Deep Learning Reconstruction Enables Highly Accelerated Biparametric MR Imaging of the Prostate

Enhancing the image quality of prostate diffusion-weighted imaging in patients with prostate cancer through model-based deep learning reconstruction

Purpose

To evaluate the utility of model-based deep learning reconstruction in prostate diffusion-weighted imaging (DWI).

Methods

This retrospective study evaluated two prostate diffusion-weighted imaging (DWI) methods: deep learning reconstruction (DL-DWI) and traditional parallel imaging (PI-DWI). We examined 32 patients with radiologically diagnosed and histologically confirmed prostate cancer (PCa) lesions ≥10 mm. Image quality was evaluated both qualitatively (for overall quality, prostate conspicuity, and lesion conspicuity) and quantitatively, using the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) for prostate tissue.

Results

In the qualitative evaluation, DL-DWI scored significantly higher than PI-DWI for all three parameters (p<0.0001). In the quantitative analysis, DL-DWI showed significantly higher SNR and CNR values compared to PI-DWI (p<0.0001). Both the prostate tissue and the lesions exhibited significantly higher ADC values in DL-DWI compared to PI-DWI (p<0.0001, p=0.0014, respectively).

Conclusion

Model-based DL reconstruction enhanced both qualitative and quantitative aspects of image quality in prostate DWI. However, this study did not include comparisons with other DL-based methods, which is a limitation that warrants future research.

Assessment of multi-modal magnetic resonance imaging for glioma based on a deep learning reconstruction approach with the denoising method

BACKGROUND

Deep learning reconstruction (DLR) with denoising has been reported as potentially improving the image quality of magnetic resonance imaging (MRI). Multi-modal MRI is a critical non-invasive method for tumor detection, surgery planning, and prognosis assessment; however, the DLR on multi-modal glioma imaging has not been assessed.

PURPOSE

To assess multi-modal MRI for glioma based on the DLR method.

MATERIAL AND METHODS

We assessed multi-modal images of 107 glioma patients (49 preoperative and 58 postoperative). All the images were reconstructed with both DLR and conventional reconstruction methods, encompassing T1-weighted (T1W), contrast-enhanced T1W (CE-T1), T2-weighted (T2W), and T2 fluid-attenuated inversion recovery (T2-FLAIR). The image quality was evaluated using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness. Visual assessment and diagnostic assessment were performed blindly by neuroradiologists.

RESULTS

In contrast with conventionally reconstructed images, (residual) tumor SNR for all modalities and tumor to white/gray matter CNR from DLR images were higher in T1W, T2W, and T2-FLAIR sequences. The visual assessment of DLR images demonstrated the superior visualization of tumor in T2W, edema in T2-FLAIR, enhanced tumor and necrosis part in CE-T1, and fewer artifacts in all modalities. Improved diagnostic efficiency and confidence were observed for preoperative cases with DLR images.

CONCLUSION

DLR of multi-modal MRI reconstruction prototype for glioma has demonstrated significant improvements in image quality. Moreover, it increased diagnostic efficiency and confidence of glioma.

Recent trends in AI applications for pelvic MRI: a comprehensive review

Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the affected organ, the Reporting and Data System (RADS) is used to standardize image acquisition and interpretation. Artificial intelligence (AI), which encompasses machine learning and deep learning algorithms, has been integrated into both pelvic MRI and the RADS, particularly for prostate MRI. This review outlines recent developments in the use of AI in various stages of the pelvic MRI diagnostic pathway, including image acquisition, image reconstruction, organ and lesion segmentation, lesion detection and classification, and risk stratification, with special emphasis on recent trends in multi-center studies, which can help to improve the generalizability of AI.

Knowledge-driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un-supervised learning

Deep learning (DL) has emerged as a leading approach in accelerating MRI. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MRI involves physics-based imaging processes, unique data properties, and diverse imaging tasks. This domain knowledge needs to be integrated with data-driven approaches. Our review will introduce the significant challenges faced by such knowledge-driven DL approaches in the context of fast MRI along with several notable solutions, which include learning neural networks and addressing different imaging application scenarios. The traits and trends of these techniques have also been given which have shifted from supervised learning to semi-supervised learning, and finally, to unsupervised learning methods. In addition, MR vendors' choices of DL reconstruction have been provided along with some discussions on open questions and future directions, which are critical for the reliable imaging systems.

Update on PI-RADS Version 2.1 Diagnostic Performance Benchmarks for Prostate MRI: Systematic Review and Meta-Analysis

Background Prostate MRI for the detection of clinically significant prostate cancer (csPCa) is standardized by the Prostate Imaging Reporting and Data System (PI-RADS), currently in version 2.1. A systematic review and meta-analysis infrastructure with a 12-month update cycle was established to evaluate the diagnostic performance of PI-RADS over time. Purpose To provide estimates of diagnostic accuracy and cancer detection rates (CDRs) of PI-RADS version 2.1 categories for prostate MRI, which is required for further evidence-based patient management. Materials and Methods A systematic search of PubMed, Embase, Cochrane Library, and multiple trial registers (English-language studies published from March 1, 2019, to August 30, 2022) was performed. Studies that reported data on diagnostic accuracy or CDRs of PI-RADS version 2.1 with csPCa as the primary outcome were included. For the meta-analysis, pooled estimates for sensitivity, specificity, and CDRs were derived from extracted data at the lesion level and patient level. Sensitivity and specificity for PI-RADS greater than or equal to 3 and PI-RADS greater than or equal to 4 considered as test positive were investigated. In addition to individual PI-RADS categories 1-5, subgroup analyses of subcategories (ie, 2+1, 3+0) were performed. Results A total of 70 studies (11 686 lesions, 13 330 patients) were included. At the patient level, with PI-RADS greater than or equal to 3 considered positive, meta-analysis found a 96% summary sensitivity (95% CI: 95, 98) and 43% specificity (95% CI: 33, 54), with an area under the summary receiver operating characteristic (SROC) curve of 0.86 (95% CI: 0.75, 0.93). For PI-RADS greater than or equal to 4, meta-analysis found an 89% sensitivity (95% CI: 85, 92) and 66% specificity (95% CI: 58, 74), with an area under the SROC curve of 0.89 (95% CI: 0.85, 0.92). CDRs were as follows: PI-RADS 1, 6%; PI-RADS 2, 5%; PI-RADS 3, 19%; PI-RADS 4, 54%; and PI-RADS 5, 84%. The CDR was 12% (95% CI: 7, 19) for transition zone 2+1 lesions and 19% (95% CI: 12, 29) for 3+0 lesions (P = .12). Conclusion Estimates of diagnostic accuracy and CDRs for PI-RADS version 2.1 categories are provided for quality benchmarking and to guide further evidence-based patient management. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Tammisetti and Jacobs in this issue.

A Feasibility Study of AI-Assisted Compressed Sensing in Prostate T2-Weighted Imaging

RATIONALE AND OBJECTIVES

To evaluate the image quality and PI-RADS scoring performance of prostate T2-weighted imaging (T2WI) based on AI-assisted compressed sensing (ACS).

MATERIALS AND METHODS

In this prospective study, adult male urological outpatients or inpatients underwent prostate MRI, including T2WI, diffusion-weighted imaging and apparent diffusion coefficient maps. Three accelerated scanning protocols using parallel imaging (PI) and ACS: T2WIPI, T2WIACS1 and T2WIACS2 were evaluated through comparative analysis. Quantitative analysis included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), slope profile, and edge rise distance (ERD). Image quality was qualitatively assessed using a five-point Likert scale (ranging from 1 = non-diagnostic to 5 = excellent). PI-RADS scores were determined for the largest or most suspicious lesions in each patient. The Friedman test and one-way ANOVA with post hoc tests were utilized for group comparisons, with statistical significance set at P < 0.05.

RESULTS

This study included 40 participants. Compared to PI, ACS reduced acquisition time by over 50%, significantly enhancing the CNR of sagittal and axial T2WI (P < 0.05), significantly improving the image quality of sagittal and axial T2WI (P < 0.05). No significant differences were observed in slope profile, ERD, and PI-RADS scores between groups (P > 0.05).

CONCLUSION

ACS reduced prostate T2WI acquisition time by half while improving image quality without affecting PI-RADS scores.

Deep Learning Reconstruction of Prospectively Accelerated MRI of the Pancreas: Clinical Evaluation of Shortened Breath-Hold Examinations With Dixon Fat Suppression

OBJECTIVE

Deep learning (DL)-enabled magnetic resonance imaging (MRI) reconstructions can enable shortening of breath-hold examinations and improve image quality by reducing motion artifacts. Prospective studies with DL reconstructions of accelerated MRI of the upper abdomen in the context of pancreatic pathologies are lacking. In a clinical setting, the purpose of this study is to investigate the performance of a novel DL-based reconstruction algorithm in T1-weighted volumetric interpolated breath-hold examinations with partial Fourier sampling and Dixon fat suppression (hereafter, VIBE-DixonDL). The objective is to analyze its impact on acquisition time, image sharpness and quality, diagnostic confidence, pancreatic lesion conspicuity, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR).

METHODS

This prospective single-center study included participants with various pancreatic pathologies who gave written consent from January 2023 to September 2023. During the same session, each participant underwent 2 MRI acquisitions using a 1.5 T scanner: conventional precontrast and postcontrast T1-weighted VIBE acquisitions with Dixon fat suppression (VIBE-Dixon, reference standard) using 4-fold parallel imaging acceleration and 6-fold accelerated VIBE-Dixon acquisitions with partial Fourier sampling utilizing a novel DL reconstruction tailored to the acquisition. A qualitative image analysis was performed by 4 readers. Acquisition time, image sharpness, overall image quality, image noise and artifacts, diagnostic confidence, as well as pancreatic lesion conspicuity and size were compared. Furthermore, a quantitative analysis of SNR and CNR was performed.

RESULTS

Thirty-two participants were evaluated (mean age ± SD, 62 ± 19 years; 20 men). The VIBE-DixonDL method enabled up to 52% reduction in average breath-hold time (7 seconds for VIBE-DixonDL vs 15 seconds for VIBE-Dixon, P < 0.001). A significant improvement of image sharpness, overall image quality, diagnostic confidence, and pancreatic lesion conspicuity was observed in the images recorded using VIBE-DixonDL (P < 0.001). Furthermore, a significant reduction of image noise and motion artifacts was noted in the images recorded using the VIBE-DixonDL technique (P < 0.001). In addition, for all readers, there was no evidence of a difference in lesion size measurement between VIBE-Dixon and VIBE-DixonDL. Interreader agreement between VIBE-Dixon and VIBE-DixonDL regarding lesion size was excellent (intraclass correlation coefficient, >90). Finally, a statistically significant increase of pancreatic SNR in VIBE-DIXONDL was observed in both the precontrast (P = 0.025) and postcontrast images (P < 0.001). Also, an increase of splenic SNR in VIBE-DIXONDL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images (P = 0.34 and P = 0.003, respectively). Similarly, an increase of pancreas CNR in VIBE-DIXONDL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images (P = 0.557 and P = 0.026, respectively).

CONCLUSIONS

The prospectively accelerated, DL-enhanced VIBE with Dixon fat suppression was clinically feasible. It enabled a 52% reduction in breath-hold time and provided superior image quality, diagnostic confidence, and pancreatic lesion conspicuity. This technique might be especially useful for patients with limited breath-hold capacity.

Strategies for improving image quality in prostate MRI

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.

AI-accelerated prostate MRI: a systematic review

BACKGROUND

Prostate cancer ranks among the most prevalent cancers affecting men globally. While conventional MRI serves as a diagnostic tool, its extended acquisition time, associated costs, and strain on healthcare systems, underscore the necessity for more efficient methods. The emergence of AI-acceleration in prostate MRI offers promise to mitigate these challenges.

METHODS

A systematic review of studies looking at AI-accelerated prostate MRI was conducted, with a focus on acquisition time along with various qualitative and quantitative measurements.

RESULTS

Two primary findings were observed. Firstly, all studies indicated that AI-acceleration in MRI achieved notable reductions in acquisition times without compromising image quality. This efficiency offers potential clinical advantages, including reduced scan durations, improved scheduling, diminished patient discomfort, and economic benefits. Secondly, AI demonstrated a beneficial effect in reducing or maintaining artefact levels in T2-weighted images despite this accelerated acquisition time. Inconsistent results were found in all other domains, which were likely influenced by factors such as heterogeneity in methodologies, variability in AI models, and diverse radiologist profiles. These variances underscore the need for larger, more robust studies, standardization, and diverse training datasets for AI models.

CONCLUSION

The integration of AI-acceleration in prostate MRI thus far shows some promising results for efficient and enhanced scanning. These advancements may fill current gaps in early detection and prognosis. However, careful navigation and collaborative efforts are essential to overcome challenges and maximize the potential of this innovative and evolving field.

ADVANCES IN KNOWLEDGE

This article reveals overall significant reductions in acquisition time without compromised image quality in AI-accelerated prostate MRI, highlighting potential clinical and diagnostic advantages.

Reduced field-of-view DWI based on deep learning reconstruction improving diagnostic accuracy of VI-RADS for evaluating muscle invasion

OBJECTIVES

To investigate whether reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) with deep learning reconstruction (DLR) can improve the accuracy of evaluating muscle invasion using VI-RADS.

METHODS

Eighty-six bladder cancer participants who were evaluated by conventional full field-of-view (fFOV) DWI, standard rFOV (rFOVSTA) DWI, and fast rFOV with DLR (rFOVDLR) DWI were included in this prospective study. Tumors were categorized according to the vesical imaging reporting and data system (VI-RADS). Qualitative image quality scoring, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and ADC value were evaluated. Friedman test with post hoc test revealed the difference across the three DWIs. Receiver operating characteristic analysis was performed to calculate the areas under the curve (AUCs).

RESULTS

The AUC of the rFOVSTA DWI and rFOVDLR DWI were higher than that of fFOV DWI. rFOVDLR DWI reduced the acquisition time from 5:02 min to 3:25 min, and showed higher scores in overall image quality with higher CNR and SNR, compared to rFOVSTA DWI (p < 0.05). The mean ADC of all cases of rFOVSTA DWI and rFOVDLR DWI was significantly lower than that of fFOV DWI (all p < 0.05). There was no difference in mean ADC value and the AUC for evaluating muscle invasion between rFOVSTA DWI and rFOVDLR DWI (p > 0.05).

CONCLUSIONS

rFOV DWI with DLR can improve the diagnostic accuracy of fFOV DWI for evaluating muscle invasion. Applying DLR to rFOV DWI reduced the acquisition time and improved overall image quality while maintaining ADC value and diagnostic accuracy.

CRITICAL RELEVANCE STATEMENT

The diagnostic performance and image quality of full field-of-view DWI, reduced field-of-view (rFOV) DWI with and without DLR were compared. DLR would benefit the wide clinical application of rFOV DWI by reducing the acquisition time and improving the image quality.

KEY POINTS

Deep learning reconstruction (DLR) can reduce scan time and improve image quality. Reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) with DLR showed better diagnostic performances than full field-of-view DWI. There was no difference of diagnostic accuracy between rFOV DWI with DLR and standard rFOV DWI.

Picture Perfect: The Status of Image Quality in Prostate MRI

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.

Performance of an ultra-fast deep-learning accelerated MRI screening protocol for prostate cancer compared to a standard multiparametric protocol

OBJECTIVES

To establish and evaluate an ultra-fast MRI screening protocol for prostate cancer (PCa) in comparison to the standard multiparametric (mp) protocol, reducing scan time and maintaining adequate diagnostic performance.

MATERIALS AND METHODS

This prospective single-center study included consecutive biopsy-naïve patients with suspected PCa between December 2022 and March 2023. A PI-RADSv2.1 conform mpMRI protocol was acquired in a 3 T scanner (scan time: 25 min 45 sec). In addition, two deep-learning (DL) accelerated sequences (T2- and diffusion-weighted) were acquired, serving as a screening protocol (scan time: 3 min 28 sec). Two readers evaluated image quality and the probability of PCa regarding PI-RADSv2.1 scores in two sessions. The diagnostic performance of the screening protocol with mpMRI serving as the reference standard was derived. Inter- and intra-reader agreements were evaluated using weighted kappa statistics.

RESULTS

We included 77 patients with 97 lesions (mean age: 66 years; SD: 7.7). Diagnostic performance of the screening protocol was excellent with a sensitivity and specificity of 100%/100% and 89%/98% (cut-off ≥ PI-RADS 4) for reader 1 (R1) and reader 2 (R2), respectively. Mean image quality was 3.96 (R1) and 4.35 (R2) for the standard protocol vs. 4.74 and 4.57 for the screening protocol (p < 0.05). Inter-reader agreement was moderate (κ: 0.55) for the screening protocol and substantial (κ: 0.61) for the multiparametric protocol.

CONCLUSION

The ultra-fast screening protocol showed similar diagnostic performance and better imaging quality compared to the mpMRI in under 15% of scan time, improving efficacy and enabling the implementation of screening protocols in clinical routine.

CLINICAL RELEVANCE STATEMENT

The ultra-fast protocol enables examinations without contrast administration, drastically reducing scan time to 3.5 min with similar diagnostic performance and better imaging quality. This facilitates patient-friendly, efficient examinations and addresses the conflict of increasing demand for examinations at currently exhausted capacities.

KEY POINTS

Time-consuming MRI protocols are in conflict with an expected increase in examinations required for prostate cancer screening. An ultra-fast MRI protocol shows similar performance and better image quality compared to the standard protocol. Deep-learning acceleration facilitates efficient and patient-friendly examinations, thus improving prostate cancer screening capacity.

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

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.

Deep learning image reconstruction of diffusion-weighted imaging in evaluation of prostate cancer focusing on its clinical implications

Background

Image-based assessment of prostate cancer (PCa) is increasingly emphasized in the diagnostic workflow for selecting biopsy targets and possibly predicting clinically significant prostate cancer (csPCa). Assessment is based on Prostate Imaging-Reporting and Data System (PI-RADS) which is largely dependent on T2-weighted image (T2WI) and diffusion weighted image (DWI). This study aims to determine whether deep learning reconstruction (DLR) can improve the image quality of DWI and affect the assessment of PI-RADS ≥4 in patients with PCa.

Methods

In this retrospective study, 3.0T post-biopsy prostate magnetic resonance imaging (MRI) of 70 patients with PCa in Korea University Ansan Hospital from November 2021 to July 2022 was reconstructed with and without using DLR. Four DWI image sets were made: (I) conventional DWI (CDWI): DWI with acceleration factor 2 and conventional parallel imaging reconstruction, (II) DL1: DWI with acceleration factor 2 using DLR, (III) DL2: DWI with acceleration factor 3 using DLR, and (IV) DL3: DWI with acceleration factor 3 and half average b-value using DLR. Apparent diffusion coefficient (ADC) value, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured by one reviewer, while two reviewers independently assessed overall image quality, noise, and lesion conspicuity using a four-point visual scoring system from each DWI image set. Two reviewers also performed PI-RADSv2.1 scoring on lesions suspected of malignancy.

Results

A total of 70 patients (mean age, 70.8±9.7 years) were analyzed. The image acquisition time was 4:46 min for CDWI and DL1, 3:40 min for DL2, and 2:00 min for DL3. DL1 and DL2 images resulted in better lesion conspicuity compared to CDWI images assessed by both readers (P<0.05). DLR resulted in a significant increase in SNR, from 38.4±14.7 in CDWI to 56.9±21.0 in DL1. CNR increased from 25.1±11.5 in CDWI to 43.1±17.8 in DL1 (P<0.001). PI-RADS v2.1 scoring for PCa lesions was more agreeable with the DL1 reconstruction method than with CDWI (κ value CDWI, DL1; 0.40, 0.61, respectively). A statistically significant number of lesions were upgraded from PI-RADS <4 in CDWI image to PI-RADS ≥4 in DL1 images for both readers (P<0.05). Most of the PI-RADS upgraded lesions were from higher than unfavorable intermediate-risk groups according to the 2023 National Comprehensive Cancer Network guidelines with statistically significant difference of marginal probability in DL1 and DL2 for both readers (P<0.05).

Conclusions

DLR in DWI for PCa can provide options for improving image quality with a significant impact on PI-RADS evaluation or about a 23% reduction in acquisition time without compromising image quality.

Fast MRI Reconstruction Using Deep Learning-based Compressed Sensing: A Systematic Review

Magnetic resonance imaging (MRI) has revolutionized medical imaging, providing a non-invasive and highly detailed look into the human body. However, the long acquisition times of MRI present challenges, causing patient discomfort, motion artifacts, and limiting real-time applications. To address these challenges, researchers are exploring various techniques to reduce acquisition time and improve the overall efficiency of MRI. One such technique is compressed sensing (CS), which reduces data acquisition by leveraging image sparsity in transformed spaces. In recent years, deep learning (DL) has been integrated with CS-MRI, leading to a new framework that has seen remarkable growth. DL-based CS-MRI approaches are proving to be highly effective in accelerating MR imaging without compromising image quality. This review comprehensively examines DL-based CS-MRI techniques, focusing on their role in increasing MR imaging speed. We provide a detailed analysis of each category of DL-based CS-MRI including end-to-end, unroll optimization, self-supervised, and federated learning. Our systematic review highlights significant contributions and underscores the exciting potential of DL in CS-MRI. Additionally, our systematic review efficiently summarizes key results and trends in DL-based CS-MRI including quantitative metrics, the dataset used, acceleration factors, and the progress of and research interest in DL techniques over time. Finally, we discuss potential future directions and the importance of DL-based CS-MRI in the advancement of medical imaging. To facilitate further research in this area, we provide a GitHub repository that includes up-to-date DL-based CS-MRI publications and publicly available datasets - https://github.com/mosaf/Awesome-DL-based-CS-MRI.

FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging

Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use. In May 2023, fastMRI was expanded to include biparametric (T2- and diffusion-weighted) prostate MRI data from a clinical population. Biparametric MRI plays a vital role in the diagnosis and management of prostate cancer. Advances in imaging methods, such as reconstructing under-sampled data from accelerated acquisitions, can improve cost-effectiveness and accessibility of prostate MRI. Raw k-space data, reconstructed images and slice, volume and exam level annotations for likelihood of prostate cancer are provided in this dataset for 47468 slices corresponding to 1560 volumes from 312 patients. This dataset facilitates AI and algorithm development for prostate image reconstruction, with the ultimate goal of enhancing prostate cancer diagnosis.

Deep Learning Accelerated Image Reconstruction of Fluid-Attenuated Inversion Recovery Sequence in Brain Imaging: Reduction of Acquisition Time and Improvement of Image Quality

RATIONALE AND OBJECTIVES

Fluid-attenuated inversion recovery (FLAIR) imaging is playing an increasingly significant role in the detection of brain metastases with a concomitant increase in the number of magnetic resonance imaging (MRI) examinations. Therefore, the purpose of this study was to investigate the impact on image quality and diagnostic confidence of an innovative deep learning-based accelerated FLAIR (FLAIRDLR) sequence of the brain compared to conventional (standard) FLAIR (FLAIRS) imaging.

MATERIALS AND METHODS

Seventy consecutive patients with staging cerebral MRIs were retrospectively enrolled in this single-center study. The FLAIRDLR was conducted using the same MRI acquisition parameters as the FLAIRS sequence, except for a higher acceleration factor for parallel imaging (from 2 to 4), which resulted in a shorter acquisition time of 1:39 minute instead of 2:40 minutes (-38%). Two specialized neuroradiologists evaluated the imaging datasets using a Likert scale that ranged from 1 to 4, with 4 indicating the best score for the following parameters: sharpness, lesion demarcation, artifacts, overall image quality, and diagnostic confidence. Additionally, the image preference of the readers and the interreader agreement were assessed.

RESULTS

The average age of the patients was 63 ± 11years. FLAIRDLR exhibited significantly less image noise than FLAIRS, with P-values of< .001 and< .05, respectively. The sharpness of the images and the ability to detect lesions were rated higher in FLAIRDLR, with a median score of 4 compared to a median score of 3 in FLAIRS (P-values of<.001 for both readers). In terms of overall image quality, FLAIRDLR was rated superior to FLAIRS, with a median score of 4 vs 3 (P-values of<.001 for both readers). Both readers preferred FLAIRDLR in 68/70 cases.

CONCLUSION

The feasibility of deep learning FLAIR brain imaging was shown with additional 38% reduction in examination time compared to standard FLAIR imaging. Furthermore, this technique has shown improvement in image quality, noise reduction, and lesion demarcation.

Update on Optimization of Prostate MR Imaging Technique and Image Quality

Prostate MR imaging quality has improved dramatically over recent times, driven by advances in hardware, software, and improved functional imaging techniques. MRI now plays a key role in prostate cancer diagnostic work-up, but outcomes of the MRI-directed pathway are heavily dependent on image quality and optimization. MR sequences can be affected by patient-related degradations relating to motion and susceptibility artifacts which may enable only partial mitigation. In this Review, we explore issues relating to prostate MRI acquisition and interpretation, mitigation strategies at a patient and scanner level, PI-QUAL reporting, and future directions in image quality, including artificial intelligence solutions.

Accelerated Diffusion-Weighted Imaging in 3 T Breast MRI Using a Deep Learning Reconstruction Algorithm With Superresolution Processing: A Prospective Comparative Study

OBJECTIVES

Diffusion-weighted imaging (DWI) enhances specificity in multiparametric breast MRI but is associated with longer acquisition time. Deep learning (DL) reconstruction may significantly shorten acquisition time and improve spatial resolution. In this prospective study, we evaluated acquisition time and image quality of a DL-accelerated DWI sequence with superresolution processing (DWI DL ) in comparison to standard imaging including analysis of lesion conspicuity and contrast of invasive breast cancers (IBCs), benign lesions (BEs), and cysts.

MATERIALS AND METHODS

This institutional review board-approved prospective monocentric study enrolled participants who underwent 3 T breast MRI between August and December 2022. Standard DWI (DWI STD ; single-shot echo-planar DWI combined with reduced field-of-view excitation; b-values: 50 and 800 s/mm 2 ) was followed by DWI DL with similar acquisition parameters and reduced averages. Quantitative image quality was analyzed for region of interest-based signal-to-noise ratio (SNR) on breast tissue. Apparent diffusion coefficient (ADC), SNR, contrast-to-noise ratio, and contrast (C) values were calculated for biopsy-proven IBCs, BEs, and for cysts. Two radiologists independently assessed image quality, artifacts, and lesion conspicuity in a blinded independent manner. Univariate analysis was performed to test differences and interrater reliability.

RESULTS

Among 65 participants (54 ± 13 years, 64 women) enrolled in the study, the prevalence of breast cancer was 23%. Average acquisition time was 5:02 minutes for DWI STD and 2:44 minutes for DWI DL ( P < 0.001). Signal-to-noise ratio measured in breast tissue was higher for DWI STD ( P < 0.001). The mean ADC values for IBC were 0.77 × 10 -3 ± 0.13 mm 2 /s in DWI STD and 0.75 × 10 -3 ± 0.12 mm 2 /s in DWI DL without significant difference when sequences were compared ( P = 0.32). Benign lesions presented with mean ADC values of 1.32 × 10 -3 ± 0.48 mm 2 /s in DWI STD and 1.39 × 10 -3 ± 0.54 mm 2 /s in DWI DL ( P = 0.12), and cysts presented with 2.18 × 10 -3 ± 0.49 mm 2 /s in DWI STD and 2.31 × 10 -3 ± 0.43 mm 2 /s in DWI DL . All lesions presented with significantly higher contrast in the DWI DL ( P < 0.001), whereas SNR and contrast-to-noise ratio did not differ significantly between DWI STD and DWI DL regardless of lesion type. Both sequences demonstrated a high subjective image quality (29/65 for DWI STD vs 20/65 for DWI DL ; P < 0.001). The highest lesion conspicuity score was observed more often for DWI DL ( P < 0.001) for all lesion types. Artifacts were scored higher for DWI DL ( P < 0.001). In general, no additional artifacts were noted in DWI DL . Interrater reliability was substantial to excellent (k = 0.68 to 1.0).

CONCLUSIONS

DWI DL in breast MRI significantly reduced scan time by nearly one half while improving lesion conspicuity and maintaining overall image quality in a prospective clinical cohort.

Utility of accelerated T2-weighted turbo spin-echo imaging with deep learning reconstruction in female pelvic MRI: a multi-reader study

OBJECTIVES

To determine the clinical feasibility of T2-weighted turbo spin-echo (T2-TSE) imaging with deep learning reconstruction (DLR) in female pelvic MRI compared with conventional T2 TSE in terms of image quality and scan time.

METHODS

Between May 2021 and September 2021, 52 women (mean age, 44 years ± 12) who underwent 3-T pelvic MRI with additional T2-TSE using a DLR algorithm were included in this single-center prospective study with patient's informed consents. Conventional, DLR, and DLR T2-TSE images with reduced scan times were independently assessed and compared by four radiologists. The overall image quality, differentiation of anatomic details, lesion conspicuity, and artifacts were evaluated using a 5-point scale. Inter-observer agreement of the qualitative scores was compared and reader protocol preferences were then evaluated.

RESULTS

In the qualitative analysis of all readers, fast DLR T2-TSE showed significantly better overall image quality, differentiation of anatomic regions, lesion conspicuity, and lesser artifacts than conventional T2-TSE and DLR T2-TSE, despite approximately 50% reduction in scan time (all p < 0.05). The inter-reader agreement for the qualitative analysis was moderate to good. All readers preferred DLR over conventional T2-TSE regardless of scan time and preferred fast DLR T2-TSE (57.7-78.8%), except for one who preferred DLR over fast DLR T2-TSE (53.8% vs. 46.1%).

CONCLUSION

In female pelvic MRI, image quality and accelerated image acquisition for T2-TSE can be significantly improved by using DLR compared to conventional T2-TSE. Fast DLR T2-TSE was non-inferior to DLR T2-TSE in terms of reader preference and image quality.

CLINICAL RELEVANCE STATEMENT

DLR of T2-TSE in female pelvic MRI enables fast imaging along with maintaining optimal image quality compared with parallel imaging-based conventional T2-TSE.

KEY POINTS

• Conventional T2 turbo spin-echo based on parallel imaging has limitations for accelerated image acquisition while maintaining good image quality. • Deep learning image reconstruction showed better image quality in both images obtained using the same or accelerated image acquisition parameters compared with conventional T2 turbo spin-echo in female pelvic MRI. • Deep learning image reconstruction enables accelerated image acquisition while maintaining good image quality in the T2-TSE of female pelvic MRI.

Development trends and knowledge framework in the application of magnetic resonance imaging in prostate cancer: a bibliometric analysis from 1984 to 2022

Background

Prostate cancer (PCa) is the most common tumor of the male genitourinary system. With the development of imaging technology, the role of magnetic resonance imaging (MRI) in the management of PCa is increasing. The present study summarizes research on the application of MRI in the field of PCa using bibliometric analysis and predicts future research hotspots.

Methods

Articles regarding the application of MRI in PCa between January 1, 1984 and June 30, 2022 were selected from the Web of Science Core Collection (WoSCC) on November 6, 2022. Microsoft Excel 2016 and the Bibliometrix Biblioshiny R-package software were used for data analysis and bibliometric indicator extraction. CiteSpace (version 6.1.R3) was used to visualize literature feature clustering, including co-occurrence analysis of countries, institutions, authors, references, and burst keywords analysis.

Results

A total of 10,230 articles were included in the study. Turkbey was the most prolific author. The USA was the most productive country and had strong partnerships with other countries. The most productive institution was Memorial Sloan Kettering Cancer Center. Journal of Magnetic Resonance Imaging and Radiology were the most productive and highest impact factor (IF) journals in the field, respectively. Timeline views showed that "#1 multiparametric magnetic resonance imaging", "#4 pi-rads", and "#8 psma" were currently the latest research hotspots. Keywords burst analysis showed that "machine learning", "psa density", "multi parametric mri", "deep learning", and "artificial intelligence" were the most frequently used keywords in the past 3 years.

Conclusions

MRI has a wide range of applications in PCa. The USA is the leading country in this field, with a concentration of highly productive and high-level institutions. Meanwhile, it can be projected that "deep learning", "radiomics", and "artificial intelligence" will be research hotspots in the future.

Comparison of a Deep Learning-Accelerated vs. Conventional T2-Weighted Sequence in Biparametric MRI of the Prostate

BACKGROUND

Demand for prostate MRI is increasing, but scan times remain long even in abbreviated biparametric MRIs (bpMRI). Deep learning can be leveraged to accelerate T2-weighted imaging (T2WI).

PURPOSE

To compare conventional bpMRIs (CL-bpMRI) with bpMRIs including a deep learning-accelerated T2WI (DL-bpMRI) in diagnosing prostate cancer.

STUDY TYPE

Retrospective.

POPULATION

Eighty consecutive men, mean age 66 years (47-84) with suspected prostate cancer or prostate cancer on active surveillance who had a prostate MRI from December 28, 2020 to April 28, 2021 were included. Follow-up included prostate biopsy or stability of prostate-specific antigen (PSA) for 1 year.

FIELD STRENGTH AND SEQUENCES

A 3 T MRI. Conventional axial and coronal T2 turbo spin echo (CL-T2), 3-fold deep learning-accelerated axial and coronal T2-weighted sequence (DL-T2), diffusion weighted imaging (DWI) with b = 50 sec/mm2 , 1000 sec/mm2 , calculated b = 1500 sec/mm2 .

ASSESSMENT

CL-bpMRI and DL-bpMRI including the same conventional diffusion-weighted imaging (DWI) were presented to three radiologists (blinded to acquisition method) and to a deep learning computer-assisted detection algorithm (DL-CAD). The readers evaluated image quality using a 4-point Likert scale (1 = nondiagnostic, 4 = excellent) and graded lesions using Prostate Imaging Reporting and Data System (PI-RADS) v2.1. DL-CAD identified and assigned lesions of PI-RADS 3 or greater.

STATISTICAL TESTS

Quality metrics were compared using Wilcoxon signed rank test, and area under the receiver operating characteristic curve (AUC) were compared using Delong's test.

SIGNIFICANCE

P = 0.05.

RESULTS

Eighty men were included (age: 66 ± 9 years; 17/80 clinically significant prostate cancer). Overall image quality results by the three readers (CL-T2, DL-T2) are reader 1: 3.72 ± 0.53, 3.89 ± 0.39 (P = 0.99); reader 2: 3.33 ± 0.82, 3.31 ± 0.74 (P = 0.49); reader 3: 3.67 ± 0.63, 3.51 ± 0.62. In the patient-based analysis, the reader results of AUC are (CL-bpMRI, DL-bpMRI): reader 1: 0.77, 0.78 (P = 0.98), reader 2: 0.65, 0.66 (P = 0.99), reader 3: 0.57, 0.60 (P = 0.52). Diagnostic statistics from DL-CAD (CL-bpMRI, DL-bpMRI) are sensitivity (0.71, 0.71, P = 1.00), specificity (0.59, 0.44, P = 0.05), positive predictive value (0.23, 0.24, P = 0.25), negative predictive value (0.88, 0.88, P = 0.48).

CONCLUSION

Deep learning-accelerated T2-weighted imaging may potentially be used to decrease acquisition time for bpMRI.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 2.

Evaluation of late gadolinium enhancement cardiac MRI using deep learning reconstruction

BACKGROUND

Deep learning (DL)-based methods have been used to improve the imaging quality of magnetic resonance imaging (MRI) by denoising.

PURPOSE

To assess the effects of DL-based MR reconstruction (DLR) method on late gadolinium enhancement (LGE) image quality.

MATERIAL AND METHODS

A total of 85 patients who underwent cardiovascular magnetic resonance (CMR) examination, including LGE imaging using conventional construction and DLR with varying levels of noise reduction (NR) levels, were included. Both magnitude LGE (MLGE) and phase-sensitive LGE (PSLGE) images were reviewed independently by double-blinded observers who used a 5-point Likert scale for multiple measures regarding image quality. Meanwhile, the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness of images were calculated and compared between conventional LGE imaging and DLR LGE imaging.

RESULTS

Both MLGE and PSLGE with DLR at 50% and 75% noise reduction levels received significantly higher scores than conventional imaging for overall imaging quality (all P < 0.01). In addition, the SNR, CNR, and edge sharpness of all DLR LGE imaging are higher than conventional imaging (all P < 0.01). The highest subjective score and best image quality is obtained when the DLR noise reduction level is at 75%.

CONCLUSION

DLR reduced image noise while improving image contrast and sharpness in the cardiovascular LGE imaging.

Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in in vitro and in vivo studies

PURPOSE

Deep learning reconstruction (DLR) to improve imaging quality has already been introduced, but no studies have evaluated the effect of DLR on diffusion-weighted imaging (DWI) or intravoxel incoherent motion (IVIM) in in vitro or in vivo studies. The purpose of this study was to determine the effect of DLR for magnetic resonance imaging (MRI) in terms of image quality improvement, apparent diffusion coefficient (ADC) assessment, and IVIM index evaluation on DWI through in vitro and in vivo studies.

METHODS

For the in vitro study, a phantom recommended by the Quantitative Imaging Biomarkers Alliance was scanned and reconstructed with and without DLR, and 15 patients with brain tumors with normal-appearing gray and white matter examined using IVIM and reconstructed with and without DLR were included in the in vivo study. The ADCs of all phantoms for DWI with and without DLR, as well as the coefficient of variation percentage (CV%), and ADCs and IVIM indexes for each participant, were evaluated based on DWI with and without DLR by means of region-of-interest measurements. For the in vitro study, using the mean ADCs for all phantoms, a t-test was adopted to compare DWI with and without DLR. For the in vivo study, a Wilcoxon signed-rank test was used to compare the CV% between the two types of DWI. In addition, the Wilcoxon signed-rank test was used to compare the ADC, true diffusion coefficient (D), pseudodiffusion coefficient (D*), and percentage of water molecules in micro perfusion within 1 voxel (f) with and without DLR; the limits of agreement of each parameter were determined through a Bland-Altman analysis.

RESULTS

The in vitro study identified no significant differences between the ADC values for DWI with and without DLR (P > 0.05), and the CV% was significantly different for DWI with and without DLR (P < 0.05) when b values ≥250 s/mm2 were used. The in vivo study revealed that D* and f with and without DLR were significantly different (P < 0.001). The limits of agreement of the ADC, D, and D* values for DWI with and without DLR were determined as 0.00 ± 0.51 × 10-3, 0.00 ± 0.06 × 10-3, and 1.13 ± 4.04 × 10-3 mm2/s, respectively. The limits of agreement of the f values for DWI with and without DLR were determined as -0.01 ± 0.07.

CONCLUSION

Deep learning reconstruction for MRI has the potential to significantly improve DWI quality at higher b values. It has some effect on D* and f values in the IVIM index evaluation, but ADC and D values are less affected by DLR.

Deep Learning Super-Resolution Reconstruction for Fast and Motion-Robust T2-weighted Prostate MRI

Background Deep learning (DL) reconstructions can enhance image quality while decreasing MRI acquisition time. However, DL reconstruction methods combined with compressed sensing for prostate MRI have not been well studied. Purpose To use an industry-developed DL algorithm to reconstruct low-resolution T2-weighted turbo spin-echo (TSE) prostate MRI scans and compare these with standard sequences. Materials and Methods In this prospective study, participants with suspected prostate cancer underwent prostate MRI with a Cartesian standard-resolution T2-weighted TSE sequence (T2C) and non-Cartesian standard-resolution T2-weighted TSE sequence (T2NC) between August and November 2022. Additionally, a low-resolution Cartesian DL-reconstructed T2-weighted TSE sequence (T2DL) with compressed sensing DL denoising and resolution upscaling reconstruction was acquired. Image sharpness was assessed qualitatively by two readers using a five-point Likert scale (from 1 = nondiagnostic to 5 = excellent) and quantitatively by calculating edge rise distance. The Friedman test and one-way analysis of variance with post hoc Bonferroni and Tukey tests, respectively, were used for group comparisons. Prostate Imaging Reporting and Data System (PI-RADS) score agreement between sequences was compared by using Cohen κ. Results This study included 109 male participants (mean age, 68 years ± 8 [SD]). Acquisition time of T2DL was 36% and 29% lower compared with that of T2C and T2NC (mean duration, 164 seconds ± 20 vs 257 seconds ± 32 and 230 seconds ± 28; P < .001 for both). T2DL showed improved image sharpness compared with standard sequences using both qualitative (median score, 5 [IQR, 4-5] vs 4 [IQR, 3-4] for T2C and 4 [IQR, 3-4] for T2NC; P < .001 for both) and quantitative (mean edge rise distance, 0.75 mm ± 0.39 vs 1.15 mm ± 0.68 for T2C and 0.98 mm ± 0.65 for T2NC; P < .001 and P = .01) methods. PI-RADS score agreement between T2NC and T2DL was excellent (κ range, 0.92-0.94 [95% CI: 0.87, 0.98]). Conclusion DL reconstruction of low-resolution T2-weighted TSE sequences enabled accelerated acquisition times and improved image quality compared with standard acquisitions while showing excellent agreement with conventional sequences for PI-RADS ratings. Clinical trial registration no. NCT05820113 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Turkbey in this issue.

Assessment of deep learning-based reconstruction on T2-weighted and diffusion-weighted prostate MRI image quality

PURPOSE

To evaluate the impact of a commercially available deep learning-based reconstruction (DLR) algorithm with varying combinations of DLR noise reduction settings and imaging parameters on quantitative and qualitative image quality, PI-RADS classification and examination time in prostate T2-weighted (T2WI) and diffusion-weighted (DWI) imaging.

METHOD

Forty patients were included. Standard-of-care (SoC) prostate MRI sequences including T2WI and DWI were reconstructed without and with different DLR de-noising levels (low, medium, high). In addition, faster T2WI(Fast) and DWI(Fast) sequences, and a higher resolution T2WI(HR) sequence were evaluated. Quantitative analysis included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and apparent diffusion coefficient (ADC) values. Two radiologists performed qualitative analysis, independently evaluating imaging datasets using 5-point scoring scales for image quality and artifacts. PI-RADS category assignment was also performed by the more experienced radiologist.

RESULTS

All DLR levels resulted in significantly higher SNR and CNR compared to the DLR(off) acquisitions. DLR allowed the acquisition time to be reduced by 33% for T2WI(Fast) and 49% for DWI(Fast) compared to SoC, without affecting image quality, whilst T2WI(HR) with DLR allowed for a 73% increase in spatial resolution in the phase encode direction compared to SoC. The inter-reader agreement for image quality and artifact scores was substantial for all subjective measurements on T2WI and DWI. The T2WI(Fast) protocol with DLR(medium) and DWI(Fast) with DLR(low) received the highest qualitative quality score.

CONCLUSION

DLR can reduce T2WI and DWI acquisition time and increase SNR and CNR without compromising image quality or altering PI-RADS classification.

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

Prostate MRI plays an important role in imaging the prostate gland and surrounding tissues, particularly in the diagnosis and management of prostate cancer. With the widespread adoption of multiparametric magnetic resonance imaging in recent years, the concerns surrounding the variability of imaging quality have garnered increased attention. Several factors contribute to the inconsistency of image quality, such as acquisition parameters, scanner differences and interobserver variabilities. While efforts have been made to standardize image acquisition and interpretation via the development of systems, such as PI-RADS and PI-QUAL, the scoring systems still depend on the subjective experience and acumen of humans. Artificial intelligence (AI) has been increasingly used in many applications, including medical imaging, due to its ability to automate tasks and lower human error rates. These advantages have the potential to standardize the tasks of image interpretation and quality control of prostate MRI. Despite its potential, thorough validation is required before the implementation of AI in clinical practice. In this article, we explore the opportunities and challenges of AI, with a focus on the interpretation and quality of prostate MRI.

Accelerated diffusion-weighted imaging of the prostate using deep learning image reconstruction: A retrospective comparison with standard diffusion-weighted imaging

PURPOSE

Routine multiparametric MRI of the prostate reduces overtreatment and increases sensitivity in the diagnosis of the most common solid cancer in men. However, the capacity of MRI systems is limited. Here we investigate the ability of deep learning image reconstruction to accelerate time consuming diffusion-weighted imaging (DWI) acquisition while maintaining diagnostic image quality.

METHOD

In this retrospective study, raw data of DWI sequences of consecutive patients undergoing MRI of the prostate at a tertiary care hospital in Germany were reconstructed using standard and deep learning reconstruction. To simulate a shortening of acquisition times by 39 %, one instead of two and six instead of ten averages were used in the reconstruction of b = 0 and 1000 s/mm2 images, respectively. Image quality was assessed by three radiologists and objective image quality metrics.

RESULTS

After the application of exclusion criteria, 35 out of 147 patients examined between September 2022 and January 2023 were included in this study. The radiologists perceived less image noise on deep learning reconstructed images at b = 0 s/mm2 images and ADC maps with good inter-reader agreement. Signal-to-noise ratios were similar overall with discretely reduced values in the transitional zone after deep learning reconstruction.

CONCLUSIONS

An acquisition time reduction of 39 % without loss in image quality is feasible in DWI of the prostate when using deep learning image reconstruction.

Prostate MRI and image Quality: It is time to take stock

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.

Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI

Background MRI is a powerful diagnostic tool with a long acquisition time. Recently, deep learning (DL) methods have provided accelerated high-quality image reconstructions from undersampled data, but it is unclear if DL image reconstruction can be reliably translated to everyday clinical practice. Purpose To determine the diagnostic equivalence of prospectively accelerated DL-reconstructed knee MRI compared with conventional accelerated MRI for evaluating internal derangement of the knee in a clinical setting. Materials and Methods A DL reconstruction model was trained with images from 298 clinical 3-T knee examinations. In a prospective analysis, patients clinically referred for knee MRI underwent a conventional accelerated knee MRI protocol at 3 T followed by an accelerated DL protocol between January 2020 and February 2021. The equivalence of the DL reconstruction of the images relative to the conventional images for the detection of an abnormality was assessed in terms of interchangeability. Each examination was reviewed by six musculoskeletal radiologists. Analyses pertaining to the detection of meniscal or ligament tears and bone marrow or cartilage abnormalities were based on four-point ordinal scores for the likelihood of an abnormality. Additionally, the protocols were compared with use of four-point ordinal scores for each aspect of image quality: overall image quality, presence of artifacts, sharpness, and signal-to-noise ratio. Results A total of 170 participants (mean age ± SD, 45 years ± 16; 76 men) were evaluated. The DL-reconstructed images were determined to be of diagnostic equivalence with the conventional images for detection of abnormalities. The overall image quality score, averaged over six readers, was significantly better (P < .001) for the DL than for the conventional images. Conclusion In a clinical setting, deep learning reconstruction enabled a nearly twofold reduction in scan time for a knee MRI and was diagnostically equivalent with the conventional protocol. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Roemer in this issue.

Comparison of deep learning-based reconstruction of PROPELLER Shoulder MRI with conventional reconstruction

OBJECTIVE

To compare the image quality and agreement among conventional and accelerated periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI with both conventional reconstruction (CR) and deep learning-based reconstruction (DLR) methods for evaluation of shoulder.

MATERIALS AND METHODS

We included patients who underwent conventional (acquisition time, 8 min) and accelerated (acquisition time, 4 min and 24 s; 45% reduction) PROPELLER shoulder MRI using both CR and DLR methods between February 2021 and February 2022 on a 3 T MRI system. Quantitative evaluation was performed by calculating the signal-to-noise ratio (SNR). Two musculoskeletal radiologists compared the image quality using conventional sequence with CR as the reference standard. Interobserver agreement between image sets for evaluating shoulder was analyzed using weighted/unweighted kappa statistics.

RESULTS

Ninety-two patients with 100 shoulder MRI scans were included. Conventional sequence with DLR had the highest SNR (P < .001), followed by accelerated sequence with DLR, conventional sequence with CR, and accelerated sequence with CR. Comparison of image quality by both readers revealed that conventional sequence with DLR (P = .003 and P < .001) and accelerated sequence with DLR (P = .016 and P < .001) had better image quality than the conventional sequence with CR. Interobserver agreement was substantial to almost perfect for detecting shoulder abnormalities (κ = 0.600-0.884). Agreement between the image sets was substantial to almost perfect (κ = 0.691-1).

CONCLUSION

Accelerated PROPELLER with DLR showed even better image quality than conventional PROPELLER with CR and interobserver agreement for shoulder pathologies comparable to that of conventional PROPELLER with CR, despite the shorter scan time.

Deep, deep learning with BART

PURPOSE

To develop a deep-learning-based image reconstruction framework for reproducible research in MRI.

METHODS

The BART toolbox offers a rich set of implementations of calibration and reconstruction algorithms for parallel imaging and compressed sensing. In this work, BART was extended by a nonlinear operator framework that provides automatic differentiation to allow computation of gradients. Existing MRI-specific operators of BART, such as the nonuniform fast Fourier transform, are directly integrated into this framework and are complemented by common building blocks used in neural networks. To evaluate the use of the framework for advanced deep-learning-based reconstruction, two state-of-the-art unrolled reconstruction networks, namely the Variational Network and MoDL, were implemented.

RESULTS

State-of-the-art deep image-reconstruction networks can be constructed and trained using BART's gradient-based optimization algorithms. The BART implementation achieves a similar performance in terms of training time and reconstruction quality compared to the original implementations based on TensorFlow.

CONCLUSION

By integrating nonlinear operators and neural networks into BART, we provide a general framework for deep-learning-based reconstruction in MRI.

Thin-Slice Prostate MRI Enabled by Deep Learning Image Reconstruction

OBJECTIVES

Thin-slice prostate MRI might be beneficial for prostate cancer diagnostics. However, prolongation of acquisition time is a major drawback of thin-slice imaging. Therefore, the purpose of this study was to investigate the impact of a thin-slice deep learning accelerated T2-weighted (w) TSE imaging sequence (T2DLR) of the prostate as compared to conventional T2w TSE imaging (T2S).

MATERIALS AND METHODS

Thirty patients were included in this prospective study at one university center after obtaining written informed consent. T2S (3 mm slice thickness) was acquired first in three orthogonal planes followed by thin-slice T2DLR (2 mm slice thickness) in axial plane. Acquisition time of axial conventional T2S was 4:12 min compared to 4:37 min for T2DLR. Imaging datasets were evaluated by two radiologists using a Likert-scale ranging from 1-4, with 4 being the best regarding the following parameters: sharpness, lesion detectability, artifacts, overall image quality, and diagnostic confidence. Furthermore, preference of T2S versus T2DLR was evaluated.

RESULTS

The mean patient age was 68 ± 8 years. Sharpness of images and lesion detectability were rated better in T2DLR with a median of 4 versus a median of 3 in T2S (p < 0.001 for both readers). Image noise was evaluated to be significantly worse in T2DLR as compared to T2S (p < 0.001 and p = 0.021, respectively). Overall image quality was also evaluated to be superior in T2DLR versus T2S with a median of 4 versus 3 (p < 0.001 for both readers). Both readers chose T2DLR in 29 cases as their preference.

CONCLUSIONS

Thin-slice T2DLR of the prostate provides a significant improvement of image quality without significant prolongation of acquisition time.

A Faster Prostate MRI: Comparing a Novel Denoised, Single-Average T2 Sequence to the Conventional Multiaverage T2 Sequence Regarding Lesion Detection and PI-RADS Score Assessment

BACKGROUND

The T₂ w sequence is a standard component of a prostate MRI examination; however, it is time-consuming, requiring multiple signal averages to achieve acceptable image quality.

PURPOSE/HYPOTHESIS

To determine whether a denoised, single-average T₂ sequence (T₂ -R) is noninferior to the standard multiaverage T₂ sequence (T₂ -S) in terms of lesion detection and PI-RADS score assessment.

STUDY TYPE

Retrospective.

POPULATION

A total of 45 males (age range 60-75 years) who underwent clinically indicated prostate MRI examinations, 21 of whom had pathologically proven prostate cancer.

FIELD STRENGTH/SEQUENCE

A 3 T; T₂ w FSE, DWI with ADC maps, and dynamic contrast-enhanced images with color-coded perfusion maps. T₂ -R images were created from the raw data utilizing a single "average" with iterative denoising.

ASSESSMENT

Nine readers randomly assessed complete exams including T₂ -R and T₂ -S images in separate sessions. PI-RADS version 2.1 was used. All readers then compared the T₂ -R and T₂ -S images side by side to evaluate subjective preference. An additional detailed image quality assessment was performed by three senior level readers.

STATISTICAL TESTS

Generalized linear mixed effects models for differences in lesion detection, image quality features, and overall preference between T₂ -R and T₂ -S sequences. Intraclass correlation coefficients (ICC) were used to assess reader agreement for all comparisons. A significance threshold of P = 0.05 was used for all statistical tests.

RESULTS

There was no significant difference between sequences regarding identification of lesions with PI-RADS ≥3 (P = 0.10) or PI-RADS score (P = 0.77). Reader agreement was excellent for lesion identification (ICC = 0.84). There was no significant overall preference between the two sequences regarding image quality (P = 0.07, 95% CI: [-0.23, 0.01]). Reader agreement was good regarding sequence preference (ICC = 0.62).

DATA CONCLUSION

Use of single-average, denoised T₂ -weighted images was noninferior in prostate lesion detection or PI-RADS scoring when compared to standard multiaverage T₂ -weighted images.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 3.

Prospectively Accelerated T2-Weighted Imaging of the Prostate by Combining Compressed SENSE and Deep Learning in Patients with Histologically Proven Prostate Cancer

BACKGROUND

To assess the performance of prospectively accelerated and deep learning (DL) reconstructed T2-weighted (T2w) imaging in volunteers and patients with histologically proven prostate cancer (PCa).

METHODS

Prospectively undersampled T2w datasets were acquired with acceleration factors of 1.7 (reference), 3.4 and 4.8 in 10 healthy volunteers and 23 patients with histologically proven PCa. Image reconstructions using compressed SENSE (C-SENSE) and a combination of C-SENSE and DL-based artificial intelligence (C-SENSE AI) were analyzed. Qualitative image comparison was performed using a 6-point Likert scale (overall image quality, noise, motion artifacts, lesion detection, diagnostic certainty); the T2 and PI-RADS scores were compared between the two reconstructions. Additionally, quantitative image parameters were assessed (apparent SNR, apparent CNR, lesion size, line profiles).

RESULTS

All C-SENSE AI-reconstructed images received a significantly higher qualitative rating compared to the C-SENSE standard images. Analysis of the quantitative parameters supported this finding, with significantly higher aSNR and aCNR. The line profiles demonstrated a significantly steeper signal change at the border of the prostatic lesion and the adjacent normal tissue in the C-SENSE AI-reconstructed images, whereas the T2 and PI-RADS scores as well as the lesion size did not differ.

CONCLUSION

In this prospective study, we demonstrated the clinical feasibility of a novel C-SENSE AI reconstruction enabling a 58% acceleration in T2w imaging of the prostate while obtaining significantly better image quality.

Convolutional neural network-based reconstruction for acceleration of prostate T2 weighted MR imaging: a retro- and prospective study

OBJECTIVE

The aim of this study was to develop a deep neural network (DNN)-based parallel imaging reconstruction for highly accelerated 2D turbo spin echo (TSE) data in prostate MRI without quality degradation compared to conventional scans.

METHODS

155 participant data were acquired for training and testing. Two DNN models were generated according to the number of acquisitions (NAQ) of input images: DNN-N1 for NAQ = 1 and DNN-N2 for NAQ = 2. In the test data, DNN and TSE images were compared by quantitative error metrics. The visual appropriateness of DNN reconstructions on accelerated scans (DNN-N1 and DNN-N2) and conventional scans (TSE-Conv) was assessed for nine parameters by two radiologists. The lesion detection was evaluated at DNNs and TES-Conv by prostate imaging-reporting and data system.

RESULTS

The scan time was reduced by 71% at NAQ = 1, and 42% at NAQ = 2. Quantitative evaluation demonstrated the better error metrics of DNN images (29-43% lower NRMSE, 4-13% higher structure similarity index, and 2.8-4.8 dB higher peak signal-to-noise ratio; p < 0.001) than TSE images. In the assessment of the visual appropriateness, both radiologists evaluated that DNN-N2 showed better or comparable performance in all parameters compared to TSE-Conv. In the lesion detection, DNN images showed almost perfect agreement (κ > 0.9) scores with TSE-Conv.

CONCLUSIONS

DNN-based reconstruction in highly accelerated prostate TSE imaging showed comparable quality to conventional TSE.

ADVANCES IN KNOWLEDGE

Our framework reduces the scan time by 42% of conventional prostate TSE imaging without sequence modification, revealing great potential for clinical application.