Purpose To develop a deep learning reconstruction approach to improve the reconstruction speed and quality of highly undersampled variable-density single-shot fast spin-echo imaging by using a variational network (VN), and to clinically evaluate the feasibility of this approach. Materials and Methods Imaging was performed with a 3.0-T imager with a coronal variable-density single-shot fast spin-echo sequence at 3.25 times acceleration in 157 patients referred for abdominal imaging (mean age, 11 years; range, 1-34 years; 72 males [mean age, 10 years; range, 1-26 years] and 85 females [mean age, 12 years; range, 1-34 years]) between March 2016 and April 2017. A VN was trained based on the parallel imaging and compressed sensing (PICS) reconstruction of 130 patients. The remaining 27 patients were used for evaluation. Image quality was evaluated in an independent blinded fashion by three radiologists in terms of overall image quality, perceived signal-to-noise ratio, image contrast, sharpness, and residual artifacts with scores ranging from 1 (nondiagnostic) to 5 (excellent). Wilcoxon tests were performed to test the hypothesis that there was no significant difference between VN and PICS. Results VN achieved improved perceived signal-to-noise ratio (P = .01) and improved sharpness (P < .001), with no difference in image contrast (P = .24) and residual artifacts (P = .07). In terms of overall image quality, VN performed better than did PICS (P = .02). Average reconstruction time ± standard deviation was 5.60 seconds ± 1.30 per section for PICS and 0.19 second ± 0.04 per section for VN. Conclusion Compared with the conventional parallel imaging and compressed sensing reconstruction (PICS), the variational network (VN) approach accelerates the reconstruction of variable-density single-shot fast spin-echo sequences and achieves improved overall image quality with higher perceived signal-to-noise ratio and sharpness. © RSNA, 2018 Online supplemental material is available for this article.

Purpose To enable rapid imaging with a scan time–efficient 3D cones trajectory with a deep‐learning off‐resonance artifact correction technique. Methods A residual convolutional neural network to correct off‐resonance artifacts (Off‐ResNet) was trained with a prospective study of pediatric MRA exams. Each exam acquired a short readout scan (1.18 ms ± 0.38) and a long readout scan (3.35 ms ± 0.74) at 3 T. Short readout scans, with longer scan times but negligible off‐resonance blurring, were used as reference images and augmented with additional off‐resonance for supervised training examples. Long readout scans, with greater off‐resonance artifacts but shorter scan time, were corrected by autofocus and Off‐ResNet and compared with short readout scans by normalized RMS error, structural similarity index, and peak SNR. Scans were also compared by scoring on 8 anatomical features by two radiologists, using analysis of variance with post hoc Tukey's test and two one‐sided t‐tests. Reader agreement was determined with intraclass correlation. Results The total scan time for long readout scans was on average 59.3% shorter than short readout scans. Images from Off‐ResNet had superior normalized RMS error, structural similarity index, and peak SNR compared with uncorrected images across ±1 kHz off‐resonance (P < .01). The proposed method had superior normalized RMS error over −677 Hz to +1 kHz and superior structural similarity index and peak SNR over ±1 kHz compared with autofocus (P < .01). Radiologic scoring demonstrated that long readout scans corrected with Off‐ResNet were noninferior to short readout scans (P < .05). Conclusion The proposed method can correct off‐resonance artifacts from rapid long‐readout 3D cones scans to a noninferior image quality compared with diagnostically standard short readout scans.

Background: View-sharing (VS) increases spatiotemporal resolution in dynamic contrast-enhanced (DCE) MRI by sharing high-frequency k-space data across temporal phases. This temporal sharing results in respiratory motion within any phase to propagate artifacts across all shared phases. Compressed sensing (CS) eliminates the need for VS by recovering missing k-space data from pseudorandom undersampling, reducing temporal blurring while maintaining spatial resolution. Purpose: To evaluate a CS reconstruction algorithm on undersampled DCE-MRI data for image quality and hepatocellular carcinoma (HCC) detection. Study Type: Retrospective. Subjects: Fifty consecutive patients undergoing MRI for HCC screening (29 males, 21 females, 52-72 years). Field Strength/Sequence: 3.0T MRI. Multiphase 3D-SPGR T 1 -weighted sequence undersampled in arterial phases with a complementary Poisson disc sampling pattern reconstructed with VS and CS algorithms. Assessment: VS and CS reconstructions evaluated by blinded assessments of image quality and anatomic delineation on Likert scales (1-4 and 1-5, respectively), and HCC detection by OPTN/UNOS criteria including a diagnostic confidence score (1-5). Blinded side-by-side reconstruction comparisons for lesion depiction and overall series preference (-3-3). Statistical Analysis: Two-tailed Wilcoxon signed rank tests for paired nonparametric analyses with Bonferroni-Holm multiple-comparison corrections. McNemar's test for differences in lesion detection frequency and transplantation eligibility. Results: CS compared with VS demonstrated significantly improved contrast (mean 3.6 vs. 2.9, P < 0.0001) and less motion artifact (mean 3.6 vs. 3.2, P = 0.006). CS compared with VS demonstrated significantly improved delineations of liver margin (mean 4.5 vs. 3.8, P = 0.0002), portal veins (mean 4.5 vs. 3.7, P < 0.0001), and hepatic veins (mean 4.6 vs. 3.5, P < 0.0001), but significantly decreased delineation of hepatic arteries (mean 3.2 vs. 3.7, P = 0.004). No significant differences were seen in the other assessments. Data Conclusion: Applying a CS reconstruction to data acquired for a VS reconstruction significantly reduces motion artifacts in a clinical DCE protocol for HCC screening. Level of Evidence: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2019;49:984-993. View this article online at wileyonlinelibrary.com.

Symptoms from giant cavernous hemangiomas of the liver (those larger than 4 cm in diameter) in adults are nonspecific and related to the size of the tumor and pressure on adjacent organs, such as stomach and gallbladder. Pain can occur from hemorrhage or infection and rarely spontaneous rupture.1 Bleeding diathesis is seen due to a variety of mechanisms including thrombocytopenia and disseminated intravascular coagulation. 2Celiac angiography and dynamic computed tomography (CT) show a characteristic picture.3,4 Since percutaneous liver biopsy is contraindicated, it is important to recognize the radiologic picture of these tumors in order to make the diagnosis and plan surgery when appropriate.We describe a patient with massive cavernous hemangioma; to our knowledge, this is the largest such hemangioma reported. Our patient had an atypical celiac angiogram, but a characteristic dynamic CT scan appearance and posed a management problem due to the massive size and diffuse nature of the tumor. Case ReportA 47-year-old Sudanese man had a six-year history of increasing abdominal girth associated with anorexia, weight loss, and dyspnea on exertion. He had been examined at another hospital, where a wedge biopsy of the liver taken during laparotomy was said to have shown hemangiomas. The histology slides were not then available for review.He appeared chronically ill with diffuse muscle wasting. He was well androgenized. Examination of the cardiovascular, respiratory, and nervous systems showed no abnormalities. A large, nodular, nontender abdominal mass extended from both costal margins to the pelvis (Figure 1). Inferiorly, the mass divided into two lobes over which bruits could be heard. Findings on rectal examination were normal.

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