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Cerebral oxygen metabolism is altered in relapsing-remitting multiple sclerosis (RRMS), possibly a result of disease related cerebral atrophy with subsequent decreased oxygen demand. However, MS inflammation can also inhibit brain metabolism. Therefore, we measured cerebral blood flow (CBF) and cerebral metabolic rate of oxygen (CMRO2) using MRI phase contrast mapping and susceptibility-based oximetry in 44 patients with early RRMS and 36 healthy controls. Cerebral atrophy and white matter lesion load were assessed from high-resolution structural MRI. Expanded Disability Status Scale (EDSS) scores were collected from medical records. The CMRO2 was significantly lower in patients (−15%, p = 0.002) and decreased significantly with age in patients relative to the controls (−1.35 µmol/100 g/min/year, p = 0.036). The lower CMRO2 in RRMS was primarily driven by a higher venous oxygen saturation in the sagittal sinus (p = 0.007) and not a reduction in CBF (p = 0.69). There was no difference in cerebral atrophy between the groups, and no correlation between CMRO2 and MS lesion volume or EDSS score. Therefore, the progressive CMRO2 decline observed before the occurrence of significant cerebral atrophy and despite adequate CBF supports emerging evidence of dysfunctional cellular respiration as a potential pathogenic mechanism and therapeutic target in RRMS. [ABSTRACT FROM AUTHOR]

Introduction: Patients with MS are MRI scanned continuously throughout their disease course resulting in a large manual workload for radiologists which includes lesion detection and size estimation. Though many models for automatic lesion segmentation have been published, few are used broadly in clinic today, as there is a lack of testing on clinical datasets. By collecting a large, heterogeneous training dataset directly from our MS clinic we aim to present a model which is robust to different scanner protocols and artefacts and which only uses MRI modalities present in routine clinical examinations. Methods: We retrospectively included 746 patients from routine examinations at our MS clinic. The inclusion criteria included acquisition at one of seven different scanners and an MRI protocol including 2D or 3D T2-w FLAIR, T2-w and T1-w images. Reference lesion masks on the training (n = 571) and validation (n = 70) datasets were generated using a preliminary segmentation model and subsequent manual correction. The test dataset (n = 100) was manually delineated. Our segmentation model https://github.com/CAAI/AIMS/ was based on the popular nnU-Net, which has won several biomedical segmentation challenges. We tested our model against the published segmentation models HD-MS-Lesions, which is also based on nnU-Net, trained with a more homogenous patient cohort. We furthermore tested model robustness to data from unseen scanners by performing a leave-one-scanner-out experiment. Results: We found that our model was able to segment MS white matter lesions with a performance comparable to literature: DSC = 0.68, precision = 0.90, recall = 0.70, f1 = 0.78. Furthermore, the model outperformed HD-MS-Lesions in all metrics except precision = 0.96. In the leave-one-scanner-out experiment there was no significant change in performance (p < 0.05) between any of the models which were only trained on part of the dataset and the full segmentation model. Conclusion: In conclusion we have seen, that by including a large, heterogeneous dataset emulating clinical reality, we have trained a segmentation model which maintains a high segmentation performance while being robust to data from unseen scanners. This broadens the applicability of the model in clinic and paves the way for clinical implementation. [ABSTRACT FROM AUTHOR]

Background: Dynamic contrast‐enhanced MRI (DCE‐MRI) has seen increasing use for quantification of low level of blood–brain barrier (BBB) leakage in various pathological disease states and correlations with clinical outcomes. However, currently there exists limited studies on reproducibility in healthy controls, which is important for the establishment of a normality threshold for future research. Purpose: To investigate the reproducibility of DCE‐MRI and to evaluate the effect of arterial input function (AIF) selection and manual region of interests (ROI) delineation vs. automated global segmentation. Study Type: Prospective. Population: A total of 16 healthy controls; 11 females; mean age 28.7 years (SD 10.1). Field Strength/Sequence: A 3T; GE DCE; 3D TFE T1WI. 2D TSE T2. Assessment: The influx constant Ki, a measure of BBB permeability, and Vp, the blood plasma volume, was calculated using the Patlak model. Cerebral blood flow (CBF) was calculated using Tikhonov model free deconvolution. Manual tissue ROIs, drawn by H.J.S. (30+ years of experience), were compared to automatic tissue segmentation. Statistical Tests: Intraclass correlation coefficient (ICC) and repeatability coefficient (RC) was used to assess reproducibility. Bland–Altman plots were used to evaluate agreement between measurements day 1 vs. day 2, and manual vs. segmentation method. Results: Ki showed excellent reproducibility in both white and gray matter with an ICC between 0.79 and 0.82 and excellent agreement between manual ROI and automatic segmentation, with an ICC of 0.89 for Ki in WM. Furthermore, Ki values in gray and white matter conforms with histological tissue characteristics, where gray matter generally has a 2‐fold higher vessel density. The highest reproducibility measures of Ki (ICC = 0.83), CBF (ICC = 0.77) and Vd (ICC = 0.83) was obtained with the AIF sampled in the internal carotid artery (ICA). Data Conclusion: DCE‐MRI shows excellent reproducibility of pharmacokinetic variables derived from healthy controls. Evidence Level: 2 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Abstract Background Magnetic Resonance Imaging (MRI) methods were evaluated as a tool for the study of experimental meningitis. The identification and characterisation of pathophysiological parameters that vary during the course of the disease could be used as markers for future studies of new treatment strategies. Methods Rats infected intracisternally with S. pneumoniae (n = 29) or saline (n = 13) were randomized for imaging at 6, 12, 24, 30, 36, 42 or 48 hours after infection. T1W, T2W, quantitative diffusion, and post contrast T1W images were acquired at 4.7 T. Dynamic MRI (dMRI) was used to evaluate blood-brain-barrier (BBB) permeability and to obtain a measure of cerebral and muscle perfusion. Clinical- and motor scores, bacterial counts in CSF and blood, and WBC counts in CSF were measured. Results MR images and dMRI revealed the development of a highly significant increase in BBB permeability (P < 0.002) and ventricle size (P < 0.0001) among infected rats. Clinical disease severity was closely related to ventricle expansion (P = 0.024). Changes in brain water distribution, assessed by ADC, and categorization of brain 'perfusion' by cortex ΔSI(bolus) were subject to increased inter-rat variation as the disease progressed, but without overall differences compared to uninfected rats (P > 0.05). Areas of well-'perfused' muscle decreased with the progression of infection indicative of septicaemia (P = 0.05). Conclusion The evolution of bacterial meningitis was successfully followed in-vivo with MRI. Increasing BBB-breakdown and ventricle size was observed in rats with meningitis whereas changes in brain water distribution were heterogeneous. MRI will be a valuable technique for future studies aiming at evaluating or optimizing adjunctive treatments