Deep learning MR reconstruction in knees and ankles in children and young adults. Is it ready for clinical use?

OBJECTIVE: To evaluate the diagnostic performance and image quality of accelerated Turbo Spin Echo sequences using deep-learning (DL) reconstructions compared to conventional sequences in knee and ankle MRIs of children and young adults. MATERIALS AND METHODS: IRB-approved prospective study consisting of 49 MRIs from 48 subjects (10 males, mean age 16.4 years, range 7-29 years), with each MRI consisting of both conventional and DL sequences. Sequences were evaluated blindly to determine predictive values, sensitivity, and specificity of DL sequences using conventional sequences and knee arthroscopy (if available) as references. Physeal patency and appearance were evaluated. Qualitative parameters were compared. Presence of undesired image alterations was assessed. RESULTS: The prevalence of abnormal findings in the knees and ankles were 11.7% (75/640), and 11.5% (19/165), respectively. Using conventional sequences as reference, sensitivity and specificity of DL sequences in knees were 90.7% and 99.3%, and in ankles were 100.0% and 100.0%. Using arthroscopy as reference, sensitivity and specificity of DL sequences were 80.0% and 95.8%, and of conventional sequences were 80.0% and 97.9%. Agreement of physeal status was 100.0%. DL sequences were qualitatively "same-or-better" compared to conventional (p < 0.032), except for pixelation artifact for the PDFS sequence (p = 0.233). No discrete image alteration was identified in the knee DL sequences. In the ankle, we identified one DL artifact involving a tendon (0.8%, 1/125). DL sequences were faster than conventional sequences by a factor of 2 (p < 0.001). CONCLUSION: In knee and ankle MRIs, DL sequences provided similar diagnostic performance and "same-or-better" image quality than conventional sequences at half the acquisition time.

Deep Learning-Based Reconstruction of 3D T1 SPACE Vessel Wall Imaging Provides Improved Image Quality with Reduced Scan Times: A Preliminary Study.

BACKGROUND AND PURPOSE: Intracranial vessel wall imaging is technically challenging to implement, given the simultaneous requirements of high spatial resolution, excellent blood and CSF signal suppression, and clinically acceptable gradient times. Herein, we present our preliminary findings on the evaluation of a deep learning-optimized sequence using T1-weighted imaging. MATERIALS AND METHODS: Clinical and optimized deep learning-based image reconstruction T1 3D Sampling Perfection with Application optimized Contrast using different flip angle Evolution (SPACE) were evaluated, comparing noncontrast sequences in 10 healthy controls and postcontrast sequences in 5 consecutive patients. Images were reviewed on a Likert-like scale by 4 fellowship-trained neuroradiologists. Scores (range, 1-4) were separately assigned for 11 vessel segments in terms of vessel wall and lumen delineation. Additionally, images were evaluated in terms of overall background noise, image sharpness, and homogeneous CSF signal. Segment-wise scores were compared using paired samples t tests. RESULTS: The scan time for the clinical and deep learning-based image reconstruction sequences were 7:26 minutes and 5:23 minutes respectively. Deep learning-based image reconstruction images showed consistently higher wall signal and lumen visualization scores, with the differences being statistically significant in most vessel segments on both pre- and postcontrast images. Deep learning-based image reconstruction had lower background noise, higher image sharpness, and uniform CSF signal. Depiction of intracranial pathologies was better or similar on the deep learning-based image reconstruction. CONCLUSIONS: Our preliminary findings suggest that deep learning-based image reconstruction-optimized intracranial vessel wall imaging sequences may be helpful in achieving shorter gradient times with improved vessel wall visualization and overall image quality. These improvements may help with wider adoption of intracranial vessel wall imaging in clinical practice and should be further validated on a larger cohort.

Diagnostic evaluation of deep learning accelerated lumbar spine MRI.

BACKGROUND AND PURPOSE: Deep learning (DL) accelerated MR techniques have emerged as a promising approach to accelerate routine MR exams. While prior studies explored DL acceleration for specific lumbar MRI sequences, a gap remains in comprehending the impact of a fully DL-based MRI protocol on scan time and diagnostic quality for routine lumbar spine MRI. To address this, we assessed the image quality and diagnostic performance of a DL-accelerated lumbar spine MRI protocol in comparison to a conventional protocol. METHODS: We prospectively evaluated 36 consecutive outpatients undergoing non-contrast enhanced lumbar spine MRIs. Both protocols included sagittal T1, T2, STIR, and axial T2-weighted images. Two blinded neuroradiologists independently reviewed images for foraminal stenosis, spinal canal stenosis, nerve root compression, and facet arthropathy. Grading comparison employed the Wilcoxon signed rank test. For the head-to-head comparison, a 5-point Likert scale to assess image quality, considering artifacts, signal-to-noise ratio (SNR), anatomical structure visualization, and overall diagnostic quality. We applied a 15% noninferiority margin to determine whether the DL-accelerated protocol was noninferior. RESULTS: No significant differences existed between protocols when evaluating foraminal and spinal canal stenosis, nerve compression, or facet arthropathy (all p > .05). The DL-spine protocol was noninferior for overall diagnostic quality and visualization of the cord, CSF, intervertebral disc, and nerve roots. However, it exhibited reduced SNR and increased artifact perception. Interobserver reproducibility ranged from moderate to substantial (κ = 0.50-0.76). CONCLUSION: Our study indicates that DL reconstruction in spine imaging effectively reduces acquisition times while maintaining comparable diagnostic quality to conventional MRI.

7T for clinical imaging of benign peripheral nerve tumors: preliminary results.

PURPOSE: MRI has become an essential diagnostic imaging modality for peripheral nerve pathology. Early MR imaging for peripheral nerve depended on inferred nerve involvement by visualizing downstream effects such as denervation muscular atrophy; improvements in MRI technology have made possible direct visualization of the nerves. In this paper, we share our early clinical experience with 7T for benign neurogenic tumors. MATERIALS: Patients with benign neurogenic tumors and 7T MRI examinations available were reviewed. Cases of individual benign peripheral nerve tumors were included to demonstrate 7T MRI imaging characteristics. All exams were performed on a 7T MRI MAGNETOM Terra using a 28-channel receive, single-channel transmit knee coil. RESULTS: Five cases of four pathologies were selected from 38 patients to depict characteristic imaging features in different benign nerve tumors and lesions using 7T MRI. CONCLUSION: The primary advantage of 7T over 3T is an increase in signal-to-noise ratio which allows higher in plane resolution so that the smallest neural structures can be seen and characterized. This improvement in MR imaging provides the opportunity for more accurate diagnosis and surgical planning in selected cases. As this technology continues to evolve for clinical purposes, we anticipate increasing applications and improved patient care using 7T MRI for the diagnosis of peripheral nerve masses.

Image Artifact Management for Clinical Magnetic Resonance Imaging on a 7 T Scanner Using Single-Channel Radiofrequency Transmit Mode.

OBJECTIVES: The aim of this work was to devise mitigation strategies for addressing a range of image artifacts on a clinical 7 T magnetic resonance imaging scanner using the regulatory-approved single-channel radiofrequency transmit mode and vendor-supplied radiofrequency coils to facilitate clinical scanning within reasonable scan times. MATERIALS AND METHODS: Optimized imaging sequence protocols were developed for routine musculoskeletal knee and neurological imaging. Sources of severe image nonuniformities were identified, and mitigation strategies were devised. A range of custom-made high permittivity dielectric pads were used to compensate for B1 and B1 inhomogeneities, and also for magnetic susceptibility-induced signal dropouts particularly in the basal regions of the temporal lobes and in the cerebellum. RESULTS: Significant improvements in image uniformity were obtained using dielectric pads in the knee and brain. A combination of small voxels, reduced field of view B0 shimming, and high in-plane parallel imaging factors helped to minimize signal loss in areas of high susceptibility-induced field distortions. The high inherent signal-to-noise ratio at 7 T allowed for high receiver bandwidths and thin slices to minimize chemical shift artifacts. Intermittent artifacts due to radiofrequency inversion pulse limitations (power, bandwidth) were minimized with dielectric pads. A patient with 2 implanted metallic cranial fixation devices located within the radiofrequency transmit field was successfully imaged, with minimal image geometric distortions. CONCLUSIONS: Challenges relating to severe image artifacts at 7 T using single-channel radiofrequency transmit functionality in the knee and brain were overcome using the approaches described in this article. The resultant high diagnostic image quality paves the way for incorporation of this technology into the routine clinical workflow. Further developmental efforts are required to expand the range of applications to other anatomical areas, and to expand the evidence- and knowledge-base relating to the safety of scanning patients with implanted metallic devices.

Uniform combined reconstruction of multichannel 7T knee MRI receive coil data without the use of a reference scan.

BACKGROUND: MR image intensity nonuniformity is often observed at 7T. Reference scans from the body coil used for uniformity correction at lower field strengths are typically not available at 7T. PURPOSE: To evaluate the efficacy of a novel algorithm, Uniform Combined Reconstruction (UNICORN), to correct receive coil-induced nonuniformity in musculoskeletal 7T MRI without the use of a reference scan. STUDY TYPE: Retrospective image analysis study. SUBJECTS: MRI data of 20 subjects was retrospectively processed offline. Field Strength/Sequence: Knees of 20 subjects were imaged at 7T with a single-channel transmit, 28-channel phased-array receive knee coil. A turbo-spin-echo sequence was used to acquire 33 series of images. ASSESSMENT: Three fellowship-trained musculoskeletal radiologists with cumulative experience of 42 years reviewed the images. The uniformity, contrast, signal-to-noise ratio (SNR), and overall image quality were evaluated for images with no postprocessing, images processed with N4 bias field correction algorithm, and the UNICORN algorithm. STATISTICAL TESTS: Intraclass correlation coefficient (ICC) was used for measuring the interrater reliability. ICC and 95% confidence intervals (CIs) were calculated using the R statistical package employing a two-way mixed-effects model based on a mean rating (k = 3) for absolute agreement. The Wilcoxon signed-rank test with continuity correction was used for analyzing the overall image quality scores. RESULTS: UNICORN was preferred among the three methods evaluated for uniformity in 97.9% of the pooled ratings, with excellent interrater agreement (ICC of 0.98, CI 0.97-0.99). UNICORN was also rated better than N4 for contrast and equivalent to N4 in SNR with ICCs of 0.80 (CI 0.72-0.86) and 0.67 (CI 0.54-0.77), respectively. The overall image quality scores for UNICORN were significantly higher than N4 (P < 6 × 10-13 ), with good to excellent interrater agreement (ICC 0.90, CI 0.86-0.93). DATA CONCLUSION: Without the use of a reference scan, UNICORN provides better image uniformity, contrast, and overall image quality at 7T compared with the N4 bias field-correction algorithm. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1534-1544.