No abstract

Magnetic resonance imaging (MRI) can provide a number of measurements relevant to sport-related concussion (SRC) symptoms; however, most studies to date have used a single MRI modality and whole-brain exploratory analyses in attempts to localize concussion injury. This has resulted in highly variable findings across studies due to wide ranging symptomology, severity and nature of injury within studies. A multimodal MRI, symptom-guided region-of-interest (ROI) approach is likely to yield more consistent results. The functions of the cerebellum and basal ganglia transcend many common concussion symptoms, and thus these regions, plus the white matter tracts that connect or project from them, constitute plausible ROIs for MRI analysis. We performed diffusion tensor imaging (DTI), resting-state functional MRI, quantitative susceptibility mapping (QSM), and cerebral blood flow (CBF) imaging using arterial spin labeling (ASL), in youth aged 12-18 years following SRC, with a focus on the cerebellum, basal ganglia and white matter tracts. Compared to controls similar in age, sex and sport (N = 20), recent SRC youth (N = 29; MRI at 8 ± 3 days post injury) exhibited increased susceptibility in the cerebellum (p = 0.032), decreased functional connectivity between the caudate and each of the pallidum (p = 0.035) and thalamus (p = 0.021), and decreased diffusivity in the mid-posterior corpus callosum (p < 0.038); no changes were observed in recovered asymptomatic youth (N = 16; 41 ± 16 days post injury). For recent symptomatic-only SRC youth (N = 24), symptom severity was associated with increased susceptibility in the superior cerebellar peduncles (p = 0.011) and reduced activity in the cerebellum (p = 0.013). Fewer days between injury and MRI were associated with reduced cerebellar-parietal functional connectivity (p < 0.014), reduced activity of the pallidum (p = 0.002), increased CBF in the caudate (p = 0.005), and reduced diffusivity in the central corpus callosum (p < 0.05). Youth SRC is associated with acute cerebellar inflammation accompanied by reduced cerebellar activity and cerebellar-parietal connectivity, as well as structural changes of the middle regions of the corpus callosum accompanied by functional changes of the caudate, all of which resolve with recovery. Early MRI post-injury is important to establish objective MRI-based indicators for concussion diagnosis, recovery assessment and prediction of outcome.

BACKGROUND We recently developed a novel machine learning‐based algorithm using multiphase computed tomography angiography (mCTA) to generate perfusion maps of the brain, similar to computed tomography perfusion (CTP) (ie, multiphase CTA perfusion [mCTAp]). Here, we aim to validate the clinical utility of mCTAp in detection of brain ischemia and its side, extent, and location. METHODS In this prospective multi‐reader‐multi‐case analysis, we included baseline images: mCTAp ( StrokeSENS ‐algorithm) and CTP (4D; GE Healthcare) from 121 randomly selected patients whose scans were not part of algorithm‐development. After excluding 2/121 scans because of poor image‐quality, 3 experienced radiologists read time to maximum, and relative cerebral blood flow‐maps generated by the test (mCTAp) and reference (CTP) modality. The 2 reading sessions were separated by 5 days although the reading order was randomized. Core laboratory imaging assessments – that used non contrast computed tomography, mCTA, and CTP – were considered as ground‐truth. A mixed‐effects statistical model with “reader” as random effects variable was used to calculate the area under the curve (with 95% CI), sensitivity, and specificity for both modalities (mCTAp/CTP) for ischemia detection, affected side, and occlusion location. The time required for interpretation and inter‐rater variability in assessments were compared across the 2 modalities. RESULTS Area under the curves (95% CI) for detecting ischemia using mCTAp and CTP were 0.85 (95% CI, 0.8–0.9) and 0.84 (0.8–0.9) respectively ( P =0.43). Area under the curves for the affected side were 0.94 (0.92–0.97) and 0.96 (0.94–0.98) ( P =0.69), respectively; for detecting large vessel occlusion were 0.84 (0.8–0.9) and 0.86 (0.8–0.9), ( P =0.31), respectively; M2‐or‐distal occlusion were 0.79 (0.73–0.84) and 0.88 (0.83–0.92) ( P =0.22), respectively, for anterior cerebral artery‐occlusion 0.82 (0.66–0.98) and 0.93 (0.82–1.00) ( P =0.15), respectively, and for posterior cerebral artery‐occlusions 0.9 (0.8–1) and 0.99 (0.98–0.99) ( P =0.01), respectively. The median (interquartile range [IQR]) time for image interpretation was 62 seconds (IQR, 46–78) and 59 seconds (IQR, 42–69) for mCTAp and CTP, respectively, ( P =0.15). Fleiss` Kappa‐values for inter‐rater reliability in detecting ischemia were 0.5 and 0.8 for mCTAp and CTP, respectively. CONCLUSION mCTAp shows similar performance and interpretation times compared to CTP in assisting readers to detect brain ischemia, affected side, and occlusion location, but mainly as it relates to proximal vessel occlusions. The proposed tool still needs further refinement for distal vessel occlusions. Nonetheless, mCTAp is a promising tool as it allows for acquisition of brain perfusion maps with lower radiation exposure, acquisition time, and contrast dose compared with additional CTP.

Introduction: We recently published a Machine Learning-based algorithm using mCTA (mCTAp) that generates perfusion maps of the brain, similar to CTP. Here, we aim to validate the clinical utility of mCTAp in detection of ischemia, it’s side, extent and location. Methods: One hundred and twenty-one subjects were included in the test dataset. These subjects had completed mCTAp (StrokeSENS) and CTP (4D; GE Healthcare) at baseline. After excluding 2/121 subjects due to poor image quality, a multi-reader-multi-case (n=119) study design was conducted with 3 experienced radiologists, reading post-processed maps (Tmax and rCBF) generated by both mCTAp and CTP. Both reading sessions were separated by a few days. All readers were blinded to modality and clinical information and the reading order was randomized between sessions. Core lab imaging assessments that used NCCT, mCTA and CTP were considered as ground truth. A mixed effect regression model with the reader as random effect variable was used to calculate the AUC, sensitivity, and specificity for both imaging arms regarding ischemia detection, ischemia side, and occlusion site selection (i.e., ICA/M1[considered LVO], M2 or distal, ACA or PCA). The time required for image interpretation was compared between the two modalities. Results: The AUCs for detecting ischemia were comparable between mCTAp and CTP (0.85 [95%CI:0.8-0.9] and 0.84 [95%CI:0.8-0.9] respectively; p=0.43), the affected ischemic side (0.94 [0.92-0.97] and 0.96 [0.94-0.98] respectively; p=0.69), detecting a LVO (0.84 [0.8-0.9] and 0.86 [0.8-0.9]; p=0.31) and M2 or distal occlusions (0.8 [0.7-0.8] and 0.9 [0.84-0.92]; p=0.22). The median (IQR) time for interpretation was 62s (46-78) and 59s (42-69) for mCTAp and CTP respectively (p=0.15). Conclusion: mCTAp shows similar performances when compared to CTP in assisting readers to detect ischemia, location, and extent by using Tmax and rCBF perfusion maps.