High Resolution Quantitative MRI in a Non-Surgical Model of Spinal Cord Injury

Magnetic resonance imaging (MRI) is very sensitive to the presence of damage resulting from injury or disease, but often lacks specificity. Quantitative MRI can significantly increase the specificity in the presence of pathology but must be validated, often using an animal model, for each type of injury or disease. In the case of spinal cord injury (SCI) most models are difficult to image, either due to the location of the injury, or as a result of damage to surrounding tissues resulting from invasive surgical procedures. This thesis describes a non-surgical model of rat SCI which uses MR guided focused ultrasound and microbubbles to create an injury the cervical spinal cord which is optimal for performing quantitative MRI, and compares it with other models of SCI using MRI, histology, and immunohistochemistry. It also describes the difficulties encountered when implementing the quantitative T2 (qT2) MR sequence at the very high resolution required to image the rat spinal cord, the limitations on the qT2 sequence due to the presence of diffusion, and how the effects of diffusion were minimized. Using the new SCI model and qT2 sequence, qT2 and diffusion data were acquired at 24 hours, 1 week, and 2 weeks following SCI, and the quantitative MRI parameters were correlated with histology. The increased specificity gained using quantitative MRI will increase the information available at each timepoint, reducing both the variability and cost of longitudinal studies aimed at developing treatments for SCI.Ph.D

Intraindividual variability of striatal 1H-MRS brain metabolite measurements at 3 T

Purpose To measure possible positional and diurnal physiological effects on brain metabolites in single-voxel proton magnetic resonance spectroscopy (1H-MRS) measurements of the right and left striatum. Methods 1H-MRS measurements were performed in 10 healthy adult volunteers using a short echo PRESS sequence (TE=30 ms, TR=3000 ms). Each individual was scanned during both morning and afternoon hours. Regions of interest were right and left striatum. To control for systematic drift in scanner performance, 1H-MRS measurements of a standard phantom solution were also acquired. Statistical analysis was performed using a repeated measures analysis of variance that included three within-subject factors: metabolite (N-acetyl-aspartate [NAA] or creatine [Cr]), laterality (left or right caudate) and time (morning or afternoon). Results A significant interaction (P<.016) between time of day and metabolite levels was observed. Further exploration of this finding revealed a significant difference between morning and afternoon levels of NAA (P<.044) but not Cr. In addition, no significant morning-to-afternoon differences were observed for the 1H-MRS phantom measurements. Conclusions Systematic variation due to scanner performance does not account for the changes observed in repeated measurements of striatal NAA levels. This difference may be accounted for by either repositioning effects or circadian physiological effects. Further studies are required to learn whether time of day standardization of 1H-MRS acquisitions may contribute to improved reproducibility of measurements

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings