Deep learning-accelerated image reconstruction in back pain-MRI imaging: reduction of acquisition time and improvement of image quality.

INTRODUCTION: Low back pain is a global health issue causing disability and missed work days. Commonly used MRI scans including T1-weighted and T2-weighted images provide detailed information of the spine and surrounding tissues. Artificial intelligence showed promise in improving image quality and simultaneously reducing scan time. This study evaluates the performance of deep learning (DL)-based T2 turbo spin-echo (TSE, T2DLR) and T1 TSE (T1DLR) in lumbar spine imaging regarding acquisition time, image quality, artifact resistance, and diagnostic confidence. MATERIAL AND METHODS: This retrospective monocentric study included 60 patients with lower back pain who underwent lumbar spinal MRI between February and April 2023. MRI parameters and DL reconstruction (DLR) techniques were utilized to acquire images. Two neuroradiologists independently evaluated image datasets based on various parameters using a 4-point Likert scale. RESULTS: Accelerated imaging showed significantly less image noise and artifacts, as well as better image sharpness, compared to standard imaging. Overall image quality and diagnostic confidence were higher in accelerated imaging. Relevant disk herniations and spinal fractures were detected in both DLR and conventional images. Both readers favored accelerated imaging in the majority of examinations. The lumbar spine examination time was cut by 61% in accelerated imaging compared to standard imaging. CONCLUSION: In conclusion, the utilization of deep learning-based image reconstruction techniques in lumbar spinal imaging resulted in significant time savings of up to 61% compared to standard imaging, while also improving image quality and diagnostic confidence. These findings highlight the potential of these techniques to enhance efficiency and accuracy in clinical practice for patients with lower back pain.

Patterns of subregional cerebellar atrophy across epilepsy syndromes: An ENIGMA-Epilepsy study.

OBJECTIVE: The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current corticocentric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural magnetic resonance imaging in 1602 adults with epilepsy and 1022 healthy controls across 22 sites from the global ENIGMA-Epilepsy working group. METHODS: A state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions. Linear mixed models compared total and regional cerebellar volume in (1) all epilepsies, (2) temporal lobe epilepsy with hippocampal sclerosis (TLE-HS), (3) nonlesional temporal lobe epilepsy, (4) genetic generalized epilepsy, and (5) extratemporal focal epilepsy (ETLE). Relationships were examined for cerebellar volume versus age at seizure onset, duration of epilepsy, phenytoin treatment, and cerebral cortical thickness. RESULTS: Across all epilepsies, reduced total cerebellar volume was observed (d = .42). Maximum volume loss was observed in the corpus medullare (dmax = .49) and posterior lobe gray matter regions, including bilateral lobules VIIB (dmax = .47), crus I/II (dmax = .39), VIIIA (dmax = .45), and VIIIB (dmax = .40). Earlier age at seizure onset ( η ρ max 2 = .05) and longer epilepsy duration ( η ρ max 2 = .06) correlated with reduced volume in these regions. Findings were most pronounced in TLE-HS and ETLE, with distinct neuroanatomical profiles observed in the posterior lobe. Phenytoin treatment was associated with reduced posterior lobe volume. Cerebellum volume correlated with cerebral cortical thinning more strongly in the epilepsy cohort than in controls. SIGNIFICANCE: We provide robust evidence of deep cerebellar and posterior lobe subregional gray matter volume loss in patients with chronic epilepsy. Volume loss was maximal for posterior subregions implicated in nonmotor functions, relative to motor regions of both the anterior and posterior lobe. Associations between cerebral and cerebellar changes, and variability of neuroanatomical profiles across epilepsy syndromes argue for more precise incorporation of cerebellar subregional damage into neurobiological models of epilepsy.

Deep learning-accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality.

BACKGROUND AND PURPOSE: This study explores the use of deep learning (DL) techniques in MRI of the orbit to enhance imaging. Standard protocols, although detailed, have lengthy acquisition times. We investigate DL-based methods for T2-weighted and T1-weighted, fat-saturated, contrast-enhanced turbo spin echo (TSE) sequences, aiming to improve image quality, reduce acquisition time, minimize artifacts, and enhance diagnostic confidence in orbital imaging. METHODS: In a 3-Tesla MRI study of 50 patients evaluating orbital diseases from March to July 2023, conventional (TSES ) and DL TSE sequences (TSEDL ) were used. Two neuroradiologists independently assessed the image datasets for image quality, diagnostic confidence, noise levels, artifacts, and image sharpness using a randomized and blinded 4-point Likert scale. RESULTS: TSEDL significantly reduced image noise and artifacts, enhanced image sharpness, and decreased scan time, outperforming TSES (p < .05). TSEDL showed superior overall image quality and diagnostic confidence, with relevant findings effectively detected in both DL-based and conventional images. In 94% of cases, readers preferred accelerated imaging. CONCLUSION: The study proved that using DL for MRI image reconstruction in orbital scans significantly cut acquisition time by 69%. This approach also enhanced image quality, reduced image noise, sharpened images, and boosted diagnostic confidence.

Patterns of subregional cerebellar atrophy across epilepsy syndromes: An ENIGMA-Epilepsy study.

Objective: The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current cortico-centric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural MRI in 1,602 adults with epilepsy and 1,022 healthy controls across twenty-two sites from the global ENIGMA-Epilepsy working group. Methods: A state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions. Linear mixed models compared total and regional cerebellar volume in i) all epilepsies; ii) temporal lobe epilepsy with hippocampal sclerosis (TLE-HS); iii) non-lesional temporal lobe epilepsy (TLE-NL); iv) genetic generalised epilepsy; and (v) extra-temporal focal epilepsy (ETLE). Relationships were examined for cerebellar volume versus age at seizure onset, duration of epilepsy, phenytoin treatment, and cerebral cortical thickness. Results: Across all epilepsies, reduced total cerebellar volume was observed (d=0.42). Maximum volume loss was observed in the corpus medullare (dmax=0.49) and posterior lobe grey matter regions, including bilateral lobules VIIB (dmax= 0.47), Crus I/II (dmax= 0.39), VIIIA (dmax=0.45) and VIIIB (dmax=0.40). Earlier age at seizure onset (ηρ2max=0.05) and longer epilepsy duration (ηρ2max=0.06) correlated with reduced volume in these regions. Findings were most pronounced in TLE-HS and ETLE with distinct neuroanatomical profiles observed in the posterior lobe. Phenytoin treatment was associated with reduced posterior lobe volume. Cerebellum volume correlated with cerebral cortical thinning more strongly in the epilepsy cohort than in controls. Significance: We provide robust evidence of deep cerebellar and posterior lobe subregional grey matter volume loss in patients with chronic epilepsy. Volume loss was maximal for posterior subregions implicated in non-motor functions, relative to motor regions of both the anterior and posterior lobe. Associations between cerebral and cerebellar changes, and variability of neuroanatomical profiles across epilepsy syndromes argue for more precise incorporation of cerebellum subregions into neurobiological models of epilepsy.

[Choosing the right imaging for the diagnostics and assessment of the course of peripheral nerve injuries]. / Die Wahl der richtigen Bildgebung zur Diagnostik und Verlaufsbeurteilung peripherer Nervenverletzungen.

BACKGROUND: Nerve injuries are a frequent problem in routine clinical practice and require intensive interdisciplinary care. OBJECTIVE: The current status of imaging to confirm the diagnosis of nerve injuries is described. The role of high-resolution ultrasound and magnetic resonance imaging (MRI) in the diagnostics and follow-up of peripheral nerve injuries is elaborated. MATERIAL AND METHODS: Review of the current state of imaging to confirm the diagnosis of nerve injuries. RESULTS: Depending on the suspected site of damage, the primary domain of magnetic resonance (MR) imaging (MR neurography) is injuries in the region of the spine, nerve roots, brachial plexus and lumbar plexus, pelvis and proximal thigh. In contrast, in other peripheral nerve lesions of the extremities the advantages of high-resolution nerve ultrasound in a dynamic setting predominate. The MR neurography is indicated here, especially in the frequent bottleneck syndromes and only in very isolated and selected cases. CONCLUSION: In addition to a correct anatomical assignment, the timely decision for a possible intervention and the appropriate concomitant treatment are an important basis for a favorable prognosis of nerve injuries. Imaging techniques should therefore be used early in the diagnostics and follow-up controls of peripheral nerve injuries.

Proof of principle for the clinical use of a CE-certified automatic imaging analysis tool in rare diseases studying hereditary spastic paraplegia type 4 (SPG4).

Usage of MR imaging biomarkers is limited to experts. Automatic quantitative reports provide access for clinicians to data analysis. Automated data analysis was tested for usability in a small cohort of patients with hereditary spastic paraplegia type 4 (SPG4). We analyzed 3T MRI 3D-T1 datasets of n = 25 SPG4 patients and matched healthy controls using a commercial segmentation tool (AIRAscore structure 2.0.1) and standard VBM. In SPG4 total brain volume was reduced by 27.6 percentiles (p = 0.001) caused mainly by white matter loss (- 30.8th, p < 0.001) and stable total gray matter compared to controls. Brain volume loss occurred in: midbrain (- 41.5th, p = 0.001), pons (- 36.5th, p = 0.02), hippocampus (- 20.9th, p = 0.002), and gray matter of the cingulate gyrus (- 17.0th, p = 0.02). Ventricular volumes increased as indirect measures of atrophy. Group comparisons using percentiles aligned with results from VBM analyses. Quantitative imaging reports proved to work as an easily accessible, fully automatic screening tool for clinicians, even in a small cohort of a rare genetic disorder. We could delineate the involvement of white matter and specify involved brain regions. Group comparisons using percentiles provide comparable results to VBM analysis and are, therefore, a suitable and simple screening tool for all clinicians with and without in-depth knowledge of image processing.

Uncertainty-Aware and Lesion-Specific Image Synthesis in Multiple Sclerosis Magnetic Resonance Imaging: A Multicentric Validation Study.

Generative adversarial networks (GANs) can synthesize high-contrast MRI from lower-contrast input. Targeted translation of parenchymal lesions in multiple sclerosis (MS), as well as visualization of model confidence further augment their utility, provided that the GAN generalizes reliably across different scanners. We here investigate the generalizability of a refined GAN for synthesizing high-contrast double inversion recovery (DIR) images and propose the use of uncertainty maps to further enhance its clinical utility and trustworthiness. A GAN was trained to synthesize DIR from input fluid-attenuated inversion recovery (FLAIR) and T1w of 50 MS patients (training data). In another 50 patients (test data), two blinded readers (R1 and R2) independently quantified lesions in synthetic DIR (synthDIR), acquired DIR (trueDIR) and FLAIR. Of the 50 test patients, 20 were acquired on the same scanner as training data (internal data), while 30 were scanned at different scanners with heterogeneous field strengths and protocols (external data). Lesion-to-Background ratios (LBR) for MS-lesions vs. normal appearing white matter, as well as image quality parameters were calculated. Uncertainty maps were generated to visualize model confidence. Significantly more MS-specific lesions were found in synthDIR compared to FLAIR (R1: 26.7 ± 2.6 vs. 22.5 ± 2.2 p < 0.0001; R2: 22.8 ± 2.2 vs. 19.9 ± 2.0, p = 0.0005). While trueDIR remained superior to synthDIR in R1 [28.6 ± 2.9 vs. 26.7 ± 2.6 (p = 0.0021)], both sequences showed comparable lesion conspicuity in R2 [23.3 ± 2.4 vs. 22.8 ± 2.2 (p = 0.98)]. Importantly, improvements in lesion counts were similar in internal and external data. Measurements of LBR confirmed that lesion-focused GAN training significantly improved lesion conspicuity. The use of uncertainty maps furthermore helped discriminate between MS lesions and artifacts. In conclusion, this multicentric study confirms the external validity of a lesion-focused Deep-Learning tool aimed at MS imaging. When implemented, uncertainty maps are promising to increase the trustworthiness of synthetic MRI.

Estimation of Severity of Moyamoya Disease with [15O]Water-Positron Emission Tomography Compared with Magnetic Resonance Imaging and Angiography.

BACKGROUND: Moyamoya disease is a steno-occlusive disease of the circle of Willis with growth of pathologic collaterals. We compared functional perfusion imaging ([15O]water-positron emission tomography [PET] with acetazolamide challenge) with conventional magnetic resonance imaging (MRI) and angiography for determining indication for cerebral revascularization in patients with moyamoya. METHODS: We performed a retrospective blinded analysis of individual imaging modalities (MRI, angiography, PET) and scored each modality for severity of disease in 21 untreated patients with moyamoya with 78 affected vascular territories. RESULTS: Positive predictive value to identify insufficient perfusion on angiography and MRI together was 98.3% as proven on combined PET/computed tomography. Negative predictive value to identify sufficient perfusion on angiography and/or MRI only was 60%. Negative predictive value to predict good perfusion on PET based on MRI (no infarctions in the respective territory) was only 17%. An assumed good perfusion based on the suggestion of good collaterals on angiography was correct in only 13.4% of cases. Positive predictive value (angiography of main vessel and weak or no collateralization) to predict insufficient perfusion on PET/computed tomography was 76.9%; negative predictive value (angiography of main vessel and strong collateralization) to identify good perfusion was 13.4%. CONCLUSIONS: Reliable evaluation of cerebral blood flow might not be possible with angiography and basic MRI alone. We strongly recommend additional functional imaging (e.g., [15O]water-PET with acetazolamide challenge) to precisely evaluate the indication for cerebral revascularization.