The role of AI in prostate MRI quality and interpretation: Opportunities and challenges

Kim, Heejong; Kang, Shin Won; Kim, Jae‐Hun; Nagar, Himanshu; Sabuncu, Mert R.; Margolis, Daniel; Kim, Chan Kyo

doi:10.1016/j.ejrad.2023.110887

Cited by 10 publications

(2 citation statements)

References 130 publications

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“…The score is also based on subjective evaluation only. Objective image quality measurement on MRI in different domains is difficult, although the use of AI in quality control of prostate MRI has been proposed ( 46 ). Here, we expressed sharpness in absolute values as a no-reference statistics of gradient of the edges ( 39 ).…”

Section: Discussionmentioning

confidence: 99%

Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time

Jurka,

Macova,

Wagnerova

et al. 2024

Quant Imaging Med Surg

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Background Deep-learning-based reconstruction (DLR) improves the quality of magnetic resonance (MR) images which allows faster acquisitions. The aim of this study was to compare the image quality of standard and accelerated T2 weighted turbo-spin-echo (TSE) images of the prostate reconstructed with and without DLR and to find associations between perceived image quality and calculated image characteristics. Methods In a cohort of 47 prospectively enrolled consecutive patients referred for bi-parametric prostate magnetic resonance imaging (MRI), two T2-TSE acquisitions in the transverse plane were acquired on a 3T scanner-a standard T2-TSE sequence and a short sequence accelerated by a factor of two using compressed sensing (CS). The images were reconstructed with and without DLR in super-resolution mode. The image quality was rated in six domains. Signal-to-noise ratio (SNR), and image sharpness were measured. Results The mean acquisition time was 281±23 s for the standard and 140±12 s for the short acquisition (P<0.0001). DLR images had higher sharpness compared to non-DLR (P<0.001). Short and short-DLR had lower SNR than the standard and standard-DLR (P<0.001). The perceived image quality of short-DLR was rated better in all categories compared to the standard sequence (P<0.001 to P=0.004). All domains of subjective evaluation were correlated with measured image sharpness (P<0.001). Conclusions T2-TSE acquisition of the prostate accelerated using CS combined with DLR reconstruction provides images with increased sharpness that have a superior quality as perceived by human readers compared to standard T2-TSE. The perceived image quality is correlated with measured image contrast.

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Section: Discussionmentioning

confidence: 99%

Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time

Jurka,

Macova,

Wagnerova

et al. 2024

Quant Imaging Med Surg

View full text Add to dashboard Cite

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“…Deep and machine learning, accompanied by pattern recognition tools, are developing into important components for extracting information that improve clinical diagnosis and predict disease progression [5]. Typically, AI is applied for the analysis of regions of interest (ROI) on a radiation therapy (RT) simulation scan, but also for computed tomography (CT), magnetic resonance (MR)-guided RT, MR scans and other imaging techniques [6][7][8][9]. AI replaces some iterative analysis steps and enhances the efficiency and accuracy of the results [10,11].…”

Section: Introductionmentioning

confidence: 99%

Digital Pathology: A Comprehensive Review of Open-Source Histological Segmentation Software

Pavone,

Giannone,

Cabibi

et al. 2024

BioMedInformatics

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In the era of digitalization, the biomedical sector has been affected by the spread of artificial intelligence. In recent years, the possibility of using deep and machine learning methods for clinical diagnostic and therapeutic interventions has been emerging as an essential resource for biomedical imaging. Digital pathology represents innovation in a clinical world that looks for faster and better-performing diagnostic methods, without losing the accuracy of current human-guided analyses. Indeed, artificial intelligence has played a key role in a wide variety of applications that require the analysis of a massive amount of data, including segmentation processes in medical imaging. In this context, artificial intelligence enables the improvement of image segmentation methods, moving towards the development of fully automated systems of analysis able to support pathologists in decision-making procedures. The aim of this review is to aid biologists and clinicians in discovering the most common segmentation open-source tools, including ImageJ (v. 1.54), CellProfiler (v. 4.2.5), Ilastik (v. 1.3.3) and QuPath (v. 0.4.3), along with their customized implementations. Additionally, the tools’ role in the histological imaging field is explored further, suggesting potential application workflows. In conclusion, this review encompasses an examination of the most commonly segmented tissues and their analysis through open-source deep and machine learning tools.

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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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The role of AI in prostate MRI quality and interpretation: Opportunities and challenges

Cited by 10 publications

References 130 publications

Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time

Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time

Digital Pathology: A Comprehensive Review of Open-Source Histological Segmentation Software

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

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