Deep learning HASTE sequence compared with T2-weighted BLADE sequence for liver MRI at 3 Tesla: a qualitative and quantitative prospective study.

OBJECTIVES: To qualitatively and quantitatively compare a single breath-hold fast half-Fourier single-shot turbo spin echo sequence with deep learning reconstruction (DL HASTE) with T2-weighted BLADE sequence for liver MRI at 3 T. METHODS: From December 2020 to January 2021, patients with liver MRI were prospectively included. For qualitative analysis, sequence quality, presence of artifacts, conspicuity, and presumed nature of the smallest lesion were assessed using the chi-squared and McNemar tests. For quantitative analysis, number of liver lesions, size of the smallest lesion, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in both sequences were assessed using the paired Wilcoxon signed-rank test. Intraclass correlation coefficients (ICCs) and kappa coefficients were used to assess agreement between the two readers. RESULTS: One hundred and twelve patients were evaluated. Overall image quality (p = .006), artifacts (p < .001), and conspicuity of the smallest lesion (p = .001) were significantly better for the DL HASTE sequence than for the T2-weighted BLADE sequence. Significantly more liver lesions were detected with the DL HASTE sequence (356 lesions) than with the T2-weighted BLADE sequence (320 lesions; p < .001). CNR was significantly higher for the DL HASTE sequence (p < .001). SNR was higher for the T2-weighted BLADE sequence (p < .001). Interreader agreement was moderate to excellent depending on the sequence. Of the 41 supernumerary lesions visible only on the DL HASTE sequence, 38 (93%) were true-positives. CONCLUSION: The DL HASTE sequence can be used to improve image quality and contrast and reduces artifacts, allowing the detection of more liver lesions than with the T2-weighted BLADE sequence. CLINICAL RELEVANCE STATEMENT: The DL HASTE sequence is superior to the T2-weighted BLADE sequence for the detection of focal liver lesions and can be used in daily practice as a standard sequence. KEY POINTS: • The half-Fourier acquisition single-shot turbo spin echo sequence with deep learning reconstruction (DL HASTE sequence) has better overall image quality, reduced artifacts (particularly motion artifacts), and improved contrast, allowing the detection of more liver lesions than with the T2-weighted BLADE sequence. • The acquisition time of the DL HASTE sequence is at least eight times faster (21 s) than that of the T2-weighted BLADE sequence (3-5 min). • The DL HASTE sequence could replace the conventional T2-weighted BLADE sequence to meet the growing indication for hepatic MRI in clinical practice, given its diagnostic and time-saving performance.

Combined Deep Learning-based Super-Resolution and Partial Fourier Reconstruction for Gradient Echo Sequences in Abdominal MRI at 3 Tesla: Shortening Breath-Hold Time and Improving Image Sharpness and Lesion Conspicuity.

RATIONALE AND OBJECTIVES: To investigate the impact of a prototypical deep learning-based super-resolution reconstruction algorithm tailored to partial Fourier acquisitions on acquisition time and image quality for abdominal T1-weighted volume-interpolated breath-hold examination (VIBESR) at 3 Tesla. The standard T1-weighted images were used as the reference standard (VIBESD). MATERIALS AND METHODS: Patients with diverse abdominal pathologies, who underwent a clinically indicated contrast-enhanced abdominal VIBE magnetic resonance imaging at 3T between March and June 2021 were retrospectively included. Following the acquisition of the standard VIBESD sequences, additional images for the non-contrast, dynamic contrast-enhanced and post-contrast T1-weighted VIBE acquisition were retrospectively reconstructed using the same raw data and employing a prototypical deep learning-based super-resolution reconstruction algorithm. The algorithm was designed to enhance edge sharpness by avoiding conventional k-space filtering and to perform a partial Fourier reconstruction in the slice phase-encoding direction for a predefined asymmetric sampling ratio. In the retrospective reconstruction, the asymmetric sampling was realized by omitting acquired samples at the end of the acquisition and therefore corresponding to a shorter acquisition. Four radiologists independently analyzed the image datasets (VIBESR and VIBESD) in a blinded manner. Outcome measures were: sharpness of abdominal organs, sharpness of vessels, image contrast, noise, hepatic lesion conspicuity and size, overall image quality and diagnostic confidence. These parameters were statistically compared and interrater reliability was computed using Fleiss' Kappa and intraclass correlation coefficient (ICC). Finally, the rate of detection of hepatic lesions was documented and was statistically compared using the paired Wilcoxon test. RESULTS: A total of 32 patients aged 59 ± 16 years (23 men (72%), 9 women (28%)) were included. For VIBESR, breath-hold time was significantly reduced by approximately 13.6% (VIBESR 11.9 ± 1.2 seconds vs. VIBESD: 13.9 ± 1.4 seconds, p < 0.001). All readers rated sharpness of abdominal organs, sharpness of vessels to be superior in images with VIBESR (p values ranged between p = 0.005 and p < 0.001). Despite reduction of acquisition time, image contrast, noise, overall image quality and diagnostic confidence were not compromised, as there was no evidence of a difference between VIBESR and VIBESD (p > 0.05). The inter-reader agreement was substantial with a Fleiss' Kappa of >0.7 in all contrast phases. A total of 13 hepatic lesions were analyzed. The four readers observed a superior lesion conspicuity in VIBESR than in VIBESD (p values ranged between p = 0.046 and p < 0.001). In terms of lesion size, there was no significant difference between VIBESD and VIBESR for all readers. Finally, there was an excellent inter-reader agreement regarding lesion size (ICC > 0.9). For all readers, no statistically significant difference was observed regarding detection of hepatic lesions between VIBESD and VIBESR. CONCLUSION: The deep learning-based super-resolution reconstruction with partial Fourier in the slice phase-encoding direction enabled a reduction of breath-hold time and improved image sharpness and lesion conspicuity in T1-weighted gradient echo sequences in abdominal magnetic resonance imaging at 3 Tesla. Faster acquisition time without compromising image quality or diagnostic confidence was possible by using this deep learning-based reconstruction technique.

Comprehensive Clinical Evaluation of a Deep Learning-Accelerated, Single-Breath-Hold Abdominal HASTE at 1.5 T and 3 T.

To evaluate the clinical performance of a deep learning-accelerated single-breath-hold half-Fourier acquisition single-shot turbo spin echo (HASTEDL)-sequence for T2-weighted fat-suppressed MRI of the abdomen at 1.5 T and 3 T in comparison to standard T2-weighted fat-suppressed multi-shot turbo spin echo-sequence. A total of 320 patients who underwent a clinically indicated liver MRI at 1.5 T and 3 T between August 2020 and February 2021 were enrolled in this single-center, retrospective study. HASTEDL and standard sequences were assessed regarding overall and organ-based image quality, noise, contrast, sharpness, artifacts, diagnostic confidence, as well as lesion detectability using a Likert scale ranging from 1 to 4 (4 = best). The number of visible lesions of each organ was counted and the largest diameter of the major lesion was measured. HASTEDL showed excellent image quality (median 4, interquartile range 3-4), although BLADE (median 4, interquartile range 4-4) was rated significantly higher for overall and organ-based image quality of the adrenal gland (P < .001), contrast (P < 0.001), sharpness (P < 0.001), artifacts (P < 0.001), as well as diagnostic confidence (P < .001). No significant differences were found concerning noise (P = 0.886), organ-based image quality of the liver, pancreas, spleen, and kidneys (P = 0.120-0.366), number and measured diameter of the detected lesions (ICC = 0.972-1.0). Reduction of the aquisition time (TA) was at least 89% for 1.5 T images and 86% for 3 T images. HASTEDL provided excellent image quality, good diagnostic confidence and lesion detection compared to a standard T2-sequences, allowing an eminent reduction of the acquisition time.

Accelerated T2-weighted MRI of the liver at 3 T using a single-shot technique with deep learning-based image reconstruction: impact on the image quality and lesion detection.

PURPOSE: Fat-suppressed T2-weighted imaging (T2-FS) requires a long scan time and can be wrought with motion artifacts, urging the development of a shorter and more motion robust sequence. We compare the image quality of a single-shot T2-weighted MRI prototype with deep-learning-based image reconstruction (DL HASTE-FS) with a standard T2-FS sequence for 3 T liver MRI. METHODS: 41 consecutive patients with 3 T abdominal MRI examinations including standard T2-FS and DL HASTE-FS, between 5/6/2020 and 11/23/2020, comprised the study cohort. Three radiologists independently reviewed images using a 5-point Likert scale for artifact and image quality measures, while also assessing for liver lesions. RESULTS: DL HASTE-FS acquisition time was 54.93 ± 16.69, significantly (p < .001) shorter than standard T2-FS (114.00 ± 32.98 s). DL HASTE-FS received significantly higher scores for sharpness of liver margin (4.3 vs 3.3; p < .001), hepatic vessel margin (4.2 vs 3.3; p < .001), pancreatic duct margin (4.0 vs 1.9; p < .001); in-plane (4.0 vs 3.2; p < .001) and through-plane (3.9 vs 3.4; p < .001) motion artifacts; other ghosting artifacts (4.3 vs 2.9; p < .001); and overall image quality (4.0 vs 2.9; p < .001), in addition to receiving a higher score for homogeneity of fat suppression (3.7 vs 3.4; p = .04) and liver-fat contrast (p = .03). For liver lesions, DL HASTE-FS received significantly higher scores for sharpness of lesion margin (4.4 vs 3.7; p = .03). CONCLUSION: Novel single-shot T2-weighted MRI with deep-learning-based image reconstruction demonstrated superior image quality compared with the standard T2-FS sequence for 3 T liver MRI, while being acquired in less than half the time.

Clinical feasibility of accelerated diffusion weighted imaging of the abdomen with deep learning reconstruction: Comparison with conventional diffusion weighted imaging.

PURPOSE: To assess the clinical feasibility of accelerated deep learning-reconstructed diffusion weighted imaging (DWI) and to compare its image quality and acquisition time with those of conventional DWI. METHODS: Seventy-four consecutive patients who underwent 3 T abdominal magnetic resonance imaging (MRI) were retrospectively enrolled. DWI were acquired using both conventional DWI and DWI with deep-learning reconstruction (DL DWI). Image quality (overall image quality, anatomic sharpness and details, artifacts, noise, and lesion conspicuity) was scored by two radiologists and compared between two DWI sequences. The apparent diffusion coefficient (ADC) was measured in six locations of the liver parenchyma and focal lesions and compared between two DWI sequences. RESULTS: The mean acquisition time for the DL DWI (216.87 ± 49.23 sec) was significantly shorter (P < 0.001) than for conventional DWI (358.69 ± 105.93 sec). DL DWI achieved higher scores than conventional DWI for all qualitative image quality parameters (P < 0.001). DL DWI had a more homogeneous distribution of ADC values throughout the liver, except for the left superior section, compared with conventional DWI. The standard deviations of the ADC values for all hepatic areas were significantly lower in DL DWI than in conventional DWI (all, P < 0.001). The ADC values for the liver parenchyma and focal hepatic lesions were lower in DL DWI than in conventional DWI. CONCLUSIONS: DL DWI is a feasible acquisition technique in clinical routines and provides improved image quality and simultaneously significant reduction in scan time compared with conventional DWI.

Analysis of a Deep Learning-Based Superresolution Algorithm Tailored to Partial Fourier Gradient Echo Sequences of the Abdomen at 1.5 T: Reduction of Breath-Hold Time and Improvement of Image Quality.

OBJECTIVES: The aim of this study was to investigate the feasibility and impact of a novel deep learning superresolution algorithm tailored to partial Fourier allowing retrospectively theoretical acquisition time reduction in 1.5 T T1-weighted gradient echo imaging of the abdomen. MATERIALS AND METHODS: Fifty consecutive patients who underwent a 1.5 T contrast-enhanced magnetic resonance imaging examination of the abdomen between April and May 2021 were included in this retrospective study. After acquisition of a conventional T1-weighted volumetric interpolated breath-hold examination using Dixon for water-fat separation (VIBEStd), the acquired data were reprocessed including a superresolution algorithm that was optimized for partial Fourier acquisitions (VIBESR). To accelerate theoretically the acquisition process, a more aggressive partial Fourier setting was applied in VIBESR reconstructions practically corresponding to a shorter acquisition for the data included in the retrospective reconstruction. Precontrast, dynamic contrast-enhanced, and postcontrast data sets were processed. Image analysis was performed by 2 radiologists independently in a blinded random order without access to clinical data regarding the following criteria using a Likert scale ranging from 1 to 4 with 4 being the best: noise levels, sharpness and contrast of vessels, sharpness and contrast of organs and lymph nodes, overall image quality, diagnostic confidence, and lesion conspicuity.Wilcoxon signed rank test for paired data was applied to test for significance. RESULTS: Mean patient age was 61 ± 14 years. Mean acquisition time for the conventional VIBEStd sequence was 15 ± 1 seconds versus theoretical 13 ± 1 seconds of acquired data used for the VIBESR reconstruction. Noise levels were evaluated to be better in VIBESR with a median of 4 (4-4) versus a median of 3 (3-3) in VIBEStd by both readers (P < 0.001). Sharpness and contrast of vessels as well as organs and lymph nodes were also evaluated to be superior in VIBESR compared with VIBEStd with a median of 4 (4-4) versus a median of 3 (3-3) (P < 0.001). Diagnostic confidence was also rated superior in VIBESR with a median of 4 (4-4) versus a median of 3.5 (3-4) in VIBEStd by reader 1 and with a median of 4 (4-4) for VIBESR and a median of 4 (4-4) for VIBEStd by reader 2 (both P < 0.001). CONCLUSIONS: Image enhancement using deep learning-based superresolution tailored to partial Fourier acquisitions of T1-weighted gradient echo imaging of the abdomen provides improved image quality and diagnostic confidence in combination with more aggressive partial Fourier settings leading to shorter scan time.

A parallel spatial and Bloch manifold regularized iterative reconstruction method for MR Fingerprinting.

Magnetic Resonance Fingerprinting (MRF) reconstructs tissue maps based on a sequence of very highly undersampled images. In order to be able to perform MRF reconstruction, state-of-the-art MRF methods rely on priors such as the MR physics (Bloch equations) and might also use some additional low-rank or spatial regularization. However to our knowledge these three regularizations are not applied together in a joint reconstruction. The reason is that it is indeed challenging to incorporate effectively multiple regularizations in a single MRF optimization algorithm. As a result most of these methods are not robust to noise especially when the sequence length is short. In this paper, we propose a family of new methods where spatial and low-rank regularizations, in addition to the Bloch manifold regularization, are applied on the images. We show on digital phantom and NIST phantom scans, as well as volunteer scans that the proposed methods bring significant improvement in the quality of the estimated tissue maps.

Development and Evaluation of Deep Learning-Accelerated Single-Breath-Hold Abdominal HASTE at 3 T Using Variable Refocusing Flip Angles.

OBJECTIVE: Deep learning (DL) reconstruction enables substantial acceleration of image acquisition while maintaining diagnostic image quality. The aims of this study were to overcome the drawback of specific absorption rate (SAR)-related limitations at 3 T and to develop a DL-accelerated single-breath-hold half-Fourier acquisition single-shot turbo spin echo (HASTE) sequence for 2-dimesional T2-weighted fat-suppressed magnetic resonance imaging of the abdomen at 3 T using a variable flip angle (FA) evolution for the refocusing radiofrequency pulses, as well as to evaluate its feasibility and image quality in comparison to state-of-the-art T2-weighted fat-suppressed imaging technique (BLADE). MATERIALS AND METHODS: First, a suitable FA evolution with low cardiac motion-related signal loss (CRSL) and low SAR was determined through a prospective volunteer study with 11 participants. Image quality and diagnostic confidence with 5 different FA evolutions of a HASTEDL were assessed to identify the most suitable FA evolution. Second, the identified FA evolution was implemented clinically and evaluated in 51 patients undergoing a clinically indicated liver magnetic resonance imaging at 3 T. Two radiologists assessed the HASTEDL and standard sequences regarding overall image quality, noise, contrast, sharpness, artifacts, CRSL, and diagnostic confidence using a Likert scale ranging from 1 to 4, with 4 being the best. Comparative analyses were conducted to assess the differences between HASTEDL (acquisition time, 21 seconds; single breath-hold) and the routinely used T2-weighted BLADE sequence (acquisition time, 4 minutes; respiratory triggering). RESULTS: From the volunteer study, the FA evolution characterized by the control points 130-90-110-130 degrees (HASTEDL) was identified as optimal among the 5 evolutions evaluated and was implemented in our clinical protocol. In all 51 patients, HASTEDL was successfully acquired at 3 T and showed excellent image quality (median, 4; interquartile range, 3-4). Although BLADE was rated significantly higher for overall image quality, noise, contrast, sharpness, artifacts, CRSL, and diagnostic confidence than HASTEDL, no differences were found concerning the number (n = 102) and measured diameter of the detected hepatic lesions between the 2 sequences BLADE and HASTEDL. CONCLUSIONS: The proposed single-breath-hold abdominal HASTEDL with variable refocusing FAs is feasible at 3 T within SAR limits and yields high image quality and diagnostic confidence as compared with a standard T2-weighted acquisition technique, at a 10th of the acquisition time.

Accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based image reconstruction: qualitative and quantitative comparison of image quality with conventional T2-weighted FS sequence.

OBJECTIVE: To compare the image quality of an accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based image reconstruction (DL HASTE-FS) with conventional T2-weighted FS sequence (conventional T2 FS) at 1.5 T. METHODS: One hundred consecutive patients who underwent clinical MRI of the liver at 1.5 T including the conventional T2-weighted fat-suppressed sequence (T2 FS) and accelerated single-shot T2-weighted MRI of the liver with deep learning-based image reconstruction (DL HASTE-FS) were included. Images were reviewed independently by three blinded observers who used a 5-point confidence scale for multiple measures regarding the artifacts and image quality. Descriptive statistics and McNemar's test were used to compare image quality scores and percentage of lesions detected by each sequence, respectively. Intra-class correlation coefficient (ICC) was used to assess consistency in reader scores. RESULTS: Acquisition time for DL HASTE-FS was 51.23 +/ 10.1 s, significantly (p < 0.001) shorter than conventional T2-FS (178.9 ± 85.3 s). DL HASTE-FS received significantly higher scores than conventional T2-FS for strength and homogeneity of fat suppression; sharpness of liver margin; sharpness of intra-hepatic vessel margin; in-plane and through-plane respiratory motion; other ghosting artefacts; liver-fat contrast; and overall image quality (all, p < 0.0001). DL HASTE-FS also received higher scores for lesion conspicuity and sharpness of lesion margin (all, p < .001), without significant difference for liver lesion contrast (p > 0.05). CONCLUSIONS: Accelerated single-shot T2-weighted MRI of the liver with deep learning-based image reconstruction showed superior image quality compared to the conventional T2-weighted fat-suppressed sequence despite a 4-fold reduction in acquisition time. KEY POINTS: • Conventional fat-suppressed T2-weighted sequence (conventional T2 FS) can take unacceptably long to acquire and is the most commonly repeated sequence in liver MRI due to motion. • DL HASTE-FS demonstrated superior image quality, improved respiratory motion and other ghosting artefacts, and increased lesion conspicuity with comparable liver-to-lesion contrast compared to conventional T2FS sequence. • DL HASTE- FS has the potential to replace conventional T2 FS sequence in routine clinical MRI of the liver, reducing the scan time, and improving the image quality.

Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction.

Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided participants with data from 7,299 clinical brain scans (de-identified via a HIPAA-compliant procedure by NYU Langone Health), holding back the fully-sampled data from 894 of these scans for challenge evaluation purposes. In contrast to the 2019 challenge, we focused our radiologist evaluations on pathological assessment in brain images. We also debuted a new Transfer track that required participants to submit models evaluated on MRI scanners from outside the training set. We received 19 submissions from eight different groups. Results showed one team scoring best in both SSIM scores and qualitative radiologist evaluations. We also performed analysis on alternative metrics to mitigate the effects of background noise and collected feedback from the participants to inform future challenges. Lastly, we identify common failure modes across the submissions, highlighting areas of need for future research in the MRI reconstruction community.

Diagnostic Confidence and Feasibility of a Deep Learning Accelerated HASTE Sequence of the Abdomen in a Single Breath-Hold.

OBJECTIVE: The aim of this study was to evaluate the feasibility of a single breath-hold fast half-Fourier single-shot turbo spin echo (HASTE) sequence using a deep learning reconstruction (HASTEDL) for T2-weighted magnetic resonance imaging of the abdomen as compared with 2 standard T2-weighted imaging sequences (HASTE and BLADE). MATERIALS AND METHODS: Sixty-six patients who underwent 1.5-T liver magnetic resonance imaging were included in this monocentric, retrospective study. The following T2-weighted sequences in axial orientation and using spectral fat suppression were compared: a conventional respiratory-triggered BLADE sequence (time of acquisition [TA] = 4:00 minutes), a conventional multiple breath-hold HASTE sequence (HASTES) (TA = 1:30 minutes), as well as a single breath-hold HASTE with deep learning reconstruction (HASTEDL) (TA = 0:16 minutes). Two radiologists assessed the 3 sequences regarding overall image quality, noise, sharpness, diagnostic confidence, and lesion detectability as well as lesion characterization using a Likert scale ranging from 1 to 4 with 4 being the best. Comparative analyses were conducted to assess the differences between the 3 sequences. RESULTS: HASTEDL was successfully acquired in all patients. Overall image quality for HASTEDL was rated as good (median, 3; interquartile range, 3-4) and was significantly superior to HASTEs (P < 0.001) and inferior to BLADE (P = 0.001). Noise, sharpness, and artifacts for HASTEDL reached similar levels to BLADE (P ≤ 0.176) and were significantly superior to HASTEs (P < 0.001). Diagnostic confidence for HASTEDL was rated excellent by both readers and significantly superior to HASTEs (P < 0.001) and inferior to BLADE (P = 0.044). Lesion detectability and lesion characterization for HASTEDL reached similar levels to those of BLADE (P ≤ 0.523) and were significantly superior to HASTEs (P < 0.001). Concerning the number of detected lesions and the measured diameter of the largest lesion, no significant differences were found comparing BLADE, HASTES, and HASTEDL (P ≤ 0.912). CONCLUSIONS: The single breath-hold HASTEDL is feasible and yields comparable image quality and diagnostic confidence to standard T2-weighted TSE BLADE and may therefore allow for a remarkable time saving in abdominal imaging.

Compressive source separation: theory and methods for hyperspectral imaging.

We propose and analyze a new model for hyperspectral images (HSIs) based on the assumption that the whole signal is composed of a linear combination of few sources, each of which has a specific spectral signature, and that the spatial abundance maps of these sources are themselves piecewise smooth and therefore efficiently encoded via typical sparse models. We derive new sampling schemes exploiting this assumption and give theoretical lower bounds on the number of measurements required to reconstruct HSI data and recover their source model parameters. This allows us to segment HSIs into their source abundance maps directly from compressed measurements. We also propose efficient optimization algorithms and perform extensive experimentation on synthetic and real datasets, which reveals that our approach can be used to encode HSI with far less measurements and computational effort than traditional compressive sensing methods.