Deep-learning-reconstructed high-resolution 3D cervical spine MRI for foraminal stenosis evaluation

Accelerated Spine MRI with Deep Learning Based Image Reconstruction: A Prospective Comparison with Standard MRI

RATIONALE AND OBJECTIVES

To evaluate the performance of deep learning (DL) reconstructed MRI in terms of image acquisition time, overall image quality and diagnostic interchangeability compared to standard-of-care (SOC) MRI.

MATERIALS AND METHODS

This prospective study recruited participants between July 2023 and August 2023 who had spinal discomfort. All participants underwent two separate MRI examinations (Standard and accelerated scanning). Signal-to-noise ratios (SNR), contrast-to-noise ratios (CNR) and similarity metrics were calculated for quantitative evaluation. Four radiologists performed subjective quality and lesion characteristic assessment. Wilcoxon test was used to assess the differences of SNR, CNR and subjective image quality between DL and SOC. Various lesions of spine were also tested for interchangeability using individual equivalence index. Interreader and intrareader agreement and concordance (κ and Kendall τ and W statistics) were computed and McNemar tests were performed for comprehensive evaluation.

RESULTS

200 participants (107 male patients, mean age 46.56 ± 17.07 years) were included. Compared with SOC, DL enabled scan time reduced by approximately 40%. The SNR and CNR of DL were significantly higher than those of SOC (P < 0.001). DL showed varying degrees of improvement (0-0.35) in each of similarity metrics. All absolute individual equivalence indexes were less than 4%, indicating interchangeability between SOC and DL. Kappa and Kendall showed a good to near-perfect agreement in range of 0.72-0.98. There is no difference between SOC and DL regarding subjective scoring and frequency of lesion detection.

CONCLUSION

Compared to SOC, DL provided high-quality image for diagnosis and reduced examination time for patients. DL was found to be interchangeable with SOC in detecting various spinal abnormalities.

Development of an interactive ultra-high resolution magnetic resonance neurography atlas of the brachial plexus and upper extremity peripheral nerves

PURPOSE

To develop an educational, interactive, ultra-high resolution, in vivo magnetic resonance (MR) neurography atlas for direct visualization of the brachial plexus and upper extremity.

METHODS

A total of 16 adult volunteers without known peripheral neuropathy underwent magnetic resonance (MR) neurography of the brachial plexus and upper extremity. To improve vascular suppression, subjects received an intravenous infusion of ferumoxytol. To improve image quality, MR neurography datasets were reconstructed using a deep learning algorithm. The atlas was then developed using a web-based user-interface software, which allowed for labeling of peripheral nerves and muscles, and mapping of muscles to their respective innervation. The user interface was optimized to maximize interactivity and user-friendliness.

RESULTS

Fifteen subjects completed at least one scan with no reported adverse reactions from the ferumoxytol infusions. Adequate vascular suppression was observed in all MR neurography datasets. The images of the brachial plexus and upper extremity included in this atlas allowed for identification and labeling of 177 unique anatomical structures from the neck to the wrist. The atlas was made freely accessible on the internet.

CONCLUSION

A detailed and interactive MR neurography atlas of the brachial plexus and upper extremity was successfully developed to depict small nerves and fascicular detail with unprecedented spatial and contrast resolution. This freely available online resource (https://www.hss.edu/MRNatlas) can be used as an educational tool and clinical reference. The techniques utilized in this project serve as a framework for continued work in expanding the atlas to cover other peripheral nerve territories.

Evaluation of Image Quality and Scan Time Efficiency in Accelerated 3D T1-Weighted Pediatric Brain MRI Using Deep Learning-Based Reconstruction

OBJECTIVE

This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality.

MATERIALS AND METHODS

This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL). Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists' ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity.

RESULTS

The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols.

CONCLUSION

Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

MRI after Cervical Spine Decompression and Fusion Surgery: Technical Considerations, Expected Findings, and Complications

Cervical spine MRI is essential for evaluating potential complications and symptomatic degenerative changes following cervical decompression and fusion surgery. High-yield diagnostic interpretation considers the underlying surgical approach (anterior vs posterior), the time elapsed since surgery, and the clinical status of the patient to reliably differentiate expected postoperative changes from surgical complications. As cervical anatomy, such as the foramina and nerve roots, is smaller than that of the lumbar spine, MRI acquisition challenges include the demand for higher spatial resolution. Another unique challenge for cervical spine MRI is susceptibility to motion artifacts from swallowing, breathing, and cerebrospinal fluid pulsation. Modified MRI protocols, including the use of metal artifact suppression techniques, can help mitigate susceptibility artifacts from metallic implants. This focused review of postoperative cervical spine MRI discusses common cervical surgery decompression and fusion approaches and recommended MRI acquisition and interpretation algorithms, briefly considers radiofrequency coil selection, and illustrates complications in both early and delayed phases.

Diagnostic accuracy of MRI for detecting nerve injury in brachial plexus birth injury

OBJECTIVES

To determine the diagnostic accuracy of MRI for diagnosing nerve injury in brachial plexus birth injury (BPBI).

METHODS

Electronic databases were searched from inception to February 15, 2023 for studies reporting the accuracy of MRI (index test) compared to surgical exploration (reference standard) in detecting the target conditions of: root avulsion, any nerve abnormality, and pseudomeningocele (as a marker of root avulsion) in children with BPBI. Meta-analysis using a bivariate model was performed where data allowed.

RESULTS

Eight studies met the inclusion criteria. In total, 116 children with BPBI were included. All included studies were at risk of bias. The mean sensitivity and mean specificity of MRI for detecting root avulsion was 68% (95% CI: 55%, 79%) and 89% (95% CI: 78%, 95%), respectively. Pseudomeningocele was not a reliable marker of avulsion. Data were too sparse to determine the diagnostic accuracy of MRI for any nerve abnormality.

CONCLUSIONS

At present, surgical exploration should remain as the diagnostic modality of choice for BPBI due to the modest diagnostic accuracy of MRI in detecting root avulsion. The diagnostic accuracy of MRI needs to be close to 100% as the results may determine whether a child undergoes invasive surgery.

ADVANCES IN KNOWLEDGE

Previous research regarding MRI in detecting BPBI is highly variable and prior to our study the overall diagnostic accuracy was unclear. Through conducting a systematic review and meta-analysis, we were able to reliably determine the overall sensitivity and specificity of MRI for detecting root avulsion.

Diagnostic utility of 3D MRI sequences in the assessment of central, recess and foraminal stenoses of the spine: a systematic review

OBJECTIVE

To perform a systematic literature review on the diagnostic utility of 3D MRI sequences in the assessment of central canal, recess and foraminal stenosis in the spine.

METHODS

The databases PubMed, MEDLINE (via OVID) and The Cochrane Central Register of Controlled Trials, were searched for studies that investigated the diagnostic use of 3D MRI to evaluate stenoses in various parts of the spine in humans. Three reviewers examined the literature and conducted systematic review according to PRISMA 2020 guidelines.

RESULTS

Thirty studies were retrieved from 2 595 publications for this systematic review. The overall diagnostic performance of 3D MRI outperformed the conventional 2D MRI with reported sensitivities ranging from 79 to 100% and specificities ranging from 86 to 100% regarding the evaluation of central, recess and foraminal stenoses. In general, high level of agreement (both intra- and interrater) regarding visibility and pathology on 3D sequences was reported. Studies show that well-optimized 3D sequences allow the use of higher spatial resolution, similar scan time and increased SNR and CNR when compared to corresponding 2D sequences. However, the benefit of 3D sequences is in the additional information provided by them and in the possibility to save total protocol scan times.

CONCLUSION

The literature on the spine 3D MRI assessment of stenoses is heterogeneous with varying MRI protocols and diagnostic results. However, the 3D sequences offer similar or superior detection of stenoses with high reliability. Especially, the advantage of 3D MRI seems to be the better evaluation of recess stenoses.

Deep-learning reconstructed lumbar spine 3D MRI for surgical planning: pedicle screw placement and geometric measurements compared to CT

PURPOSE

To test equivalency of deep-learning 3D lumbar spine MRI with "CT-like" contrast to CT for virtual pedicle screw planning and geometric measurements in robotic-navigated spinal surgery.

METHODS

Between December 2021 and June 2022, 16 patients referred for spinal fusion and decompression surgery with pre-operative CT and 3D MRI were retrospectively assessed. Pedicle screws were virtually placed on lumbar (L1-L5) and sacral (S1) vertebrae by three spine surgeons, and metrics (lateral deviation, axial/sagittal angles) were collected. Vertebral body length/width (VL/VW) and pedicle height/width (PH/PW) were measured at L1-L5 by three radiologists. Analysis included equivalency testing using the 95% confidence interval (CI), a margin of ± 1 mm (± 2.08° for angles), and intra-class correlation coefficients (ICCs).

RESULTS

Across all vertebral levels, both combined and separately, equivalency between CT and MRI was proven for all pedicle screw metrics and geometric measurements, except for VL at L1 (mean difference: - 0.64 mm; [95%CI - 1.05, - 0.24]), L2 (- 0.65 mm; [95%CI - 1.11, - 0.20]), and L4 (- 0.78 mm; [95%CI - 1.11, - 0.46]). Inter- and intra-rater ICC for screw metrics across all vertebral levels combined ranged from 0.68 to 0.91 and 0.89-0.98 for CT, and from 0.62 to 0.92 and 0.81-0.97 for MRI, respectively. Inter- and intra-rater ICC for geometric measurements ranged from 0.60 to 0.95 and 0.84-0.97 for CT, and 0.61-0.95 and 0.93-0.98 for MRI, respectively.

CONCLUSION

Deep-learning 3D MRI facilitates equivalent virtual pedicle screw placements and geometric assessments for most lumbar vertebrae, with the exception of vertebral body length at L1, L2, and L4, compared to CT for pre-operative planning in patients considered for robotic-navigated spine surgery.

Deep learning denoising reconstruction enables faster T2-weighted FLAIR sequence acquisition with satisfactory image quality

INTRODUCTION

Deep learning reconstruction (DLR) technologies are the latest methods attempting to solve the enduring problem of reducing MRI acquisition times without compromising image quality. The clinical utility of this reconstruction technique is yet to be fully established. This study aims to assess whether a commercially available DLR technique applied to 2D T2-weighted FLAIR brain images allows a reduction in scan time, without compromising image quality and thus diagnostic accuracy.

METHODS

47 participants (24 male, mean age 55.9 ± 18.7 SD years, range 20-89 years) underwent routine, clinically indicated brain MRI studies in March 2022, that included a standard-of-care (SOC) T2-weighted FLAIR sequence, and an accelerated acquisition that was reconstructed using the DLR denoising product. Overall image quality, lesion conspicuity, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artefacts for each sequence, and preferred sequence on direct comparison, were subjectively assessed by two readers.

RESULTS

There was a strong preference for SOC FLAIR sequence for overall image quality (P = 0.01) and head-to-head comparison (P < 0.001). No difference was observed for lesion conspicuity (P = 0.49), perceived SNR (P = 1.0), and perceived CNR (P = 0.84). There was no difference in motion (P = 0.57) nor Gibbs ringing (P = 0.86) artefacts. Phase ghosting (P = 0.038) and pseudolesions were significantly more frequent (P < 0.001) on DLR images.

CONCLUSION

DLR algorithm allowed faster FLAIR acquisition times with comparable image quality and lesion conspicuity. However, an increased incidence and severity of phase ghosting artefact and presence of pseudolesions using this technique may result in a reduction in reading speed, efficiency, and diagnostic confidence.

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.

Optimized 3D brachial plexus MR neurography using deep learning reconstruction

OBJECTIVE

To evaluate whether 'fast,' unilateral, brachial plexus, 3D magnetic resonance neurography (MRN) acquisitions with deep learning reconstruction (DLR) provide similar image quality to longer, 'standard' scans without DLR.

MATERIALS AND METHODS

An IRB-approved prospective cohort of 30 subjects (13F; mean age = 50.3 ± 17.8y) underwent clinical brachial plexus 3.0 T MRN with 3D oblique-coronal STIR-T2-weighted-FSE. 'Standard' and 'fast' scans (time reduction = 23-48%, mean = 33%) were reconstructed without and with DLR. Evaluation of signal-to-noise ratio (SNR) and edge sharpness was performed for 4 image stacks: 'standard non-DLR,' 'standard DLR,' 'fast non-DLR,' and 'fast DLR.' Three raters qualitatively evaluated 'standard non-DLR' and 'fast DLR' for i) bulk motion (4-point scale), ii) nerve conspicuity of proximal and distal suprascapular and axillary nerves (5-point scale), and iii) nerve signal intensity, size, architecture, and presence of a mass (binary). ANOVA or Wilcoxon signed rank test compared differences. Gwet's agreement coefficient (AC2) assessed inter-rater agreement.

RESULTS

Quantitative SNR and edge sharpness were superior for DLR versus non-DLR (SNR by + 4.57 to + 6.56 [p < 0.001] for 'standard' and + 4.26 to + 4.37 [p < 0.001] for 'fast;' sharpness by + 0.23 to + 0.52/pixel for 'standard' [p < 0.018] and + 0.21 to + 0.25/pixel for 'fast' [p < 0.003]) and similar between 'standard non-DLR' and 'fast DLR' (SNR: p = 0.436-1, sharpness: p = 0.067-1). Qualitatively, 'standard non-DLR' and 'fast DLR' had similar motion artifact, as well as nerve conspicuity, signal intensity, size and morphology, with high inter-rater agreement (AC2: 'standard' = 0.70-0.98, 'fast DLR' = 0.69-0.97).

CONCLUSION

DLR applied to faster, 3D MRN acquisitions provides similar image quality to standard scans. A faster, DL-enabled protocol may replace currently optimized non-DL protocols.

Quantitative double echo steady state T2 mapping of upper extremity peripheral nerves and muscles

Introduction

T2 mapping can characterize peripheral neuropathy and muscle denervation due to axonal damage. Three-dimensional double echo steady-state (DESS) can simultaneously provide 3D qualitative information and T2 maps with equivalent spatial resolution. However, insufficient signal-to-noise ratio may bias DESS-T2 values. Deep learning reconstruction (DLR) techniques can reduce noise, and hence may improve quantitation of high-resolution DESS-T2. This study aims to (i) evaluate the effect of DLR methods on DESS-T2 values, and (ii) to evaluate the feasibility of using DESS-T2 maps to differentiate abnormal from normal nerves and muscles in the upper extremities, with abnormality as determined by electromyography.

Methods and results

Analysis of images from 25 subjects found that DLR decreased DESS-T2 values in abnormal muscles (DLR = 37.71 ± 9.11 msec, standard reconstruction = 38.56 ± 9.44 msec, p = 0.005) and normal muscles (DLR: 27.18 ± 6.34 msec, standard reconstruction: 27.58 ± 6.34 msec, p < 0.001) consistent with a noise reduction bias. Mean DESS-T2, both with and without DLR, was higher in abnormal nerves (abnormal = 75.99 ± 38.21 msec, normal = 35.10 ± 9.78 msec, p < 0.001) and muscles (abnormal = 37.71 ± 9.11 msec, normal = 27.18 ± 6.34 msec, p < 0.001). A higher DESS-T2 in muscle was associated with electromyography motor unit recruitment (p < 0.001).

Discussion

These results suggest that quantitative DESS-T2 is improved by DLR and can differentiate the nerves and muscles involved in peripheral neuropathies from those uninvolved.

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.

Flexible array coil for cervical and extraspinal (FACE) MRI at 3.0 Tesla

Objective.High-resolution MRI of the cervical spine (c-spine) and extraspinal neck region requires close-fitting receiver coils to maximize the signal-to-noise ratio (SNR). Conventional, rigid C-spine receiver coils do not adequately contour to the neck to accommodate varying body shapes, resulting in suboptimal SNR. Recent innovations in flexible surface coil array designs may provide three-dimensional (3D) bendability and conformability to optimize SNR, while improving capabilities for higher acceleration factors.Approach.This work describes the design, implementation, and preliminaryin vivotesting of a novel, conformal 23-channel receive-only flexible array for cervical and extraspinal (FACE) MRI at 3-Tesla (T), with use of high-impedance elements to enhance the coil's flexibility. Coil performance was tested by assessing SNR and geometry factors (g-factors) in a phantom compared to a conventional 21-channel head-neck-unit (HNU).In vivoimaging was performed in healthy human volunteers and patients using high-resolution c-spine and neck MRI protocols at 3T, including MR neurography (MRN).Main results.Mean SNR with the FACE was 141%-161% higher at left, right, and posterior off-isocenter positions and 4% higher at the isocenter of the phantom compared to the HNU. Parallel imaging performance was comparable for an acceleration factor (R) = 2 × 2 between the two coils, but improved forR= 3 × 3 with meang-factors ranging from 1.46-2.15 with the FACE compared to 2.36-3.62 obtained with the HNU. Preliminary human volunteer and patient testing confirmed that equivalent or superior image quality could be obtained for evaluation of osseous and soft tissue structures of the cervical region with the FACE.Significance.A conformal and highly flexible cervical array with high-impedance coil elements can potentially enable higher-resolution imaging for cervical imaging.

MRI Advancements in Musculoskeletal Clinical and Research Practice

Over the past decades, MRI has become increasingly important for diagnosing and longitudinally monitoring musculoskeletal disorders, with ongoing hardware and software improvements aiming to optimize image quality and speed. However, surging demand for musculoskeletal MRI and increased interest to provide more personalized care will necessitate a stronger emphasis on efficiency and specificity. Ongoing hardware developments include more powerful gradients, improvements in wide-bore magnet designs to maintain field homogeneity, and high-channel phased-array coils. There is also interest in low-field-strength magnets with inherently lower magnetic footprints and operational costs to accommodate global demand in middle- and low-income countries. Previous approaches to decrease acquisition times by means of conventional acceleration techniques (eg, parallel imaging or compressed sensing) are now largely overshadowed by deep learning reconstruction algorithms. It is expected that greater emphasis will be placed on improving synthetic MRI and MR fingerprinting approaches to shorten overall acquisition times while also addressing the demand of personalized care by simultaneously capturing microstructural information to provide greater detail of disease severity. Authors also anticipate increased research emphasis on metal artifact reduction techniques, bone imaging, and MR neurography to meet clinical needs.

Rapid lumbar MRI protocol using 3D imaging and deep learning reconstruction

BACKGROUND AND PURPOSE

Three-dimensional (3D) imaging of the spine, augmented with AI-enabled image enhancement and denoising, has the potential to reduce imaging times without compromising image quality or diagnostic performance. This work evaluates the time savings afforded by a novel, rapid lumbar spine MRI protocol as well as image quality and diagnostic differences stemming from the use of an AI-enhanced 3D T2 sequence combined with a single Dixon acquisition.

MATERIALS AND METHODS

Thirty-five subjects underwent MRI using standard 2D lumbar imaging in addition to a "rapid protocol" consisting of 3D imaging, enhanced and denoised using a prototype DL reconstruction algorithm as well as a two-point Dixon sequence. Images were graded by subspecialized radiologists and imaging times were collected. Comparison was made between 2D sagittal T1 and Dixon fat images for neural foraminal stenosis, intraosseous lesions, and fracture detection.

RESULTS

This study demonstrated a 54% reduction in total acquisition time of a 3D AI-enhanced imaging lumbar spine MRI rapid protocol combined with a sagittal 2D Dixon sequence, compared to a 2D standard-of-care protocol. The rapid protocol also demonstrated strong agreement with the standard-of-care protocol with respect to osseous lesions (κ = 0.88), fracture detection (κ = 0.96), and neural foraminal stenosis (ICC > 0.9 at all levels).

CONCLUSION

3D imaging of the lumbar spine with AI-enhanced DL reconstruction and Dixon imaging demonstrated a significant reduction in imaging time with similar performance for common diagnostic metrics. Although previously limited by long postprocessing times, this technique has the potential to enhance patient throughput in busy radiology practices while providing similar or improved image quality.