Picture Perfect: The Status of Image Quality in Prostate <scp>MRI</scp>

Woernle, Alexandre; Englman, Cameron; Dickinson, Louise; Kirkham, Alex; Punwani, Shonit; Haider, Aiman; Freeman, Alex; Kasivisivanathan, Veeru; Emberton, Mark; Hines, John; Moore, Caroline M.; Allen, Clare; Giganti, Francesco

doi:10.1002/jmri.29025

Cited by 12 publications

(5 citation statements)

References 137 publications

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“…High image quality is a prerequisite for any MRI-based diagnostic pathway. Suboptimal image quality can result in under or overcalling lesions, inadequate characterisation and staging, or the inability to confidently call negative scans [14,21]. Poor image quality has been associated with increased rates of indeterminate MRI findings (i.e.…”

Section: Prostate Mr Image Qualitymentioning

confidence: 99%

“…Despite the PI-RADS technical recommendations, prostate MR quality remains variable and can be influenced by various factors [14]. Prostate MRI is susceptible to a variety of artefacts including:…”

Section: Prostate Mri Artefactsmentioning

confidence: 99%

“…In theory, there is a benefit of using a higher magnetic field strength because of the higher SNR, but the disadvantage to this is a higher risk of susceptibility artefacts. Limited evidence from small studies shows higher image quality from 3 T scanners compared with 1.5 T. However, there is a consensus that adequate image quality is still possible at at 1.5 T with optimised protocols [14].…”

Section: Hardwarementioning

confidence: 99%

“…Despite the implementation of the Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 technical standards, wide variability in image quality persists in community practice [9][10][11]. This variation is not only caused by poor adherence to the PI-RADS technical requirements, but is also influenced by factors such as patient motion, metalwork, patient preparation, and scanner hardware including receiver coils, scanner performance, software level and field strength [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…PI-QUAL v1 has been evaluated in different patient cohorts and it has been demonstrated that image quality has a direct bearing on cancer detection and biopsy planning [14]. However, PI-QUAL v1 has limitations [19,20]; the most important being that it is suitable only for mpMRI examinations, thus excluding examinations without intravenous contrast medium.…”

Section: Introductionmentioning

confidence: 99%

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PI-QUAL version 2: an update of a standardised scoring system for the assessment of image quality of prostate MRI

de Rooij,

Allen,

Twilt

et al. 2024

Eur Radiol

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Multiparametric MRI is the optimal primary investigation when prostate cancer is suspected, and its ability to rule in and rule out clinically significant disease relies on high-quality anatomical and functional images. Avenues for achieving consistent high-quality acquisitions include meticulous patient preparation, scanner setup, optimised pulse sequences, personnel training, and artificial intelligence systems. The impact of these interventions on the final images needs to be quantified. The prostate imaging quality (PI-QUAL) scoring system was the first standardised quantification method that demonstrated the potential for clinical benefit by relating image quality to cancer detection ability by MRI. We present the updated version of PI-QUAL (PI-QUAL v2) which applies to prostate MRI performed with or without intravenous contrast medium using a simplified 3-point scale focused on critical technical and qualitative image parameters. Clinical relevance statement High image quality is crucial for prostate MRI, and the updated version of the PI-QUAL score (PI-QUAL v2) aims to address the limitations of version 1. It is now applicable to both multiparametric MRI and MRI without intravenous contrast medium. Key Points High-quality images are essential for prostate cancer diagnosis and management using MRI. PI-QUAL v2 simplifies image assessment and expands its applicability to prostate MRI without contrast medium. PI-QUAL v2 focuses on critical technical and qualitative image parameters and emphasises T2-WI and DWI.

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Section: Prostate Mr Image Qualitymentioning

confidence: 99%

Section: Prostate Mri Artefactsmentioning

confidence: 99%

Section: Hardwarementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PI-QUAL version 2: an update of a standardised scoring system for the assessment of image quality of prostate MRI

de Rooij,

Allen,

Twilt

et al. 2024

Eur Radiol

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Optimizing Multiparametric MRI Protocols for Prostate Cancer Detection: A Comprehensive Assessment Aligned with PI‐RADS Guidelines

Jamshidi,

Fatemi,

Karami

et al. 2024

Health Science Reports

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Background and AimMultiparametric magnetic resonance imaging (mpMRI) is recognized as the most indicative method for diagnosing prostate cancer. The purpose of this narrative review is to provide a comprehensive evaluation aligned with the Prostate Imaging and Reporting Data System (PI‐RADS) guidelines, offering an in‐depth insight into the various MRI sequences used in a standard mpMRI protocol. Additionally, it outlines the critical technical requirements necessary to perform a standard mpMRI examination of the prostate, as defined by the PI‐RADS specifications.MethodsEuropean Society of Urogenital Radiology has released PI‐RADS guideline detailing its suggestions aimed at improving the standards of the procedure. The purpose of this guideline is to establish a standard strategy for MRI protocols and image interpretation, aiming to prevent variability in each of the imaging and interpretation stages.ResultsA standard mpMRI protocol comprises morphological sequences and functional sequences. Morphological sequences which encompass T1‐ and T2‐weighted images, and various functional sequences include diffusion‐weighted imaging, and dynamic contrast‐enhanced MRI. The PI‐RADS recommendations assert that having a standard and uniform protocol for all MRI centers is imperative. Furthermore, the existence of a standardized checklist for interpreting MRI images can foster greater consensus in the process of diagnosing and treating patients.ConclusionStandardized protocols and checklists for mpMRI interpretation are essential for achieving greater consensus among radiologists, ultimately leading to improved diagnostic outcomes in prostate cancer.

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Enhancing prostate cancer segmentation on multiparametric magnetic resonance imaging with background information and gland masks

Wang,

Sun,

Wei

et al. 2024

Medical Physics

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BackgroundThe landscape of prostate cancer (PCa) segmentation within multiparametric magnetic resonance imaging (MP‐MRI) was fragmented, with a noticeable lack of consensus on incorporating background details, culminating in inconsistent segmentation outputs. Given the complex and heterogeneous nature of PCa, conventional imaging segmentation algorithms frequently fell short, prompting the need for specialized research and refinement.PurposeThis study sought to dissect and compare various segmentation methods, emphasizing the role of background information and gland masks in achieving superior PCa segmentation. The goal was to systematically refine segmentation networks to ascertain the most efficacious approach.MethodsA cohort of 232 patients (ages 61–73 years old, prostate‐specific antigen: 3.4–45.6 ng/mL), who had undergone MP‐MRI followed by prostate biopsies, was analyzed. An advanced segmentation model, namely Attention‐Unet, which combines U‐Net with attention gates, was employed for training and validation. The model was further enhanced through a multiscale module and a composite loss function, culminating in the development of Matt‐Unet. Performance metrics included Dice Similarity Coefficient (DSC) and accuracy (ACC).ResultsThe Matt‐Unet model, which integrated background information and gland masks, outperformed the baseline U‐Net model using raw images, yielding significant gains (DSC: 0.7215 vs. 0.6592; ACC: 0.8899 vs. 0.8601, p < 0.001).ConclusionA targeted and practical PCa segmentation method was designed, which could significantly improve PCa segmentation on MP‐MRI by combining background information and gland masks. The Matt‐Unet model showcased promising capabilities for effectively delineating PCa, enhancing the precision of MP‐MRI analysis.

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Picture Perfect: The Status of Image Quality in Prostate MRI

Cited by 12 publications

References 137 publications

PI-QUAL version 2: an update of a standardised scoring system for the assessment of image quality of prostate MRI

PI-QUAL version 2: an update of a standardised scoring system for the assessment of image quality of prostate MRI

Optimizing Multiparametric MRI Protocols for Prostate Cancer Detection: A Comprehensive Assessment Aligned with PI‐RADS Guidelines

Enhancing prostate cancer segmentation on multiparametric magnetic resonance imaging with background information and gland masks

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