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.

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.

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.

BACKGROUND AND PURPOSE: 3D MR neurography is a useful diagnostic tool in head and neck disorders, but neurographic imaging remains challenging in this region. Optimal sequences for nerve visualization have not yet been established and may also differ between nerves. While deep learning reconstruction can enhance nerve depiction, particularly at 1.5T, studies in the head and neck are lacking. The purpose of this study was to compare DESS and post-contrast STIR sequences in deep-learning -reconstructed 3D MR neurography of the extraforaminal cranial and spinal nerves at 1.5T. MATERIALS AND METHODS: Eighteen consecutive exams of 18 patients undergoing head-and-neck MRI at 1.5T were retrospectively included (mean age: 51 ± 14 years, 11 female). 3D DESS and post-contrast 3D STIR sequences were obtained as part of the standard protocol, and reconstructed with a prototype deep learning algorithm. Two blinded readers qualitatively evaluated visualization of the inferior alveolar, lingual, facial, hypoglossal, greater occipital, lesser occipital and greater auricular nerves, as well as overall image quality, vascular suppression and artifacts. Additionally, apparent signal-to-noise ratio (aSNR) and contrast-to-noise ratios (aCNR) of the inferior alveolar and greater occipital nerve were measured. Visual ratings and quantitative measurements, respectively, were compared between sequences using Wilcoxon signed-rank test. RESULTS: DESS demonstrated significantly improved visualization of the lesser occipital nerve, greater auricular nerve and proximal greater occipital nerve (p < 0.015). Post-contrast STIR showed significantly enhanced visualization of the lingual nerve, hypoglossal nerve and distal inferior alveolar nerve (p < 0.001). The facial nerve, proximal inferior alveolar nerve and distal greater occipital nerve did not demonstrate significant differences in visualization between sequences (p > 0.08). There was also no significant difference for overall image quality and artifacts. Post-contrast STIR achieved superior vascular suppression, reaching statistical significance for one reader (p = 0.039). Quantitatively, there was no significant difference between sequences (p > 0.05). CONCLUSIONS: Our findings suggest that 3D DESS generally provides improved visualization of spinal nerves, while post-contrast 3D STIR facilitates enhanced delineation of extraforaminal cranial nerves. ABBREVIATIONS: DESS = Dual-echo steady-state; DL = Deep learning; FN = Facial nerve; GAN = Greater auricular nerve; GON = Greater occipital nerve; HN = Hypoglossal nerve; IAN = Inferior alveolar nerve; LN = Lingual nerve; LON = Lesser occipital nerve; MRN = Magnetic resonance neurography

To evaluate the impact of deep learning-based reconstruction (DLRecon) on bone assessment in zero echo-time (ZTE) MRI of the knee at 1.5 Tesla. This retrospective study included 48 consecutive exams of 46 patients (23 females) who underwent clinically indicated knee MRI at 1.5 Tesla. Standard imaging protocol comprised a sagittal prescribed, isotropic ZTE sequence. ZTE image reconstruction was performed with a standard-of-care (non-DL) and prototype DLRecon method. Exams were divided into subsets with and without osseous pathology based on the radiology report. Using a 4-point scale, two blinded readers qualitatively graded features of bone depiction including artifacts and conspicuity of pathology including diagnostic certainty in the respective subsets. Quantitatively, one reader measured signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of bone. Comparative analyses were conducted to assess the differences between the reconstruction methods. In addition, interreader agreement was calculated for the qualitative gradings. DLRecon significantly improved gradings for bone depiction relative to non-DL reconstruction (all, p < 0.05), while there was no significant difference with regards to artifacts (both, median score of 0; p = 0.058). In the subset with pathologies, conspicuity of pathology and diagnostic confidence were also scored significantly higher in DLRecon compared to non-DL (median 3 vs 2; p ≤ 0.03). Interreader agreement ranged from moderate to almost-perfect (κ = 0.54-0.88). Quantitatively, DLRecon demonstrated significantly enhanced CNR and SNR of bone compared to non-DL (p < 0.001). ZTE MRI with DLRecon improved bone depiction in the knee, compared to non-DL. Additionally, DLRecon increased conspicuity of osseous findings together with diagnostic certainty.

Objectives: The introduction of photon-counting detector computed tomography (PCCT) has allowed for significant dose reductions compared to energy-integrating-detector CT, making it particularly relevant for applications such as lung cancer screening. Coronary artery calcification is an important incidental finding in lung cancer screening, warranting attention in this context. This study aims to assess the impact of dose reduction to levels comparable to that of a chest radiography on opportunistic evaluation of coronary artery calcification on PCCTs of the chest. Materials and Methods: Sixty-eight out of 115 patients with age >45 years and body mass index ≤30 kg/m ² undergoing noncontrast low- and chest-radiography-comparable-dose PCCT in the same session were included. Scans were performed at 100 kVp with image quality settings 12 (low-dose) and 2 (radiography-comparable-dose). Visual calcium scoring was conducted by 2 readers using 2 scoring approaches (CAD-RADS 2.0 and Shemesh). Semiautomated quantitative analysis was performed using commercially available software. Image quality was evaluated using 5-point Likert scales. Results: Sixty-eight patients (65.9 ± 8.6 years; 49 men) were subjected to evaluation. CTDI was lower for radiography-dose scans (0.11 mGy vs 0.68 mGy; P < 0.001). Image quality was found to be inferior for radiography-dose scans (4.01 vs 2.03; P < 0.001). In both visual scoring approaches, coronary calcification was scored significantly lower in radiography-dose scans ( P < 0.001 for both) with almost perfect reader agreement (CAD-RADS score Cohen's kappa =0.82; Shemesh score Cohen's kappa =0.81), most importantly reclassification from mild to absent occurred for CAD-RADS score in 31%/21% of cases and for Shemesh score in 23%/15% of cases (reader 1/reader 2). Semiautomated assessment showed no significant differences between low and radiography dose ( P = 0.121). Strong correlation between scores (Pearson's r = 0.98, P < 0.001) with good agreement (Cohen's kappa =0.61) was found. Conclusions: Coronary artery calcifications are underestimated on radiography-dose PCCT visually, whereas semiautomatic analysis provides more robust results. Visual underestimation of coronary artery calcification in low-dose imaging is further amplified with the additional dose reduction to radiography-comparable dose levels, indicating that while estimation of high cardiovascular risk is feasible, exclusion of such risk is not possible.