• Gd-EOB-DTPA-enhanced T1 relaxometry quantifies liver function • Gd-EOB-DTPA-enhanced MR relaxometry may provide parameters for assessing liver function before surgery • Gd-EOB-DTPA-enhanced MR relaxometry may be useful for monitoring liver disease progression • Gd-EOB-DTPA-enhanced MR relaxometry has the potential to become a novel liver function index.
Objectives To investigate the feasibility of a deep learning-accelerated T2-weighted turbo spin echo (TSE) sequence (T2DL) applied to female pelvic MRI, using standard T2-weighted TSE (T2S) as reference. Methods In total, 24 volunteers and 48 consecutive patients with benign uterine diseases were enrolled. Patients in the menstrual phase were excluded. T2S and T2DL sequences in three planes were performed for each participant. Quantitative image evaluation was conducted by calculating the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Image geometric distortion was evaluated by measuring the diameters in all three directions of the uterus and lesions. Qualitative image evaluation including overall image quality, artifacts, boundary sharpness of the uterine zonal layers, and lesion conspicuity were assessed by three radiologists using a 5-point Likert scale, with 5 indicating the best quality. Comparative analyses were conducted for the two sequences. Results T2DL resulted in a 62.7% timing reduction (1:54 min for T2DL and 5:06 min for T2S in axial, sagittal, and coronal imaging, respectively). Compared to T2S, T2DL had significantly higher SNR (p ≤ 0.001) and CNR (p ≤ 0.007), and without geometric distortion (p = 0.925–0.981). Inter-observer agreement regarding qualitative evaluation was excellent (Kendall’s W > 0.75). T2DL provided superior image quality (all p < 0.001), boundary sharpness of the uterine zonal layers (all p < 0.001), lesion conspicuity (p = 0.002, p < 0.001, and p = 0.021), and fewer artifacts (all p < 0.001) in sagittal, axial, and coronal imaging. Conclusions Compared with standard TSE, deep learning-accelerated T2-weighted TSE is feasible to reduce acquisition time of female pelvic MRI with significant improvement of image quality.
Objectives: The aim of this study was to evaluate the usefulness of breath-hold turbo spin echo with deep learning-based reconstruction (BH-DL-TSE) in acquiring fat-suppressed T2-weighted images (FS-T2WI) of the liver by comparing this method with conventional free-breathing turbo spin echo (FB-TSE) and breath-hold half Fourier single-shot turbo spin echo with deep learning-based reconstruction (BH-DL-HASTE). Materials and Methods: The study cohort comprised 111 patients with suspected liver disease who underwent 3 T magnetic resonance imaging. Fifty-eight focal solid liver lesions ≥10 mm were also evaluated. Three sets of FS-T2WI were acquired using FB-TSE, prototypical BH-DL-TSE, and prototypical BH-DL-HASTE, respectively. In the qualitative analysis, 2 radiologists evaluated the image quality using a 5-point scale. In the quantitative analysis, we calculated the lesionto-liver signal intensity ratio (LEL-SIR). Friedman test and Dunn multiple comparison test were performed to assess differences among 3 types of FS-T2WI with respect to image quality and LEL-SIR. Results: The mean acquisition time was 4 minutes and 43 seconds ± 1 minute and 21 seconds (95% confidence interval, 4 minutes and 28 seconds to 4 minutes and 58 seconds) for FB-TSE, 40 seconds for BH-DL-TSE, and 20 seconds for BH-DL-HASTE. In the qualitative analysis, BH-DL-HASTE resulted in the fewest respiratory motion artifacts ( P < 0.0001). BH-DL-TSE and FB-TSE exhibited significantly less motion-related signal loss and clearer intrahepatic vessels than BH-DL-HASTE ( P < 0.0001). Regarding the edge sharpness of the left lobe, BH-DL-HASTE scored the highest ( P < 0.0001), and BH-DL-TSE scored higher than FB-TSE ( P = 0.0290). There were no significant differences among 3 types of FS-T2WI with respect to the edge sharpness of the right lobe ( P = 0.1290), lesion conspicuity ( P = 0.5292), and LEL-SIR ( P = 0.6026). Conclusions: BH-DL-TSE provides a shorter acquisition time and comparable or better image quality than FB-TSE, and could replace FB-TSE in acquiring FS-T2WI of the liver. BH-DL-TSE and BH-DL-HASTE have their own advantages and may be used complementarily.
BackgroundDemand 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).PurposeTo compare conventional bpMRIs (CL‐bpMRI) with bpMRIs including a deep learning‐accelerated T2WI (DL‐bpMRI) in diagnosing prostate cancer.Study TypeRetrospective.PopulationEighty 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 SequencesA 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.AssessmentCL‐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 TestsQuality 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.ResultsEighty 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).ConclusionDeep learning‐accelerated T2‐weighted imaging may potentially be used to decrease acquisition time for bpMRI.Evidence Level3.Technical EfficacyStage 2.
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