Prospective Comparison of Standard and Deep Learning-reconstructed Turbo Spin-Echo MRI of the Shoulder.

Background Deep learning (DL)-based MRI reconstructions can reduce imaging times for turbo spin-echo (TSE) examinations. However, studies that prospectively use DL-based reconstructions of rapidly acquired, undersampled MRI in the shoulder are lacking. Purpose To compare the acquisition time, image quality, and diagnostic confidence of DL-reconstructed TSE (TSEDL) with standard TSE in patients indicated for shoulder MRI. Materials and Methods This prospective single-center study included consecutive adult patients with various shoulder abnormalities who were clinically referred for shoulder MRI between February and March 2023. Each participant underwent standard TSE MRI (proton density- and T1-weighted imaging; conventional TSE sequence was used as reference for comparison), followed by a prospectively undersampled accelerated TSEDL examination. Six musculoskeletal radiologists evaluated images using a four-point Likert scale (1, poor; 4, excellent) for overall image quality, perceived signal-to-noise ratio, sharpness, artifacts, and diagnostic confidence. The frequency of major pathologic features and acquisition times were also compared between the acquisition protocols. The intergroup comparisons were performed using the Wilcoxon signed rank test. Results Overall, 135 shoulders in 133 participants were evaluated (mean age, 47.9 years ± 17.1 [SD]; 73 female participants). The median acquisition time of the TSEDL protocol was lower than that of the standard TSE protocol (288 seconds [IQR, 288-288 seconds] vs 926 seconds [IQR, 926-950 seconds], respectively; P < .001), achieving a 69% lower acquisition time. TSEDL images were given higher scores for overall image quality, perceived signal-to-noise ratio, and artifacts (all P < .001). Similar frequency of pathologic features (P = .48 to > .99), sharpness (P = .06), or diagnostic confidence (P = .05) were noted between images from the two protocols. Conclusion In a clinical setting, TSEDL led to reduced examination time and higher image quality with similar diagnostic confidence compared with standard TSE MRI in the shoulder. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Chang and Chow in this issue.

Deep learning-accelerated T2-weighted imaging versus conventional T2-weighted imaging in the female pelvic cavity: image quality and diagnostic performance.

BACKGROUND: The deep learning (DL)-based reconstruction algorithm reduces noise in magnetic resonance imaging (MRI), thereby enabling faster MRI acquisition. PURPOSE: To compare the image quality and diagnostic performance of conventional turbo spin-echo (TSE) T2-weighted (T2W) imaging with DL-accelerated sagittal T2W imaging in the female pelvic cavity. METHODS: This study evaluated 149 consecutive female pelvic MRI examinations, including conventional T2W imaging with TSE (acquisition time = 2:59) and DL-accelerated T2W imaging with breath hold (DL-BH) (1:05 [0:14 × 3 breath-holds]) in the sagittal plane. In 294 randomly ordered sagittal T2W images, two radiologists independently assessed image quality (sharpness, subjective noise, artifacts, and overall image quality), made a diagnosis for uterine leiomyomas, and scored diagnostic confidence. For the uterus and piriformis muscle, quantitative imaging analysis was also performed. Wilcoxon signed rank tests were used to compare the two sets of T2W images. RESULTS: In the qualitative analysis, DL-BH showed similar or significantly higher scores for all features than conventional T2W imaging (P <0.05). In the quantitative analysis, the noise in the uterus was lower in DL-BH, but the noise in the muscle was lower in conventional T2W imaging. In the uterus and muscle, the signal-to-noise ratio was significantly lower in DL-BH than in conventional T2W imaging (P <0.001). The diagnostic performance of the two sets of T2W images was not different for uterine leiomyoma. CONCLUSIONS: DL-accelerated sagittal T2W imaging obtained with three breath-holds demonstrated superior or comparable image quality to conventional T2W imaging with no significant difference in diagnostic performance for uterine leiomyomas.

Deep Learning Reconstruction for Accelerated Spine MRI: Prospective Analysis of Interchangeability.

Background Deep learning (DL)-based MRI reconstructions can reduce examination times for turbo spin-echo (TSE) acquisitions. Studies that prospectively employ DL-based reconstructions of rapidly acquired, undersampled spine MRI are needed. Purpose To investigate the diagnostic interchangeability of an unrolled DL-reconstructed TSE (hereafter, TSEDL) T1- and T2-weighted acquisition method with standard TSE and to test their impact on acquisition time, image quality, and diagnostic confidence. Materials and Methods This prospective single-center study included participants with various spinal abnormalities who gave written consent from November 2020 to July 2021. Each participant underwent two MRI examinations: standard fully sampled T1- and T2-weighted TSE acquisitions (reference standard) and prospectively undersampled TSEDL acquisitions with threefold and fourfold acceleration. Image evaluation was performed by five readers. Interchangeability analysis and an image quality-based analysis were used to compare the TSE and TSEDL images. Acquisition time and diagnostic confidence were also compared. Interchangeability was tested using the individual equivalence index regarding various degenerative and nondegenerative entities, which were analyzed on each vertebra and defined as discordant clinical judgments of less than 5%. Interreader and intrareader agreement and concordance (κ and Kendall τ and W statistics) were computed and Wilcoxon and McNemar tests were used. Results Overall, 50 participants were evaluated (mean age, 46 years ± 18 [SD]; 26 men). The TSEDL method enabled up to a 70% reduction in total acquisition time (100 seconds for TSEDL vs 328 seconds for TSE, P < .001). All individual equivalence indexes were less than 4%. TSEDL acquisition was rated as having superior image noise by all readers (P < .001). No evidence of a difference was found between standard TSE and TSEDL regarding frequency of major findings, overall image quality, or diagnostic confidence. Conclusion The deep learning (DL)-reconstructed turbo spin-echo (TSE) method was found to be interchangeable with standard TSE for detecting various abnormalities of the spine at MRI. DL-reconstructed TSE acquisition provided excellent image quality, with a 70% reduction in examination time. German Clinical Trials Register no. DRKS00023278 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Hallinan in this issue.

Preliminary Study of Confounder-Corrected Fat Fraction and R2* Mapping of Bone Marrow in Children With Acute Leukemia.

BACKGROUND: The bone marrow (BM) evaluation of acute leukemia (AL) mainly depends on invasive BM puncture biopsy. Noninvasive and accurate MR examination technology has potential clinical application value in the BM evaluation of AL patients. Multi-gradient-echo (MGRE) has been found useful to evaluate changes in BM fat and iron content, but has not yet been applied in AL. PURPOSE: To explore the diagnostic capability of BM infiltration of quantitative BM fat fraction (FF) and R2* values obtained from a 3D MGRE sequence in children with primary AL. STUDY TYPE: Prospective. POPULATION/SUBJECTS: Sixty-two pediatric patients with untreated AL and 68 healthy volunteers. AL patients were divided into acute lymphoblastic leukemia (ALL) (n = 39) and acute myeloid leukemia (AML) (n = 23) groups. FIELD STRENGTH/SEQUENCE: 3T, 3D chemical-shift-encoded multi-gradient-echo, T1WI, T2WI, T2_STIR. ASSESSMENT: BM FF and R2* values were assessed by manually drawing regions of interest at the L3, L4, ilium, and 1 cm below the bilateral trochanter of the femur (upper femur). STATISTICAL TESTS: Independent sample t-tests, variance analysis, Spearman correlation. RESULTS: BM FF and R2* at L3, L4, ilium, and upper femur, FFtotal and R2*total were significantly lower in the AL than control group. BM FF did not significantly differ between ALL and AML groups (PL3 = 0.060, PL4 = 0.086, Pilium = 0.179, Pupper femur = 0.149, and Ptotle = 0.097, respectively). The R2* was significantly lower in ALL group than AML group for L3, L4, and R2*total . BM FF was moderately positively correlated with R2* in ALL group, and strongly positively correlated in AML group. Area under the receiver operating characteristic curves showed that BM FF had higher AUC in AL, ALL, and AML (all AUC = 1.000) than R2* (0.976, 0.996, and 0.941, respectively). DATA CONCLUSION: MGRE-MRI mapping can be applied to measure BM FF and R2* values, and help evaluate BM infiltration and iron storage in children with AL. EVIDENCE LEVEL: 1 Technical Efficacy: 2.

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.

Evaluation of interstitial fibrosis in chronic kidney disease by multiparametric functional MRI and histopathologic analysis.

OBJECTIVES: To investigate the diagnostic value of functional MRI to assess renal interstitial fibrosis in patients with chronic kidney disease (CKD). METHODS: We prospectively recruited 80 CKD patients who underwent renal biopsies and 16 healthy volunteers to undergo multiparametric functional MRI examinations. The Oxford MEST-C classification was used to score the interstitial fibrosis. The diagnostic performance of functional MRI to discriminate interstitial fibrosis was evaluated by calculating the area under the receiver operating characteristic (ROC) curves. RESULTS: IgA nephropathy (60%) accounted for the majority of pathologic type in the CKD patients. Apparent diffusion coefficient (ADC) from diffusion-weighted imaging (DWI) was correlated with interstitial fibrosis (rho = -0.73). Decreased renal blood flow (RBF) derived from arterial spin labeling (rho = -0.78) and decreased perfusion fraction (f) derived from DWI (rho = -0.70) were accompanied by increased interstitial fibrosis. The T1 value from T1 mapping correlated with interstitial fibrosis (rho = 0.67) (all p < 0.01). The areas under the ROC curve for the discrimination of ≤ 25% vs. > 25% and ≤ 50% vs. > 50% interstitial fibrosis were 0.87 (95% confidence interval, 0.78 to 0.94) and 0.93 (0.86 to 0.98) by ADC, 0.84 (0.74 to 0.91) and 0.94 (0.86 to 0.98) by f, 0.93 (0.85 to 0.98) and 0.90 (0.82 to 0.96) by RBF, and 0.91 (0.83 to 0.96) and 0.77 (0.66 to 0.85) by T1, respectively. CONCLUSIONS: Functional MRI parameters were strongly correlated with the interstitial fibrosis of CKD. Therefore, it might a powerful tool to assess interstitial fibrosis of CKD noninvasively. KEY POINTS: • In CKD patients, the renal cortical ADC value decreased and T1 value increased significantly compared with healthy volunteers. • Functional MRI revealed significantly decreased renal perfusion in CKD patients compared with healthy volunteers. • The renal cortical ADC, f, RBF, and T1 values were strongly correlated with the interstitial fibrosis of CKD.

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.

Prospective intraindividual comparison of a standard 2D TSE MRI protocol for ankle imaging and a deep learning-based 2D TSE MRI protocol with a scan time reduction of 48.

PURPOSE: Magnetic resonance imaging (MRI) scan time remains a limited and valuable resource. This study evaluates the diagnostic performance of a deep learning (DL)-based accelerated TSE study protocol compared to a standard TSE study protocol in ankle MRI. MATERIAL AND METHODS: Between October 2020 and July 2021 forty-seven patients were enrolled in this study for an intraindividual comparison of a standard TSE study protocol and a DL TSE study protocol either on a 1.5 T or a 3 T scanner. Two radiologists evaluated the examinations regarding structural pathologies and image quality categories (5-point-Likert-scale; 1 = "non diagnostic", 5 = "excellent"). RESULTS: Both readers showed almost perfect/perfect agreement of DL TSE with standard TSE in all analyzed structural pathologies (0.81-1.00) with a median "good" or "excellent" rating (4-5/5) in all image quality categories in both 1.5 T and 3 T MRI. The reduction of total acquisition time of DL TSE compared to standard TSE was 49% in 1.5 T and 48% in 3 T MRI to a total acquisition time of 5 min 41 s and 5 min 46 s. CONCLUSION: In ankle MRI the new DL-based accelerated TSE study protocol delivers high agreement with standard TSE and high image quality, while reducing the acquisition time by 48%.

Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest.

OBJECTIVES: A deep learning-based super-resolution for postcontrast volume-interpolated breath-hold examination (VIBE) of the chest was investigated in this study. Aim was to improve image quality, noise, artifacts and diagnostic confidence without change of acquisition parameters. MATERIALS AND METHODS: Fifty patients who received VIBE postcontrast imaging of the chest at 1.5 T were included in this retrospective study. After acquisition of the standard VIBE (VIBES), a novel deep learning-based algorithm and a denoising algorithm were applied, resulting in enhanced images (VIBEDL). Two radiologists qualitatively evaluated both datasets independently, rating sharpness of soft tissue, vessels, bronchial structures, lymph nodes, artifacts, cardiac motion artifacts, noise levels and overall diagnostic confidence, using a Likert scale ranging from 1 to 4. In the presence of lung lesions, the largest lesion was rated regarding sharpness and diagnostic confidence using the same Likert scale as mentioned above. Additionally, the largest diameter of the lesion was measured. RESULTS: The sharpness of soft tissue, vessels, bronchial structures and lymph nodes as well as the diagnostic confidence, the extent of artifacts, the extent of cardiac motion artifacts and noise levels were rated superior in VIBEDL (all P < 0.001). There was no significant difference in the diameter or the localization of the largest lung lesion in VIBEDL compared to VIBES. Lesion sharpness as well as detectability was rated significantly better by both readers with VIBEDL (both P < 0.001). CONCLUSION: The application of a novel deep learning-based super-resolution approach in T1-weighted VIBE postcontrast imaging resulted in an improvement in image quality, noise levels and diagnostic confidence as well as in a shortened acquisition time.

Feasibility of an accelerated 2D-multi-contrast knee MRI protocol using deep-learning image reconstruction: a prospective intraindividual comparison with a standard MRI protocol.

OBJECTIVES: The aim of this study was to evaluate the image quality and diagnostic performance of a deep-learning (DL)-accelerated two-dimensional (2D) turbo spin echo (TSE) MRI of the knee at 1.5 and 3 T in clinical routine in comparison to standard MRI. MATERIAL AND METHODS: Sixty participants, who underwent knee MRI at 1.5 and 3 T between October/2020 and March/2021 with a protocol using standard 2D-TSE (TSES) and DL-accelerated 2D-TSE sequences (TSEDL), were enrolled in this prospective institutional review board-approved study. Three radiologists assessed the sequences regarding structural abnormalities and evaluated the images concerning overall image quality, artifacts, noise, sharpness, subjective signal-to-noise ratio, and diagnostic confidence using a Likert scale (1-5, 5 = best). RESULTS: Overall image quality for TSEDL was rated to be excellent (median 5, IQR 4-5), significantly higher compared to TSES (median 5, IQR 4 - 5, p < 0.05), showing significantly lower extents of noise and improved sharpness (p < 0.001). Inter- and intra-reader agreement was almost perfect (κ = 0.92-1.00) for the detection of internal derangement and substantial to almost perfect (κ = 0.58-0.98) for the assessment of cartilage defects. No difference was found concerning the detection of bone marrow edema and fractures. The diagnostic confidence of TSEDL was rated to be comparable to that of TSES (median 5, IQR 5-5, p > 0.05). Time of acquisition could be reduced to 6:11 min using TSEDL compared to 11:56 min for a protocol using TSES. CONCLUSION: TSEDL of the knee is clinically feasible, showing excellent image quality and equivalent diagnostic performance compared to TSES, reducing the acquisition time about 50%. KEY POINTS: • Deep-learning reconstructed TSE imaging is able to almost halve the acquisition time of a three-plane knee MRI with proton density and T1-weighted images, from 11:56 min to 6:11 min at 3 T. • Deep-learning reconstructed TSE imaging of the knee provided significant improvement of noise levels (p < 0.001), providing higher image quality (p < 0.05) compared to conventional TSE imaging. • Deep-learning reconstructed TSE imaging of the knee had similar diagnostic performance for internal derangement of the knee compared to standard TSE.

CS-VIBE accelerates cranial nerve MR imaging for the diagnosis of facial neuritis: comparison of the diagnostic performance of post-contrast MPRAGE and CS-VIBE.

OBJECTIVE: We aimed to compare the diagnostic performance of post-contrast 3D compressed sensing volume-interpolated breath-hold examination (CS-VIBE) and 3D T1 magnetization-prepared rapid-acquisition gradient-echo (MPRAGE) in detecting facial neuritis. MATERIALS AND METHODS: Between February 2019 and September 2019, 60 patients (30 facial palsy patients and 30 controls) who underwent contrast-enhanced cranial nerve MRI with both conventional MPRAGE and CS-VIBE (scan time: 6 min 8 s vs. 2 min 48 s) were included in this retrospective study. All images were independently reviewed by three radiologists for the presence of facial neuritis. In patients with facial palsy, signal-to-noise ratio (SNR) of the pons, enhancement degree and contrast-to-noise ratio (CNRnerve-CSF) of the facial nerve were measured. The overall image quality, artifacts, and facial nerve discrimination were analyzed. The sensitivity and specificity of both sequences were calculated with the clinical diagnosis as a reference. RESULTS: CS-VIBE had comparable performance in the detection of facial neuritis to that of MPRAGE (sensitivity and specificity, 97.8% and 99.4% vs. 100.0% and 99.4% in pooled analysis; 97.8% and 98.9% vs. 100.0% and 98.9% in patents with facial palsy, p value > 0.05 for all). CS-VIBE showed significantly lower SNR (p value < 0.001 for all), but significantly higher CNRnerve-CSF (p value < 0.05 for all) than MPRAGE. CS-VIBE also performed better in the overall image quality, artifacts, and facial nerve discrimination than MPRAGE (p value < 0.001 for all). CONCLUSION: CS-VIBE achieved comparable diagnostic performance for facial neuritis compared to the conventional MPRAGE, with the scan time being half of that of MPRAGE. KEY POINTS: • Post-contrast 3D CS-VIBE MRI is a reliable method for the diagnosis of facial neuritis. • CS-VIBE reduces the scan time of cranial nerve MRI by more than half compared to conventional T1-weighted image. • CS-VIBE had better performance in contrast-to-noise ratio and favorable image quality compared with conventional T1-weighted image.

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.

Clinical Feasibility of High-Resolution Contrast-Enhanced Dynamic T1-Weighted Magnetic Resonance Imaging of the Upper Abdomen Using Compressed Sensing.

OBJECTIVE: The objective of this study was to evaluate the clinical feasibility of high-resolution contrast-enhanced dynamic T1-weighted imaging (T1WI) using compressed sensing (CS) in magnetic resonance imaging. METHODS: This study retrospectively included 35 patients who underwent dynamic T1WI using volumetric interpolated breath-hold examination (VIBE) with CS reconstruction (CS-VIBE) and 35 patients with conventional VIBE for comparison. Two observers assessed the liver and pancreas edges, hepatic artery, motion artifacts, and overall image quality. Quantitative analysis was performed by measuring signal intensity and image noise. RESULTS: The results showed that CS-VIBE achieved significantly better anatomic delineation of the liver and pancreas edges and hepatic artery clarity than VIBE (P < 0.001). There were no significant differences in motion artifacts in dynamic phases and overall image quality. The signal intensities and INs of CS-VIBE were higher than VIBE. CONCLUSIONS: High-resolution dynamic T1WI using CS provides better anatomic delineation with comparable or better overall image quality than conventional VIBE.

Morphological and functional assessment of the uterus: "one-stop shop imaging" using a compressed-sensing accelerated, free-breathing T1-VIBE sequence.

BACKGROUND: The combination of motion-insensitive, high-temporal, and spatial resolution imaging with evaluation of quantitative perfusion has the potential to increase the diagnostic capabilities of magnetic resonance imaging (MRI) in the female pelvis. PURPOSE: To compare a free-breathing compressed-sensing VIBE (fbVIBE) with flexible temporal resolution (range = 4.6-13.8 s) with breath-hold VIBE (bhVIBE) and to evaluate the potential value of quantifying uterine perfusion. MATERIAL AND METHODS: A total of 70 datasets from 60 patients (bhVIBE: n = 30; fbVIBE: n = 40) were evaluated by two radiologists. Only temporally resolved reconstruction (fbVIBE) was performed on 30 of the fbVIBE datasets. For a subset (n = 10) of the fbVIBE acquisitions, a time- and motion-resolved reconstruction (mrVIBE) was evaluated. Image quality (IQ), artifacts, diagnostic confidence (DC), and delineation of uterine structures (DoS) were graded on Likert scales (IQ/DC/DoS: 1 (non-diagnostic) to 5 (perfect); artifacts: 1 (no artifacts) to 5 (severe artifacts)). A Tofts model was applied for perfusion analysis. Ktrans was obtained in the myometrium (Mm), junctional zone (Jz), and cervix (Cx). RESULTS: The median IQ/DoS/DC scores of fbVIBE (4/5/5 κ >0.7-0.9) and bhVIBE (4/4/4; κ = 0.5-0.7; P > 0.05) were high, but Artifacts were graded low (fbVIBE/bhVIBE: 2/2; κ = 0.6/0.5; P > 0.05). Artifacts were only slightly improved by the additional motion-resolved reconstruction (fbVIBE/mrVIBE: 2/1.5; P = 0.08); fbVIBE was preferred in most cases (7/10). Significant differences of Ktrans values were found between Cx, Jz, and Mm (0.12/0.21/0.19; P < 0.05). CONCLUSION: The fbVIBE sequence allows functional and morphological assessment of the uterus at comparable IQ to bhVIBE.

Free-breathing fat and R2 * quantification in the liver using a stack-of-stars multi-echo acquisition with respiratory-resolved model-based reconstruction.

PURPOSE: To develop a free-breathing hepatic fat and R2∗ quantification method by extending a previously described stack-of-stars model-based fat-water separation technique with additional modeling of the transverse relaxation rate R2∗ . METHODS: The proposed technique combines motion-robust radial sampling using a stack-of-stars bipolar multi-echo 3D GRE acquisition with iterative model-based fat-water separation. Parallel-Imaging and Compressed-Sensing principles are incorporated through modeling of the coil-sensitivity profiles and enforcement of total-variation (TV) sparsity on estimated water, fat, and R2∗ parameter maps. Water and fat signals are used to estimate the confounder-corrected proton-density fat fraction (PDFF). Two strategies for handling respiratory motion are described: motion-averaged and motion-resolved reconstruction. Both techniques were evaluated in patients (n = 14) undergoing a hepatobiliary research protocol at 3T. PDFF and R2∗ parameter maps were compared to a breath-holding Cartesian reference approach. RESULTS: Linear regression analyses demonstrated strong (r > 0.96) and significant (P ≪ .01) correlations between radial and Cartesian PDFF measurements for both the motion-averaged reconstruction (slope: 0.90; intercept: 0.07%) and the motion-resolved reconstruction (slope: 0.90; intercept: 0.11%). The motion-averaged technique overestimated hepatic R2∗ values (slope: 0.35; intercept: 30.2 1/s) compared to the Cartesian reference. However, performing a respiratory-resolved reconstruction led to better R2∗ value consistency (slope: 0.77; intercept: 7.5 1/s). CONCLUSIONS: The proposed techniques are promising alternatives to conventional Cartesian imaging for fat and R2∗ quantification in patients with limited breath-holding capabilities. For accurate R2∗ estimation, respiratory-resolved reconstruction should be used.

Correction to: T1 mapping for liver function evaluation in gadoxetic acid-enhanced MR imaging: comparison of look-locker inversion recovery and B1 inhomogeneity-corrected variable flip angle method.

The original version of this article, published on 22 March 2019, unfortunately contained a mistake. The following correction has therefore been made in the original: Affiliations 1 and 2 were presented incorrectly.

Whole-lesion histogram and texture analyses of breast lesions on inline quantitative DCE mapping with CAIPIRINHA-Dixon-TWIST-VIBE.

PURPOSE: To investigate the diagnostic capability of whole-lesion (WL) histogram and texture analysis of dynamic contrast-enhanced (DCE) MRI inline-generated quantitative parametric maps using CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) to differentiate malignant from benign breast lesions and breast cancer subtypes. MATERIALS AND METHODS: From February 2018 to November 2018, DCE MRI using CDTV was performed on 211 patients. The inline-generated parametric maps included Ktrans, kep, Ve, and IAUGC60. Histogram and texture features were extracted from the above parametric maps respectively based on a WL analysis. Student's t tests, one-way ANOVAs, Mann-Whitney U tests, Jonckheere-Terpstra tests, and ROC curves were used for statistical analysis. RESULTS: Compared with benign breast lesions, malignant breast lesions showed significantly higher Ktrans_median, 5th percentile, entropy, and diff-entropy, IAUGC60_median, 5th percentile, entropy, and diff-entropy, kep_mean, median, 5th percentile, entropy, and diff-entropy, and Ve_95th percentile, diff-variance, and contrast, and significantly lower kep_skewness and Ve_SD, entropy, diff-entropy, and skewness (all p ≤ 0.011). The combination of all the extracted parameters yielded an AUC of 0.85 (sensitivity 76%, specificity 86%). kep_contrast showed a significant difference among different subtypes of breast cancer (p = 0.006). kep_skewness showed a significant difference between lymph node-positive and lymph node-negative breast cancer (p = 0.007). The IAGC60_5th percentile had an AUC of 0.71 (sensitivity 50%, specificity 91%) for differentiating between high- and low-proliferation groups of breast cancer. CONCLUSIONS: The WL histogram and texture analyses of CDTV-DCE-derived parameters may give additional information for further evaluation of breast cancer. KEY POINTS: • Inline DCE mapping with CDTV is effective and time-saving. • WL histogram and texture-extracted features could distinguish breast cancer from benign lesions accurately. • kep_contrast, kep_skewness, and IAUGC60_5th percentile could predict breast cancer subtypes, lymph node metastasis, and proliferation abilities, respectively.

Accurate fatty acid composition estimation of adipose tissue in the abdomen based on bipolar multi-echo MRI.

PURPOSE: To develop a bipolar multi-echo MRI method for the accurate estimation of the adipose tissue fatty acid composition (FAC) using clinically relevant protocols at clinical field strength. METHODS: The proposed technique jointly estimates confounding factors (field map, R2* , eddy-current phase) and triglyceride saturation state parameters by fitting multi-echo gradient echo acquisitions to a complex signal model. The noise propagation behavior was improved by applying a low-rank enforcing denoising technique and by addressing eddy-current-induced phase discrepancies analytically. The impact of the total echo train duration on the FAC parameter map accuracy was analyzed in an oil phantom at 3T. Accuracy and reproducibility assessment was based on in vitro oil phantom measurements at two field strengths (3T and 1.5T) and with two different protocols. Repeatability was assessed in vivo in patients (n = 8) with suspected fatty liver disease using test-retest acquisitions in the abdominal subcutaneous, perirenal and mesenteric fat depots. RESULTS: Echo train readout durations of at least five times the conventional in-phase time were required for accurate FAC estimation in areas of high fat content. In vitro, linear regression and Bland-Altman analyses demonstrated strong (r > 0.94) and significant (P â‰ª 0.01) correlations between measured and reference FACs for all acquisitions, with smaller overall intercepts and biases at 3T compared to 1.5T. In vivo, reported mean absolute differences between test and retest were 1.54%, 3.31%, and 2.63% for the saturated, mono-unsaturated, and poly-unsaturated fat component, respectively. CONCLUSIONS: Accurate and reproducible MRI-based FAC quantification within a breath-hold is possible at clinical field strengths.

T1 mapping for liver function evaluation in gadoxetic acid-enhanced MR imaging: comparison of look-locker inversion recovery and B1 inhomogeneity-corrected variable flip angle method.

OBJECTIVES: To compare look-locker inversion recovery (LLIR) and B1 inhomogeneity-corrected variable flip angle T1 mapping methods for estimation of liver function and prediction of hepatic insufficiency and decompensation on gadoxetic acid-enhanced MR imaging. METHODS: In this retrospective study, 248 patients with normal liver function, chronic liver disease, or cirrhosis underwent gadoxetic acid-enhanced liver MR imaging, including T1 mapping at 10-min and 20-min hepatobiliary phase (HBP) by using both methods. T1 relaxation times of the liver (T1Liver-pre, T1Liver-post) and the spleen (T1Spleen) were correlated between two methods. ΔT1Liver ([T1Liver-pre - T1Liver-post]/T1Liver-pre), adjusted T1Liver ([T1Spleen - T1Liver-post]/T1Spleen), and functional liver volume-to-weight ratio (liver volume on volumetric T1 map/[T1Liver-post × patient's weight]) were calculated. The diagnostic performance of T1 parameters and the predictive performance of models (serum marker, serum marker plus T1 parameter) were compared. RESULTS: T1Liver-post showed a strong correlation (r = 0.93, p < 0.001) between two methods but was significantly different. For depicting cirrhosis, LLIR-adjusted T1Liver at 10-min HBP showed the highest performance (p < 0.025). For predicting hepatic insufficiency and decompensation, LLIR-adjusted T1Liver (Akaike information criterion (AIC), 58.37; C-index, 0.867) and LLIR-T1Liver-post (AIC, 48.82; C-index, 0.885) at 10-min HBP showed the best performance, respectively, when added to serum albumin level. CONCLUSIONS: T1Liver-post showed a strong correlation between two methods but with significant differences. T1 mapping using LLIR at 10-min HBP with obtainment of adjusted T1Liver and T1Liver-post may be the best approach for estimation of liver function and prediction of hepatic insufficiency and decompensation. KEY POINTS: • T1 Liver-post showed a strong correlation between LLIR and B 1 inhomogeneity-corrected VFA methods, both at 10-min and 20-min HBP but with significant differences. • T1 Liver-post at 10-min and 20-min HBP using LLIR and B 1 inhomogeneity-corrected VFA methods could not be used interchangeably during the follow-up in patients with chronic liver disease (CLD) or cirrhosis. • T1 mapping using LLIR at 10-min HBP with obtainment of adjusted T1 Liver and T1 Liver-post may be the most suitable method and parameter for estimation of global liver function and prediction of clinical outcomes in patients with CLD or cirrhosis.

Native T1 mapping of autoimmune pancreatitis as a quantitative outcome surrogate.

OBJECTIVES: To investigate the ability of T1 mapping to visualize and quantify the short-term and mid-term response of autoimmune pancreatitis (AIP) to corticosteroid treatment (CST) and to correlate T1 relaxation time of the pancreas with clinical status and serum IgG4 level. METHODS: The institutional review board approved this prospective study, and all patients provided written informed consent. Pancreatic MRI including native T1 mapping was performed in 39 AIP patients before and during CST, and 40 patients without pancreatic diseases served as control. T1 relaxation time of the pancreatic head, body, and tail was measured in each patient. Clinical symptoms and serum IgG4 level of the patients were recorded. RESULTS: The native T1 relaxation time of AIP was significantly elongated compared to normal pancreatic tissue (1124.5 ms ± 95.7 ms vs 784.3 ms ± 41.8 ms, p < 0.001). After short-term CST (4 weeks), T1 relaxation time of AIP already shortened significantly (957.2 ms ± 97.3 ms, p < 0.001). After mid-term CST (12 weeks), the T1 relaxation time further shortened towards normalization (844.2 ms ± 71.6 ms, p < 0.001). In 33 AIP patients with elevated serum IgG4 at baseline, T1 relaxation time demonstrated a significant positive correlation with serum IgG4 level (r = 0.329, p = 0.011). In six AIP patients with normal serum IgG4 level at baseline, T1 relaxation time shortening preceded or was in accordance with symptom relief. CONCLUSIONS: Native T1 mapping can be used to assess parenchymal inflammation of AIP and to quantify response to treatment. It provides a quantitative outcome surrogate for AIP. KEY POINTS: • Parenchymal inflammation in autoimmune pancreatitis results in T1 relaxation time elongation, which shortens after effective treatment. • T1 relaxation time of the pancreas correlates with serum IgG4 level, and in serum IgG4-negative AIP patients, T1 relaxation time shortening predicts clinical improvement. • T1 mapping provides a quantitative outcome surrogate for AIP.