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

MR imaging by 3D T1-weighted black blood sequences may improve delineation of therapy-naive high-grade gliomas

Objectives!#!To investigate the value of contrast-enhanced (CE) turbo spin echo black blood (BB) sequences for imaging of therapy-naive high-grade gliomas (HGGs).!##!Methods!#!Consecutive patients with histopathologically confirmed World Health Organization (WHO) grade III or IV gliomas and no oncological treatment prior to index imaging (March 2019 to January 2020) were retrospectively included. Magnetic resonance imaging (MRI) at 3 Tesla comprised CE BB and CE turbo field echo (TFE) sequences. The lack/presence of tumor-related contrast enhancement and satellite lesions were evaluated by two readers. Sharper delineation of tumor boundaries (1, bad; 2, intermediate; 3, good delineation) and vaster expansion of HGGs into the adjacent brain parenchyma on CE BB imaging were the endpoints. Furthermore, contrast-to-noise ratios (CNRs) were calculated and compared between sequences.!##!Results!#!Fifty-four patients were included (mean age: 61.2 ± 15.9 years, 64% male). The vast majority of HGGs (51/54) showed contrast enhancement in both sequences, while two HGGs as well as one of six detected satellite lesions were depicted in CE BB imaging only. Tumor boundaries were significantly sharper (R1: 2.43 ± 0.71 vs. 2.73 ± 0.62, p < 0.001; R2: 2.44 ± 0.74 vs. 2.77 ± 0.60, p = 0.001), while the spread of HGGs into the adjacent parenchyma was larger when considering CE BB sequences according to both readers (larger spread in CE BB sequences: R1: 23 patients; R2: 20 patients). The CNR for CE BB sequences significantly exceeded that of CE TFE sequences (43.4 ± 27.1 vs. 32.5 ± 25.0, p = 0.0028).!##!Conclusions!#!Our findings suggest that BB imaging may considerably improve delineation of therapy-naive HGGs when compared with established TFE imaging. Thus, CE BB sequences might supplement MRI protocols for brain tumors.!##!Key points!#!• This study investigated contrast-enhanced (CE) T1-weighted black blood (BB) sequences for improved MRI in patients with therapy-naive high-grade gliomas (HGGs). • Compared with conventionally used turbo field echo (TFE) sequences, CE BB sequences depicted tumor boundaries and spread of HGGs into adjacent parenchyma considerably better, which also showed higher CNRs. • Two enhancing tumor masses and one satellite lesion were exclusively identified in CE BB sequences, but remained undetected in conventionally used CE TFE sequences

Bornavirus encephalitis shows a characteristic magnetic resonance phenotype in humans

Objective: The number of diagnosed fatal encephalitis cases in humans caused by the classical Borna disease virus (BoDV-1) has been increasing, ever since it was proved that BoDV-1 can cause human infections. However, awareness of this entity is low, and a specific imaging pattern has not yet been identified. We therefore provide the first comprehensive description of the morphology of human BoDV-1 encephalitis, with histopathological verification of imaging abnormalities. Methods: In an institutional review board-approved multicenter study, we carried out a retrospective analysis of 55 magnetic resonance imaging (MRI) examinations of 19 patients with confirmed BoDV-1 encephalitis. Fifty brain regions were analyzed systematically (T1w, T2w, T2*w, T1w + Gd, and DWI), in order to discern a specific pattern of inflammation. Histopathological analysis of 25 locations in one patient served as correlation for MRI abnormalities. Results: Baseline imaging, acquired at a mean of 11 ± 10 days after symptom onset, in addition to follow-up scans of 16 patients, revealed characteristic T2 hyperintensities with a predilection for the head of the caudate nucleus, insula, and cortical spread to the limbic system, whereas the occipital lobes and cerebellar hemispheres were unaffected. This gradient was confirmed by histology. Nine patients (47.4%) developed T1 hyperintensities of the basal ganglia, corresponding to accumulated lipid phagocytes on histology and typical for late-stage necrosis. Interpretation: BoDV-1 encephalitis shows a distinct pattern of inflammation in both the early and late stages of the disease. Its appearance can mimic sporadic Creutzfeldt–Jakob disease on MRI and should be considered a differential diagnosis in the case of atypical clinical presentation

Brain white-matter changes associated with symptomatic acute COVID-19 infection in the neonatal period

We report an important case of periventricular white matter damage in a 1-month-old infant, demonstrated on high quality structural (T2) and diffusion weighted magnetic resonance imaging. The infant was born at term following an uneventful pregnancy and discharged home shortly after, but was brought to the paediatric emergency department five days after birth with seizures and respiratory distress, testing positive for COVID-19 infection on PCR. These images highlight the need to consider brain MRI in all infants with symptomatic SARS-Cov-2 infection, and show how this infection can lead to extensive white matter damage in the context of multisystem inflammatio

Improving Automated Glioma Segmentation in Routine Clinical Use Through Artificial Intelligence-Based Replacement of Missing Sequences With Synthetic Magnetic Resonance Imaging Scans

Objectives: Although automated glioma segmentation holds promise for objective assessment of tumor biology and response, its routine clinical use is impaired by missing sequences, for example, due to motion artifacts. The aim of our study was to develop and validate a generative adversarial network for synthesizing missing sequences to allow for a robust automated segmentation. Materials and methods: Our model was trained on data from The Cancer Imaging Archive (n = 238 WHO II-IV gliomas) to synthesize either missing FLAIR, T2-weighted, T1-weighted (T1w), or contrast-enhanced T1w images from available sequences, using a novel tumor-targeting loss to improve synthesis of tumor areas. We validated performance in a test set from both the REMBRANDT repository and our local institution (n = 68 WHO II-IV gliomas), using qualitative image appearance metrics, but also segmentation performance with state-of-the-art segmentation models. Segmentation of synthetic images was compared with 2 commonly used strategies for handling missing input data, entering a blank mask or copying an existing sequence. Results: Across tumor areas and missing sequences, synthetic images generally outperformed both conventional approaches, in particular when FLAIR was missing. Here, for edema and whole tumor segmentation, we improved the Dice score, a common metric for evaluation of segmentation performance, by 12% and 11%, respectively, over the best conventional method. No method was able to reliably replace missing contrast-enhanced T1w images. Discussion: Replacing missing nonenhanced magnetic resonance sequences via synthetic images significantly improves segmentation quality over most conventional approaches. This model is freely available and facilitates more widespread use of automated segmentation in routine clinical use, where missing sequences are common

Automated Detection of Ischemic Stroke and Subsequent Patient Triage in Routinely Acquired Head CT

Purpose!#!Advanced machine-learning (ML) techniques can potentially detect the entire spectrum of pathology through deviations from a learned norm. We investigated the utility of a weakly supervised ML tool to detect characteristic findings related to ischemic stroke in head CT and provide subsequent patient triage.!##!Methods!#!Patients having undergone non-enhanced head CT at a tertiary care hospital in April 2020 with either no anomalies, subacute or chronic ischemia, lacunar infarcts of the deep white matter or hyperdense vessel signs were retrospectively analyzed. Anomaly detection was performed using a weakly supervised ML classifier. Findings were displayed on a voxel-level (heatmap) and pooled to an anomaly score. Thresholds for this score classified patients into i) normal, ii) inconclusive, iii) pathological. Expert-validated radiological reports were considered as ground truth. Test assessment was performed with ROC analysis; inconclusive results were pooled to pathological predictions for accuracy measurements.!##!Results!#!During the investigation period 208 patients were referred for head CT of which 111 could be included. Definite ratings into normal/pathological were feasible in 77 (69.4%) patients. Based on anomaly scores, the AUC to differentiate normal from pathological scans was 0.98 (95% CI 0.97-1.00). The sensitivity, specificity, positive and negative predictive values were 100%, 40.6%, 80.6% and 100%, respectively.!##!Conclusion!#!Our study demonstrates the potential of a weakly supervised anomaly-detection tool to detect stroke findings in head CT. Definite classification into normal/pathological was made with high accuracy in > 2/3 of patients. Anomaly heatmaps further provide guidance towards pathologies, also in cases with inconclusive ratings

Assessment of the Extent of Resection in Surgery of High-Grade Glioma—Evaluation of Black Blood Sequences for Intraoperative Magnetic Resonance Imaging at 3 Tesla

Achieving an optimal extent of resection (EOR) whilst keeping lasting neurological decline to a minimum is paramount for modern neurosurgery in patients with high-grade glioma (HGG). To improve EOR assessment, this study introduces Black Blood (BB) imaging, which uses a selective saturation pulse to suppress the blood signal, to 3-Tesla intraoperative magnetic resonance imaging (iMRI). Seventy-three patients (56.4 ± 13.9 years, 64.4% male) with contrast-enhancing HGGs underwent iMRI, including contrast-enhanced (CE) and non-CE 3D turbo field-echo imaging (TFE; acquisition time: 4:20 min per sequence) and CE and non-CE 3D BB imaging (acquisition time: 1:36 min per sequence). Two readers (R1 and R2) retrospectively evaluated the EOR and diagnostic confidence (1—very inconfident to 5—very confident) as well as the delineation of tumor boarders and spread of contrast-enhancing tumor components (in case of contrast-enhancing tumor residuals). Furthermore, the contrast-to-noise ratio (CNR) was measured for contrast-enhancing tumor residuals. Both BB and conventional TFE imaging allowed for the correct detection of all contrast-enhancing tumor residuals intraoperatively (considering postsurgical MRI and histopathological evaluation as the ground truth for determination of the lack/presence of contrast-enhancing tumor residuals), but BB imaging showed significantly higher diagnostic confidence (R1: 4.65 ± 0.53 vs. 3.88 ± 1.02, p < 0.0001; R2: 4.75 ± 0.50 vs. 4.25 ± 0.81, p < 0.0001). Delineation of contrast-enhancing tumor residuals and detection of their spread into adjacent brain parenchyma was better for BB imaging. Accordingly, significantly higher CNRs were noted for BB imaging (48.1 ± 32.1 vs. 24.4 ± 15.3, p < 0.0001). In conclusion, BB imaging is not inferior to conventional TFE imaging for EOR assessment, but may significantly reduce scanning time for iMRI whilst increasing diagnostic confidence. Furthermore, given the better depiction of contrast-enhancing tumor residual spread and borders, BB imaging could support achieving complete macroscopic resection in patients suffering from HGG, which is clinically relevant as an optimal EOR is correlated to prolonged survival

Faster and Better: How Anomaly Detection Can Accelerate and Improve Reporting of Head Computed Tomography

Background: Most artificial intelligence (AI) systems are restricted to solving a pre-defined task, thus limiting their generalizability to unselected datasets. Anomaly detection relieves this shortfall by flagging all pathologies as deviations from a learned norm. Here, we investigate whether diagnostic accuracy and reporting times can be improved by an anomaly detection tool for head computed tomography (CT), tailored to provide patient-level triage and voxel-based highlighting of pathologies. Methods: Four neuroradiologists with 1–10 years of experience each investigated a set of 80 routinely acquired head CTs containing 40 normal scans and 40 scans with common pathologies. In a random order, scans were investigated with and without AI-predictions. A 4-week wash-out period between runs was included to prevent a reminiscence effect. Performance metrics for identifying pathologies, reporting times, and subjectively assessed diagnostic confidence were determined for both runs. Results: AI-support significantly increased the share of correctly classified scans (normal/pathological) from 309/320 scans to 317/320 scans (p = 0.0045), with a corresponding sensitivity, specificity, negative- and positive- predictive value of 100%, 98.1%, 98.2% and 100%, respectively. Further, reporting was significantly accelerated with AI-support, as evidenced by the 15.7% reduction in reporting times (65.1 ± 8.9 s vs. 54.9 ± 7.1 s; p < 0.0001). Diagnostic confidence was similar in both runs. Conclusion: Our study shows that AI-based triage of CTs can improve the diagnostic accuracy and accelerate reporting for experienced and inexperienced radiologists alike. Through ad hoc identification of normal CTs, anomaly detection promises to guide clinicians towards scans requiring urgent attention