Deep Learning Convolutional Neural Network Reconstruction and Radial k-Space Acquisition MR Technique for Enhanced Detection of Retropatellar Cartilage Lesions of the Knee Joint

OBJECTIVES To assess diagnostic performance of standard radial k-space (PROPELLER) MRI sequences and compare with accelerated acquisitions combined with a deep learning-based convolutional neural network (DL-CNN) reconstruction for evaluation of the knee joint. METHODS Thirty-five patients undergoing MR imaging of the knee at 1.5 T were prospectively included. Two readers evaluated image quality and diagnostic confidence of standard and DL-CNN accelerated PROPELLER MR sequences using a four-point Likert scale. Pathological findings of bone, cartilage, cruciate and collateral ligaments, menisci, and joint space were analyzed. Inter-reader agreement (IRA) for image quality and diagnostic confidence was assessed using intraclass coefficients (ICC). Cohen's Kappa method was used for evaluation of IRA and consensus between sequences in assessing different structures. In addition, image quality was quantitatively evaluated by signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measurements. RESULTS Mean acquisition time of standard vs. DL-CNN sequences was 10 min 3 s vs. 4 min 45 s. DL-CNN sequences showed significantly superior image quality and diagnostic confidence compared to standard MR sequences. There was moderate and good IRA for assessment of image quality in standard and DL-CNN sequences with ICC of 0.524 and 0.830, respectively. Pathological findings of the knee joint could be equally well detected in both sequences (κ-value of 0.8). Retropatellar cartilage could be significantly better assessed on DL-CNN sequences. SNR and CNR was significantly higher for DL-CNN sequences (both p < 0.05). CONCLUSIONS In MR imaging of the knee, DL-CNN sequences showed significantly higher image quality and diagnostic confidence compared to standard PROPELLER sequences, while reducing acquisition time substantially. Both sequences perform comparably in the detection of knee-joint pathologies, while DL-CNN sequences are superior for evaluation of retropatellar cartilage lesions

Diagnostic performance of deep learning-based reconstruction algorithm in 3D MR neurography

OBJECTIVE The study aims to evaluate the diagnostic performance of deep learning-based reconstruction method (DLRecon) in 3D MR neurography for assessment of the brachial and lumbosacral plexus. MATERIALS AND METHODS Thirty-five exams (18 brachial and 17 lumbosacral plexus) of 34 patients undergoing routine clinical MR neurography at 1.5 T were retrospectively included (mean age: 49 ± 12 years, 15 female). Coronal 3D T2-weighted short tau inversion recovery fast spin echo with variable flip angle sequences covering plexial nerves on both sides were obtained as part of the standard protocol. In addition to standard-of-care (SOC) reconstruction, k-space was reconstructed with a 3D DLRecon algorithm. Two blinded readers evaluated images for image quality and diagnostic confidence in assessing nerves, muscles, and pathology using a 4-point scale. Additionally, signal-to-noise ratio (SNR) and contrast-to-noise ratios (CNR) between nerve, muscle, and fat were measured. For comparison of visual scoring result non-parametric paired sample Wilcoxon signed-rank testing and for quantitative analysis paired sample Student's t-testing was performed. RESULTS DLRecon scored significantly higher than SOC in all categories of image quality (p < 0.05) and diagnostic confidence (p < 0.05), including conspicuity of nerve branches and pathology. With regard to artifacts there was no significant difference between the reconstruction methods. Quantitatively, DLRecon achieved significantly higher CNR and SNR than SOC (p < 0.05). CONCLUSION DLRecon enhanced overall image quality, leading to improved conspicuity of nerve branches and pathology, and allowing for increased diagnostic confidence in evaluation of the brachial and lumbosacral plexus

Application of deep learning-based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time

OBJECTIVES To compare the image quality and diagnostic performance of conventional motion-corrected periodically rotated overlapping parallel line with enhanced reconstruction (PROPELLER) MRI sequences with post-processed PROPELLER MRI sequences using deep learning-based (DL) reconstructions. METHODS In this prospective study of 30 patients, conventional (19 min 18 s) and accelerated MRI sequences (7 min 16 s) using the PROPELLER technique were acquired. Accelerated sequences were post-processed using DL. The image quality and diagnostic confidence were qualitatively assessed by 2 readers using a 5-point Likert scale. Analysis of the pathological findings of cartilage, rotator cuff tendons and muscles, glenoid labrum and subacromial bursa was performed. Inter-reader agreement was calculated using Cohen's kappa statistic. Quantitative evaluation of image quality was measured using the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). RESULTS Mean image quality and diagnostic confidence in evaluation of all shoulder structures were higher in DL sequences (p value = 0.01). Inter-reader agreement ranged between kappa values of 0.155 (assessment of the bursa) and 0.947 (assessment of the rotator cuff muscles). In 17 cases, thickening of the subacromial bursa of more than 2 mm was only visible in DL sequences. The pathologies of the other structures could be properly evaluated by conventional and DL sequences. Mean SNR (p value = 0.01) and CNR (p value = 0.02) were significantly higher for DL sequences. CONCLUSIONS The accelerated PROPELLER sequences with DL post-processing showed superior image quality and higher diagnostic confidence compared to the conventional PROPELLER sequences. Subacromial bursa can be thoroughly assessed in DL sequences, while the other structures of the shoulder joint can be assessed in conventional and DL sequences with a good agreement between sequences. KEY POINTS • MRI of the shoulder requires long scan times and can be hampered by motion artifacts. • Deep learning-based convolutional neural networks are used to reduce image noise and scan time while maintaining optimal image quality. The radial k-space acquisition technique (PROPELLER) can reduce the scan time and has potential to reduce motion artifacts. • DL sequences show a higher diagnostic confidence than conventional sequences and therefore are preferred for assessment of the subacromial bursa, while conventional and DL sequences show comparable performance in the evaluation of the shoulder joint

Evaluation der diagnostischen Genauigkeit der Computertomographie (CT) für die nicht-invasive Beurteilung der Herzfunktion im Vergleich zur Magnetresonanztomographie (MRT).

Objectives To evaluate the accuracy of computed tomography (CT) in the assessment of left ventricular (LV) function and compare with magnetic resonance imaging (MRI). Methods We have systematically reviewed MEDLINE, EMBASE, and ISI Web of Science. The inclusion criteria were: ejection fraction (EF), end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), and left ventricular mass (LVM). Bland Altman analysis was used in the assessment of differences between modalities and limits of agreement (LoA) were provided. Cochran’s Q test and Higgins I2 statistic were used for the evaluation of heterogeneity. Estimation of heterogeneity variance was performed using the DerSimonian-Laird method. Results Totally 53 studies (1814 patients) were chosen and evaluated. The mean difference between CT and MRI was -0.56% (LoA, -11.6 to 10.5%) for EF, 2.62 ml (-34.1 to 39.3 ml) for EDV and 1.61 ml (-22.4 to 25.7 ml) for ESV, 3.21 ml (-21.8 to 28.3 ml) for SV and 0.13 g (-28.2 to 28.4 g) for LVM. Wall motion abnormalities on a per-segment basis were detected with CT with 90% sensitivity and 97% specificity. Conclusions The assessment of LV function parameters is accurate with CT, but the limits of agreement compared with MRI are moderately wide. CT can also detect wall motion deficits with high accuracy.Zielsetzung Ziel der Studie war die Evaluation der diagnostischen Genauigkeit der Computertomographie (CT) für die Beurteilung der Herzfunktion im Vergleich zur Magnetresonanztomographie (MRT). Methoden Die Datenbanken MEDLINE, EMBASE und ISI Web of Science wurden systematisch überprüft. Die Suchbegriffe umfassten: Ejektionsfraktion (EF), enddiastolisches Volumen (EDV), endsystolisches Volumen (ESV), Schlagvolumen (SV) und linksventrikuläre Masse (LVM). Unterschiede zwischen den Modalitäten wurden mithilfe von Limits of agreements (LoA) analysiert. Das Publikationsbias wurde mit Egger- Regressionstest gemessen. Die Heterogenität wurde mittels Cochrans Q-Test und Higgins-I2-Statistik bewertet. Im Falle einer Heterogenität wurde die DerSimon-Laird-Methode zur Schätzung der Heterogenitätsvarianz verwendet. Ergebnisse 53 Studien mit insgesamt 1814 Patienten wurden identifiziert und analysiert. Der mittlere Unterschied zwischen CT und MRT betrug für die EF -0,56% (LoA, -11,6 bis 10,5%), für das EDV 2,62 ml (-34,1 bis 39,3 ml) und für das ESV 1,61 ml (-22,4 bis 25,7 ml), 3,21 ml -21,8 bis 28,3 ml) für das SV und 0,13 g (-28,2 bis 28,4 g) für die LVM. Die CT erkannte Wandbewegungsanomalien auf einer Segmentebene mit 90% Sensitivität und 97% Spezifität. Schlussfolgerung Die Evaluation der LV Funktionsparameter ist möglich mittels CT, jedoch zeigen die LoA im Vergleich zur MRT eine relativ breite Streuung. Für die Beurteilung von Wandbewegungsstörungen des Myokards zeigt die CT eine hohe diagnostische Genauigkeit

Enhanced bone assessment of the shoulder using zero-echo time MRI with deep-learning image reconstruction

OBJECTIVES To assess a deep learning-based reconstruction algorithm (DLRecon) in zero echo-time (ZTE) MRI of the shoulder at 1.5 Tesla for improved delineation of osseous findings. METHODS In this retrospective study, 63 consecutive exams of 52 patients (28 female) undergoing shoulder MRI at 1.5 Tesla in clinical routine were included. Coronal 3D isotropic radial ZTE pulse sequences were acquired in the standard MR shoulder protocol. In addition to standard-of-care (SOC) image reconstruction, the same raw data was reconstructed with a vendor-supplied prototype DLRecon algorithm. Exams were classified into three subgroups: no pathological findings, degenerative changes, and posttraumatic changes, respectively. Two blinded readers performed bone assessment on a 4-point scale (0-poor, 3-perfect) by qualitatively grading image quality features and delineation of osseous pathologies including diagnostic confidence in the respective subgroups. Quantitatively, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of bone were measured. Qualitative variables were compared using the Wilcoxon signed-rank test for ordinal data and the McNemar test for dichotomous variables; quantitative measures were compared with Student's t-testing. RESULTS DLRecon scored significantly higher than SOC in all visual metrics of image quality (all, p < 0.03), except in the artifact category (p = 0.37). DLRecon also received superior qualitative scores for delineation of osseous pathologies and diagnostic confidence (p ≤ 0.03). Quantitatively, DLRecon achieved superior CNR (95 CI [1.4-3.1]) and SNR (95 CI [15.3-21.5]) of bone than SOC (p < 0.001). CONCLUSION DLRecon enhanced image quality in ZTE MRI and improved delineation of osseous pathologies, allowing for increased diagnostic confidence in bone assessment

Infected Extrahepatic Splanchnic Venous Stent(-Grafts): Clinical Presentation, Imaging, and Treatment in Three Patients

This brief report describes 3 patients with infected extrahepatic splanchnic venous stents or stent grafts. These devices had been placed to treat prehepatic portal hypertension 4 wk, 3 mo, and 31 mo, respectively, before readmission for fever. Blood cultures and fluorine-18 fludeoxyglucose positron emission tomography/CT were positive in all. With systemic antibiotic treatment, 2 patients showed a clinical recovery. In the third patient, antibiotic treatment failed. Therefore, the infected stent graft was surgically removed and a splenorenal shunt was created. No recurrent splanchnic venous infection was observed in these 3 patients

Comparison of AI-powered 3D automated ultrasound tomography with standard handheld ultrasound for the visualization of the hands-clinical proof of concept

OBJECTIVE To assess the ability of a newly developed AI-powered ultrasound 3D hand scanner to visualize joint structures in healthy hands and detect degenerative changes in cadaveric hands. MATERIALS AND METHODS Twelve individuals (6 males, 6 females, age 43.5 ± 17.8 years) underwent four scans with the 3D ultrasound tomograph (right and left hand, dorsal and palmar, respectively) as well as four sets of handheld ultrasound of predefined anatomic regions. The 3D ultrasound tomographic images and the standard handheld ultrasound images were assessed by two radiologists with regard to visibility of bone contour, joint capsule and space, and tendons. In addition, three cadaveric hands were scanned with the 3D ultrasound tomograph and CT. RESULTS Mean scan time for both hands was significantly faster with handheld ultrasound (10 min 30 s ± 95 s) compared to 3D ultrasound tomography (32 min 9 s ± 6 s; p < 0.001). Interreader and intermodality agreement was moderate (0.4 < κ ≤ 0.6) to substantial (0.6 < κ ≤ 0.8). Overall visibility of joint structures was comparable between the modalities at the level of the wrist (p = 0.408), and significantly better with handheld ultrasound at the level of the finger joints and the thumb (both p < 0.001). The 3D ultrasound tomograph was able to detect osteophytes in cadaveric hands which were confirmed by CT. CONCLUSION The AI-powered 3D ultrasound tomograph was able to visualize joint structures in healthy hands and singular osteophytes in cadaveric hands. Further technical improvements are necessary to shorten scan times and improve automated scanning of the finger joints and the thumb

Suspensory Ligaments of the Female Genital Organs: MRI Evaluation with Intraoperative Correlation

The uterus, which plays an important role in the reproductive process, provides a home for the developing fetus and so must be in a stable, though flexible, location. Various structures with suspensory ligaments help provide this berth. MRI with high spatial resolution allows us to detect and evaluate these relatively fine structures. Under physiologic conditions, MRI can be used to depict uterine and ovarian ligaments (ie, the uterosacral, cardinal, and round ligaments, as well as the suspensory ligament of the ovary). In the presence of pathologic conditions (inflammation, endometriosis, tumors), the suspensory ligaments may appear thickened or invaded, which makes their delineation easier. Understanding the normal anatomy of the suspensory ligaments of the female genital organs and using a standardized nomenclature are essential for identifying and reporting related pathologic conditions. The female pelvic anatomy and the suspensory ligaments of the female genital organs are described as depicted with MRI. Also, the compartmental anatomy of the female pelvis is explained, including the extraperitoneal pelvic spaces. Finally, a checklist is provided for structured reporting of the MRI findings in the female pelvis. Online supplemental material is available for this article. ©RSNA, 2018

3D zero-echo time and 3D T1-weighted gradient-echo MRI sequences as an alternative to CT for the evaluation of the lumbar facet joints and lumbosacral transitional vertebrae

BACKGROUND Computed tomography (CT) is the reference standard for assessment of the bone. Magnetic resonance imaging (MRI) developments enable a CT-like visualization of the osseous structures. PURPOSE To assess the diagnostic performance of 3D zero-echo time (3D-ZTE) and 3D T1-weighted gradient-echo (3D-T1GRE) MRI sequences for the evaluation of lumbar facet joints (LFJs) and the detection of lumbosacral transitional vertebrae (LSTV) using CT as the reference standard. MATERIAL AND METHODS In total, 87 adult patients were included in this prospective study. Evaluation of degenerative changes of the facet joints at the L3/L4, L4/L5, and L5/S1 levels on both sides was performed by two readers using a 4-point Likert scale. LSTV were classified according to Castelvi et al. Image quality was quantitatively measured using the signal-to-noise (SNR) and contrast-to-noise (CNR) ratios. Intra-reader, inter-reader, and inter-modality reliability were calculated using Cohen's kappa statistic. RESULTS Intra-reader agreement for 3D-ZTE, 3D-T1GRE, and CT was 0.607, 0.751, and 0.856 and inter-reader agreement was 0.535, 0.563, and 0.599, respectively. The inter-modality agreement between 3D-ZTE and CT was 0.631 and between 3D-T1GRE and CT 0.665. A total of LSTV were identified in both MR sequences with overall comparable accuracy compared to CT. Mean SNR for bone, muscle, and fat was highest for 3D-T1GRE and mean CNR was highest for CT. CONCLUSION 3D-ZTE and 3D-T1GRE MRI sequences can assess the LFJs and LSTV and may serve as potential alternatives to CT

Differentiation of inflammatory from degenerative changes in the sacroiliac joints by machine learning supported texture analysis

PURPOSE To compare the diagnostic performance of texture analysis (TA) against visual qualitative assessment in the differentiation of spondyloarthritis (SpA) from degenerative changes in the sacroiliac joints (SIJ). METHOD Ninety patients referred for suspected inflammatory lower back pain from the rheumatology department were retrospectively included at our university hospital institution. MRI at 3 T of the lumbar spine and SIJ was performed with oblique coronal T1-weighted (w), fluid-sensitive fat-saturated (fs) TIRM and fsT1w intravenously contrast-enhanced (CE) images. Subjects were divided into three age- and gender-matched groups (30 each) based on definite clinical diagnosis serving as clinical reference standard with either degenerative, inflammatory (SpA) or no changes of the SIJ. SIJ were rated qualitatively by two independent radiologists and quantitatively by region-of-interest-based TA with 304 features subjected to machine learning logistic regression with randomized ten-fold selection of training and validation data. Qualitative and quantitative results were evaluated for diagnostic performance and compared against clinical reference standard. RESULTS Agreement of radiologist's diagnose with clinical reference was fair for both readers (κ = 0.32 and 0.44). ROC statistics revealed significant outperformance of TA compared to qualitative ratings for differentiation of SpA from remainder (AUC = 0.89 vs. 0.75), SpA from degenerative (AUC = 0.91 vs. 0.67) and TIRM-positive SpA (i.e. with bone marrow edema) from remainder cases (AUC = 0.95 vs. 0.76). T1w-CE images were the most important discriminator for detection of SpA. CONCLUSIONS TA is superior to qualitative assessment for the differentiation of inflammatory from degenerative changes of the SIJ. Intravenous CE-images increase diagnostic yield in quantitative TA