aggressiveness, changes in the structured report, the need for the parameters for imaging quality and performance control, and indications for expansion of the system to include other indications.
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Affiliation(s)
- Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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9
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Chang SD, Reinhold C, Kirkpatrick IDC, Clarke SE, Schieda N, Hurrell C, Cool DW, Tunis AS, Alabousi A, Diederichs BJ, Haider MA. Canadian Association of Radiologists Prostate MRI White Paper. Can Assoc Radiol J 2022; 73:626-638. [PMID: 35971326 DOI: 10.1177/08465371221105532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Prostate cancer is the most common malignancy and the third most common cause of death in Canadian men. In light of evolving diagnostic pathways for prostate cancer and the increased use of MRI, which now includes its use in men prior to biopsy, the Canadian Association of Radiologists established a Prostate MRI Working Group to produce a white paper to provide recommendations on establishing and maintaining a Prostate MRI Programme in the context of the Canadian healthcare system. The recommendations, which are based on available scientific evidence and/or expert consensus, are intended to maintain quality in image acquisition, interpretation, reporting and targeted biopsy to ensure optimal patient care. The paper covers technique, reporting, quality assurance and targeted biopsy considerations and includes appendices detailing suggested reporting templates, quality assessment tools and sample image acquisition protocols relevant to the Canadian healthcare context.
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Affiliation(s)
- Silvia D Chang
- Department of Radiology, University of British Columbia, Vancouver General Hospital, Vancouver, BC, Canada
| | - Caroline Reinhold
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of McGill University Health Centre, McGill University Health Centre, Montreal, QC, Canada
| | | | | | - Nicola Schieda
- Department of Diagnostic Imaging, The Ottawa Hospital- Civic Campus, Ottawa, ON, Canada
| | - Casey Hurrell
- Canadian Association of Radiologists, Ottawa, ON, Canada
| | - Derek W Cool
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Adam S Tunis
- Department of Medical Imaging, University of Toronto, North York General Hospital, Toronto, ON, Canada
| | - Abdullah Alabousi
- Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, ON, Canada
| | | | - Masoom A Haider
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada
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10
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Ciggaar IA, Henneman OD, Oei SA, J.S.M.L. Vanhooymissen I, Blikkendaal MD, Bipat S. Bowel preparation in MRI for detection of endometriosis: comparison of the effect of an enema, no additional medication and intravenous butylscopolamine on image quality. Eur J Radiol 2022; 149:110222. [DOI: 10.1016/j.ejrad.2022.110222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/08/2022] [Accepted: 02/12/2022] [Indexed: 11/27/2022]
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11
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Arnoldner MA, Polanec SH, Lazar M, Kadhjavi S, Clauser P, Pötsch N, Schwarz-Nemec U, Korn S, Hübner N, Shariat SF, Helbich TH, Baltzer PAT. Rectal preparation significantly improves prostate imaging quality: Assessment of the PI-QUAL score with visual grading characteristics. Eur J Radiol 2022; 147:110145. [PMID: 35007983 DOI: 10.1016/j.ejrad.2021.110145] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 12/23/2021] [Accepted: 12/29/2021] [Indexed: 01/13/2023]
Abstract
PURPOSE To investigate the effects of a rectal preparation regimen, that consisted of a rectal cleansing enema and an endorectal gel filling protocol, on prostate imaging quality (PI-QUAL). METHODS Multiparametric MRI (mpMRI) was performed in 150 consecutive patients divided into two groups of 75 patients. One group received a rectal preparation with a cleansing enema and endorectal gel filling (median age 65.3 years, median PSA level 6 ng/ml). The other patient group did not receive such a preparation (median age 64 years, median PSA level 6 ng/ml). Two uroradiologists independently rated general image quality and lesion visibility on diffusion-weighted imaging (DWI), T2-weighted (T2w), and dynamic contrast-enhanced (DCE) images using a five-point ordinal scale. In addition, two uroradiologists assigned PI-QUAL scores, using the dedicated scoring sheet. Data sets were compared using visual grading characteristics (VGC) and receiver operating characteristics (ROC)/ area under the curve (AUC) analysis. RESULTS VGC revealed significantly better general image quality for DWI (AUC R1 0.708 (0.628-0.779 CI, p < 0.001; AUC R2 0.687 (0.606-0.760 CI, p < 0.001) and lesion visibility for both readers (AUC R1 0.729 (0.607-0.831 CI, p < 0.001); AUC R2 0.714 (0.590-0.818CI, p < 0.001) in the preparation group. For T2w imaging, rectal preparation resulted in significantly better lesion visibility for both readers (R1 0.663 (0.537-0.774 CI, p = 0.014; R2 0.663 (0.537-0.774 CI, p = 0.014)). Averaged PI-QUAL scores were significantly improved with rectal preparation (AUC R3/R4 0.667, CI 0.581-0.754, p < 0.001). CONCLUSION Rectal preparation significantly improved prostate imaging quality (PI-QUAL) and lesion visibility. Hence, a rectal preparation regimen consisting of a rectal cleansing enema and an endorectal gel filling could be considered.
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Affiliation(s)
- Michael A Arnoldner
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Austria
| | | | | | - Sam Kadhjavi
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Austria
| | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Austria
| | - Nina Pötsch
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Austria
| | - Ursula Schwarz-Nemec
- Department of Biomedical Imaging and Image-guided Therapy, Division of Neuroradiology and Musculoskeletal Radiology, Medical University of Vienna, Austria
| | - Stephan Korn
- Department of Urology, Medical University of Vienna, Austria
| | - Nicolai Hübner
- Department of Urology, Medical University of Vienna, Austria
| | - Shahrokh F Shariat
- Department of Urology, Medical University of Vienna, Austria; Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria; Department of Urology, Weill Cornell Medical College, New York, NY, USA; Department of Urology, University of Texas Southwestern, Dallas, TX, USA; Department of Urology, Second Faculty of Medicine, Charles University, Prag, Czech Republic; Institute for Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Austria
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Austria.
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12
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Influence of Enema and Dietary Restrictions on Prostate MR Image Quality: A Multireader Study. Acad Radiol 2022; 29:4-14. [PMID: 33162316 DOI: 10.1016/j.acra.2020.10.019] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 10/14/2020] [Accepted: 10/15/2020] [Indexed: 01/23/2023]
Abstract
PURPOSE To evaluate the effect of enema and dietary restrictions on prostate MR image quality metrics and to assess inter-reader agreement for these metrics. METHODS This retrospective study included 195 men divided into groups based on their compliance with preparation instructions before prostate MRI (Enema + Diet, n = 98; Enema, n = 42; Diet, n = 35; Control [no compliance], n = 20). Four readers independently assessed six image quality metrics on a 5-point scale. Between-group comparisons were made using Wilcoxon rank sum test. Inter-reader agreement was calculated using Fleiss' kappa. RESULTS Compared with the Control group, image quality with respect to rectal stool/gas, distortion of diffusion-weighted images, overall image quality, and confidence in assessment was higher in the Enema + Diet, Enema, and Diet groups (p < 0.05 for all comparisons). The Enema + Diet and Enema groups had significantly higher scores than the Diet group for rectal stool/gas (p < 0.001 and 0.005, respectively). The Enema + Diet and Diet groups had higher scores than the Control group for rectal peristalsis (p = 0.027 and 0.009, respectively), but there were no significant differences in motion artifacts on T2-weighted images. Agreement among readers was fair, with kappa values ranging from 0.25 to 0.37. CONCLUSION Enema and dietary restriction can improve the quality of prostate MRI by decreasing rectal distension and distortion of diffusion-weighted images and by increasing reader confidence in image assessment. Inter-reader agreement using subjective criteria for analysis of MRI quality is fair.
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13
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Bates DD, Shaish H, Gollub MJ, Harisinghani M, Lall C, Sheedy S. Multi-practice survey on MR imaging practice patterns in rectal cancer in the United States. Abdom Radiol (NY) 2022; 47:28-37. [PMID: 34605968 PMCID: PMC9671700 DOI: 10.1007/s00261-021-03279-2] [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: 08/04/2021] [Revised: 09/07/2021] [Accepted: 09/08/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To investigate practice patterns related to MR technique and structured reporting for MRI of rectal cancer at academic centers and private practice groups in the United States. METHODS AND MATERIALS A survey developed by active members of the Society of Abdominal Radiology Rectal and Anal Cancer Disease Focus Panel was sent to 100 private practice and 189 academic radiology groups. The survey asked targeted questions about practice demographics and utilization, technical MR parameters and reporting practices related to MRI of rectal cancer. The results were analyzed using software in an online survey program. RESULTS The survey received 47 unique responses from academic (37/47, 78.7%) and private practice (10/47, 21.3%) groups. Most practices report using rectal MR for staging newly diagnosed rectal cancer always (29/47, 61.7%), and less than half always do so for re-staging after neoadjuvant chemoradiation (20/47, 42.6%). Most groups perform between 1 to 5 rectal MR studies for baseline staging per week (32/47, 68.1%) and most groups perform 0 to 2 MR studies for re-staging per week (27/47, 57.4%). Regarding patient preparation and MR technical parameters, there is variability, but a few key points include most practices (27/47, 57.4%) acquire axial T2-weighted images at a slice thickness of 3 mm or less, axial T2-weighted images with 2D acquisition (30/47, 63.8%), a T2-weighted axial oblique sequence through the tumor (43/47, 91.5%), and T2-weighted images without fat suppression (37/47, 78.7%). Equal numbers of groups report using a maximal b-value less than 1000 s/mm2 (19/43, 44.2%) and 1000 s/mm2 or greater (19/43, 44.2%); the rest were unsure. A substantial portion of respondents do not use intravenous contrast (13/47, 27.7%). Most believe that structured report templates contribute to uniformity of reporting practices (39/47, 83.0%), though there is considerable heterogeneity in usage and included elements. CONCLUSION There is considerable technical heterogeneity among respondents' answers and reporting practices in MR for rectal cancer, and most of the groups report reading only a modest number of studies per week. Our findings suggest there may be room for improvement in terms of radiologist education for performance and standardization of clinical practice for MR imaging of rectal cancer.
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Affiliation(s)
- David D.B. Bates
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Hiram Shaish
- Department of Radiology, Columbia University Medical Center, New York, NY
| | - Marc J. Gollub
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Chandana Lall
- Department of Radiology, University of Florida College of Medicine, Jacksonville, FL
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14
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Sathiadoss P, Haroon M, Osman H, Ahmad F, Papadatos P, Schieda N. Comparison of 5 Rectal Preparation Strategies for Prostate MRI and Impact on Image Quality. Can Assoc Radiol J 2021; 73:346-354. [PMID: 34404240 DOI: 10.1177/08465371211033753] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
PURPOSE To compare 5 different rectal preparation strategies for prostate MRI. METHODS This 5-arm quality-assurance study evaluated 56 patients per arm (280 patients) including: no preparation, clear-fluids diet (CFD) beginning at 00:00 hours on the day of MRI, Fleet®-enema, enema + CFD, enema + CFD + IV-antispasmodic agent. The study was powered to 0.80 with alpha-error of 0.05. Three blinded radiologists independently evaluated T2-Weighted (T2W) and Diffusion Weighed Imaging (DWI) for: rectal diameter (maximal AP diameter), rectal content (stool, fluid, gas), rectal motion, T2W/DWI image quality, T2W image sharpness and DWI susceptibility artifact using 5-point Likert scales. Overall comparisons were performed using analysis of variance (ANOVA) and Kruskal-Wallis, with pair-wise comparisons using paired t-tests and Wilcoxon sign-rank tests. RESULTS Rectal diameter and amount of gas were lower in enema compared to non-enema groups (p < 0.001), with smallest diameter and least gas in the enema + CFD + IV-antispasmodic group (p = 0.022-<0.001). T2W image quality and sharpness were highest in the enema + CFD groups (p < 0.001) with no difference comparing enema + CFD with/without IV-antispasmodic (p = 0.064, 0.084). Motion artifact was least in enema + CFD + IV-antispasmodic group compared to all other groups (p < 0.001), followed by the enema + CFD group (p = 0.008-<0.001). DWI image quality was highest (p < 0.001) and DWI susceptibility artifact lowest (p < 0.001) in the enema + CFD groups (p < 0.001) and did not differ comparing enema + CFD with/without anti-spasmodic (p = 0.058-0.202). CONCLUSIONS Use of enema + clear-fluids diet before prostate MRI yields the highest T2W and DWI image quality with the least DWI artifact. IV-antispasmodic use reduces motion on T2W but does not improve image quality on T2W or DWI, or lessen DWI artifact compared to enema + clear-fluids diet.
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Affiliation(s)
- Paul Sathiadoss
- Department of Medical Imaging, The Ottawa Hospital, 6363University of Ottawa, Ottawa, Ontario, Canada
| | - Mohammad Haroon
- Department of Medical Imaging, The Ottawa Hospital, 6363University of Ottawa, Ottawa, Ontario, Canada
| | - Heba Osman
- Department of Medical Imaging, The Ottawa Hospital, 6363University of Ottawa, Ottawa, Ontario, Canada
| | - Faraz Ahmad
- Faculty of Medicine, 6363University of Ottawa, Ottawa, Ontario, Canada
| | - Philip Papadatos
- Department of Medical Imaging, The Ottawa Hospital, 6363University of Ottawa, Ottawa, Ontario, Canada
| | - Nicola Schieda
- Department of Medical Imaging, The Ottawa Hospital, 6363University of Ottawa, Ottawa, Ontario, Canada
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15
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Cipollari S, Guarrasi V, Pecoraro M, Bicchetti M, Messina E, Farina L, Paci P, Catalano C, Panebianco V. Convolutional Neural Networks for Automated Classification of Prostate Multiparametric Magnetic Resonance Imaging Based on Image Quality. J Magn Reson Imaging 2021; 55:480-490. [PMID: 34374181 PMCID: PMC9291235 DOI: 10.1002/jmri.27879] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 12/26/2022] Open
Abstract
Background Prostate magnetic resonance imaging (MRI) is technically demanding, requiring high image quality to reach its full diagnostic potential. An automated method to identify diagnostically inadequate images could help optimize image quality. Purpose To develop a convolutional neural networks (CNNs) based analysis pipeline for the classification of prostate MRI image quality. Study Type Retrospective. Subjects Three hundred sixteen prostate mpMRI scans and 312 men (median age 67). Field Strength/Sequence A 3 T; fast spin echo T2WI, echo planar imaging DWI, ADC, gradient‐echo dynamic contrast enhanced (DCE). Assessment MRI scans were reviewed by three genitourinary radiologists (V.P., M.D.M., S.C.) with 21, 12, and 5 years of experience, respectively. Sequences were labeled as high quality (Q1) or low quality (Q0) and used as the reference standard for all analyses. Statistical Tests Sequences were split into training, validation, and testing sets (869, 250, and 120 sequences, respectively). Inter‐reader agreement was assessed with the Fleiss kappa. Following preprocessing and data augmentation, 28 CNNs were trained on MRI slices for each sequence. Model performance was assessed on both a per‐slice and a per‐sequence basis. A pairwise t‐test was performed to compare performances of the classifiers. Results The number of sequences labeled as Q0 or Q1 was 38 vs. 278 for T2WI, 43 vs. 273 for DWI, 41 vs. 275 for ADC, and 38 vs. 253 for DCE. Inter‐reader agreement was almost perfect for T2WI and DCE and substantial for DWI and ADC. On the per‐slice analysis, accuracy was 89.95% ± 0.02% for T2WI, 79.83% ± 0.04% for DWI, 76.64% ± 0.04% for ADC, 96.62% ± 0.01% for DCE. On the per‐sequence analysis, accuracy was 100% ± 0.00% for T2WI, DWI, and DCE, and 92.31% ± 0.00% for ADC. The three best algorithms performed significantly better than the remaining ones on every sequence (P‐value < 0.05). Data Conclusion CNNs achieved high accuracy in classifying prostate MRI image quality on an individual‐slice basis and almost perfect accuracy when classifying the entire sequences. Evidence Level 4 Technical Efficacy Stage 1
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Affiliation(s)
- Stefano Cipollari
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Valerio Guarrasi
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Italy
| | - Martina Pecoraro
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Marco Bicchetti
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Emanuele Messina
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Lorenzo Farina
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Italy
| | - Paola Paci
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, Italy
| | - Carlo Catalano
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Valeria Panebianco
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
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16
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Value of bowel preparation techniques for prostate MRI: a preliminary study. Abdom Radiol (NY) 2021; 46:4002-4013. [PMID: 33770222 PMCID: PMC8286932 DOI: 10.1007/s00261-021-03046-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 03/05/2021] [Accepted: 03/09/2021] [Indexed: 12/24/2022]
Abstract
Background Bowel preparation before multiparametric MRI (mpMRI) of the prostate is performed widely, despite contradictory or no evidence for efficacy. Purpose To investigate the value of hyoscine N-butylbromide (HBB), microenema (ME) and ‘dietary restrictions’ (DR) for artifact reduction and image quality (IQ) in mpMRI of the prostate. Study type Retrospective. Population Between 10/2018 and 02/2020 treatment-naïve men (median age, 64.9; range 39.8–87.3) who underwent mpMRI of the prostate were included. The total patient sample comprised of n = 180 patients, who received either HBB, ME, were instructed to adhere to DR, or received a combination of those measures prior to the MR scan. Field strength/sequence T2-weighted imaging (T2w), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI) scanned on two 3T systems. Assessment A radiologist specialized in urogenital imaging (R1) and a senior radiology resident (R2) visually assessed IQ parameters on transversal T2w, DWI and ADC maps on a 5-point Likert-like scale. Statistical tests Group comparison between IQ parameters was performed on reader level using Kruskal–Wallis and Mann–Whitney U tests. Binary univariate logistic regression analysis was used to assess independent predictors of IQ. Interrater agreement was assessed using Intraclass Correlation Coefficient (ICC). Results ‘DWI geometric distortion’ was significantly more pronounced in the HBB+/ME−/DR− (R1, 3.6 and R2, 4.0) as compared to the HBB−/ME+/DR− (R1, 4.2 and R2, 4.6) and HBB+/ME+/DR− (R1, 4.3 and R2, 4.7) cohort, respectively. Parameters ‘DWI IQ’ and ‘Whole MRI IQ’ were rated similarly by both readers. ME was a significant independent predictor of ‘good IQ’ for the whole MRI for R1 [b = 1.09, OR 2.98 (95% CI 1.29, 6.87)] and R2 [b = 1.01, OR 2.73 (95% CI 1.24, 6.04)], respectively. Data conclusion ME seems to significantly improve image quality of DWI and the whole mpMRI image set of the prostate. HBB and DR did not have any benefit.
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17
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Giganti F, Kasivisvanathan V, Kirkham A, Punwani S, Emberton M, Moore CM, Allen C. Prostate MRI quality: a critical review of the last 5 years and the role of the PI-QUAL score. Br J Radiol 2021; 95:20210415. [PMID: 34233502 PMCID: PMC8978249 DOI: 10.1259/bjr.20210415] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
There is increasing interest in the use of multiparametric magnetic resonance imaging (mpMRI) in the prostate cancer pathway. The European Association of Urology (EAU) and the British Association of Urological Surgeons (BAUS) now advise mpMRI prior to biopsy, and the Prostate Imaging Reporting and Data System (PI-RADS) recommendations set out the minimal technical requirements for the acquisition of mpMRI of the prostate.The widespread and swift adoption of this technique has led to variability in image quality. Suboptimal image acquisition reduces the sensitivity and specificity of mpMRI for the detection and staging of clinically significant prostate cancer.This critical review outlines the studies aimed at improving prostate MR quality that have been published over the last 5 years. These span from the use of specific MR sequences, magnets and coils to patient preparation. The rates of adherence of prostate mpMRI to technical standards in different cohorts across the world are also discussed.Finally, we discuss the first standardised scoring system (i.e., Prostate Imaging Quality, PI-QUAL) that has been created to evaluate image quality, although further iterations of this score are expected in the future.
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Affiliation(s)
- Francesco Giganti
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK.,Division of Surgery & Interventional Science, University College London, London, UK
| | - Veeru Kasivisvanathan
- Division of Surgery & Interventional Science, University College London, London, UK.,Department of Urology, 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
| | - Mark Emberton
- Division of Surgery & Interventional Science, University College London, London, UK.,Department of Urology, University College London Hospital NHS Foundation Trust, 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
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18
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Brennan DL, Lazarakis S, Lee A, Tan TH, Chin KY, Oon SF. Do antispasmodics or rectal enemas improve image quality on multiparametric prostate MRI? An 'Evidence-Based Practice' review of the literature. Abdom Radiol (NY) 2021; 46:2770-2778. [PMID: 33464364 DOI: 10.1007/s00261-020-02916-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 12/10/2020] [Accepted: 12/12/2020] [Indexed: 01/02/2023]
Abstract
Multiparametric magnetic resonance imaging (mpMRI) of the prostate is increasingly used for the preoperative detection and staging of prostate cancer. Image quality of prostate mpMRI can be significantly degraded by motion related artefact due to bowel peristalsis and susceptibility related artefact, which reduces cancer detection sensitivity. The use of several different methods including anstispasmodic medications and rectal enemas were proposed as potential methods to reduce mpMRI artefacts, but current recommendations in the scientific literature are conflicting and inconsistent. This article seeks to identify the best available evidence to determine which patient preparation method is most effective in improving prostate mpMRI, and provides recommendations for further areas of research. We used the five-step 'Evidence-Based Practice' systematic approach of 'Ask, Search, Appraise, Apply and Evaluate' described by the McMaster University and National Health Service for critical appraisal of topics. We developed a focused clinical question using a PICO format, and performed a primary and secondary literature search through Ovid Medline, Ovid Embase and Cochrane CENTRAL (Wiley). All identified articles were appraised for strength and validity. Seven articles were retrieved which demonstrated conflicting sensitivities and specificities for intravenous hyoscine butylbromide and rectal enema in improving image susceptibility artefact, motion artefact, and anatomic distortion on the T2 or diffusion weighted imaging sequences. Intravenous hysoscine butylbromide is the optimum patient preparation method for improving T2W and DWI image quality in prostate mpMRI. The use of a preparatory rectal enema is not currently recommended, but better quality studies are required.
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19
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Giganti F, Kirkham A, Kasivisvanathan V, Papoutsaki MV, Punwani S, Emberton M, Moore CM, Allen C. Understanding PI-QUAL for prostate MRI quality: a practical primer for radiologists. Insights Imaging 2021; 12:59. [PMID: 33932167 PMCID: PMC8088425 DOI: 10.1186/s13244-021-00996-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 04/01/2021] [Indexed: 12/19/2022] Open
Abstract
Prostate magnetic resonance imaging (MRI) of high diagnostic quality is a key determinant for either detection or exclusion of prostate cancer. Adequate high spatial resolution on T2-weighted imaging, good diffusion-weighted imaging and dynamic contrast-enhanced sequences of high signal-to-noise ratio are the prerequisite for a high-quality MRI study of the prostate. The Prostate Imaging Quality (PI-QUAL) score was created to assess the diagnostic quality of a scan against a set of objective criteria as per Prostate Imaging-Reporting and Data System recommendations, together with criteria obtained from the image. The PI-QUAL score is a 1-to-5 scale where a score of 1 indicates that all MR sequences (T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced sequences) are below the minimum standard of diagnostic quality, a score of 3 means that the scan is of sufficient diagnostic quality, and a score of 5 implies that all three sequences are of optimal diagnostic quality. The purpose of this educational review is to provide a practical guide to assess the quality of prostate MRI using PI-QUAL and to familiarise the radiologist and all those involved in prostate MRI with this scoring system. A variety of images are also presented to demonstrate the difference between suboptimal and good prostate MR scans.
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Affiliation(s)
- Francesco Giganti
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK. .,Division of Surgery and Interventional Science, University College London, London, W1W 7TS, UK.
| | - Alex Kirkham
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
| | - Veeru Kasivisvanathan
- Division of Surgery and Interventional Science, University College London, London, W1W 7TS, UK.,Department of Urology, 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
| | - Mark Emberton
- Division of Surgery and Interventional Science, University College London, London, W1W 7TS, UK.,Department of Urology, University College London Hospital NHS Foundation Trust, London, UK
| | - Caroline M Moore
- Division of Surgery and Interventional Science, University College London, London, W1W 7TS, 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
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20
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Reischauer C, Cancelli T, Malekzadeh S, Froehlich JM, Thoeny HC. How to improve image quality of DWI of the prostate-enema or catheter preparation? Eur Radiol 2021; 31:6708-6716. [PMID: 33758955 PMCID: PMC8379127 DOI: 10.1007/s00330-021-07842-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Revised: 02/12/2021] [Accepted: 02/25/2021] [Indexed: 12/28/2022]
Abstract
OBJECTIVES To compare the impact of laxative enema preparation versus air/gas suction through a small catheter on image quality of prostate DWI. METHODS In this single-center study, 200 consecutive patients (100 in each arm) with either enema or catheter preparation were retrospectively included. Two blinded readers independently assessed aspects of image quality on 5-point Likert scales. Scores were compared between groups and the influence of confounding factors evaluated using multivariable logistic regression. Prostate diameters were compared on DWI and T2-weighted imaging using intraclass correlation coefficients. RESULTS Image quality was significantly higher in the enema group regarding the severity of susceptibility-related artifacts (reader 1: 0.34 ± 0.77 vs. 1.73 ± 1.34, reader 2: 0.38 ± 0.86 vs. 1.76 ± 1.39), the differentiability of the anatomy (reader 1: 3.36 ± 1.05 vs. 2.08 ± 1.31, reader 2: 3.37 ± 1.05 vs. 2.09 ± 1.35), and the overall image quality (reader 1: 3.66 ± 0.77 vs. 2.26 ± 1.33, Reader 2: 3.59 ± 0.87 vs. 2.23 ± 1.38) with almost perfect inter-observer agreement (κ = 0.92-0.95). In the enema group, rectal distention was significantly lower and strongly correlated with the severity of artifacts (reader 1: ρ = 0.79, reader 2: ρ = 0.73). Furthermore, there were significantly fewer substantial image distortions, with odds ratios of 0.051 and 0.084 for the two readers which coincided with a higher agreement of the prostate diameters in the phase-encoding direction (0.96 vs. 0.89). CONCLUSIONS Enema preparation is superior to catheter preparation and yields substantial improvements in image quality. KEY POINTS • Enema preparation is superior to decompression of the rectum using air/gas suction through a small catheter. • Enema preparation markedly improves the image quality of prostate DWI regarding the severity of susceptibility-related artifacts, the differentiability of the anatomy, and the overall image quality and considerably reduces substantial artifacts that may impair a reliable diagnosis.
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Affiliation(s)
- Carolin Reischauer
- Department of Medicine, University of Fribourg, Chemin du Musée 8, 1700, Fribourg, CH, Switzerland.
- Department of Radiology, Cantonal Hospital Fribourg, Fribourg, Switzerland.
| | - Timmy Cancelli
- Department of Radiology, Cantonal Hospital Fribourg, Fribourg, Switzerland
| | - Sonaz Malekzadeh
- Department of Medicine, University of Fribourg, Chemin du Musée 8, 1700, Fribourg, CH, Switzerland
- Department of Radiology, Cantonal Hospital Fribourg, Fribourg, Switzerland
| | - Johannes M Froehlich
- Department of Medicine, University of Fribourg, Chemin du Musée 8, 1700, Fribourg, CH, Switzerland
- Department of Radiology, Cantonal Hospital Fribourg, Fribourg, Switzerland
| | - Harriet C Thoeny
- Department of Medicine, University of Fribourg, Chemin du Musée 8, 1700, Fribourg, CH, Switzerland
- Department of Radiology, Cantonal Hospital Fribourg, Fribourg, Switzerland
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