Campeau, Norbert G.

Objective This study investigates a locally low-rank (LLR) denoising algorithm applied to source images from a clinical task-based functional MRI (fMRI) exam before post-processing for improving statistical confidence of task-based activation maps. Methods Task-based motor and language fMRI was obtained in eleven healthy volunteers under an IRB approved protocol. LLR denoising was then applied to raw complex-valued image data before fMRI processing. Activation maps generated from conventional non-denoised (control) data were compared with maps derived from LLR-denoised image data. Four board-certified neuroradiologists completed consensus assessment of activation maps; region-specific and aggregate motor and language consensus thresholds were then compared with nonparametric statistical tests. Additional evaluation included retrospective truncation of exam data without and with LLR denoising; a ROI-based analysis tracked t-statistics and temporal SNR (tSNR) as scan durations decreased. A test-retest assessment was performed; retest data were matched with initial test data and compared for one subject. Results fMRI activation maps generated from LLR-denoised data predominantly exhibited statistically significant ( p = 4.88×10 –4 to p = 0.042; one p = 0.062) increases in consensus t-statistic thresholds for motor and language activation maps. Following data truncation, LLR data showed task-specific increases in t-statistics and tSNR respectively exceeding 20 and 50% compared to control. LLR denoising enabled truncation of exam durations while preserving cluster volumes at fixed thresholds. Test-retest showed variable activation with LLR data thresholded higher in matching initial test data. Conclusion LLR denoising affords robust increases in t-statistics on fMRI activation maps compared to routine processing, and offers potential for reduced scan duration while preserving map quality.

BackgroundA low‐cryogen, compact 3T (C3T) MRI scanner with high‐performance gradients capable of simultaneously achieving 80 mT/m gradient amplitude and 700 T/m/second slew rate has been in use to study research patients since March 2016 but has not been implemented in the clinical practice.PurposeTo compare head MRI examinations obtained with the C3T system and a conventional whole‐body 3T (WB3T) scanner in seven parameters across five commonly used brain imaging sequences.Study TypeProspective.SubjectsThirty patients with a clinically indicated head MRI.Sequence 3T; T1 FLAIR, T1 MP‐RAGE, 3D T2 FLAIR, T2 FSE, and DWI.AssessmentAll patients tolerated the scans well. Three board‐certified neuroradiologists scored the comparative quality of C3T and WB3T images in blinded fashion using a five‐point Likert scale in terms of: signal‐to‐noise ratio, lesion conspicuity, motion artifact, gray/white matter contrast, cerebellar folia, susceptibility artifact, and overall quality.Statistical TestLeft‐sided, right‐sided, and two‐sided Wilcoxon signed rank test; Fisher's method. A P value <0.05 was considered statistically significant.ResultsThe C3T system performed better than the WB3T in virtually all comparisons, except for motion artifacts for the T1 FLAIR and T1 MP‐RAGE sequences, where the WB3T system was deemed better. When combining all sequences together, the C3T system outperformed the WB3T system in all image quality parameters evaluated, except for motion artifact (P = 0.13).Data ConclusionThe C3T scanner provided better overall image quality for all sequences, and performed better in all individual categories, except for motion artifact on the T1 FLAIR and T1 MP‐RAGE.Level of Evidence2Technical Efficacy Stage1

Introduction There is increased interest in the use of artificial intelligence-based (AI) software packages in the evaluation of neuroimaging studies for acute ischemic stroke. We studied whether, compared to standard image interpretation without AI, Brainomix e-ASPECTS software improved interobserver agreement and accuracy in detecting ASPECTS regions affected in anterior circulation LVO. Methods We included 60 consecutive patients with anterior circulation LVO who had TICI 3 revascularization within 60 minutes of their baseline CT. A total of 16 readers, including senior neuroradiologists, junior neuroradiologists and vascular neurologists participated. Readers interpreted CT scans on independent workstations and assessed final ASPECTS and evaluated whether each individual ASPECTS region was affected. Two months later, readers again evaluated the CT scans, but with assistance of e-ASPECTS software. We assessed interclass correlation coefficient for total ASPECTS and interobserver agreement with Fleiss’ Kappa for each ASPECTS region with and without assistance of the e-ASPECTS. We also assessed accuracy for the readers with and without e-ASPECTS assistance. In our assessment of accuracy, ground truth was the 24 hour CT in this cohort of patients who had prompt and complete revascularization. Results Interclass correlation coefficient for total ASPECTS without e-ASPECTS assistance was 0.395, indicating fair agreement compared, to 0.574 with e-ASPECTS assistance, indicating good agreement (P < 0.01). There was significant improvement in inter-rater agreement with e-ASPECTS assistance for each individual region with the exception of M6 and caudate. The e-ASPECTS software had higher accuracy than the overall cohort of readers (with and without e-ASPECTS assistance) for every region except the caudate. Conclusions Use of Brainomix e-ASPECTS software resulted in significant improvements in inter-rater agreement and accuracy of ASPECTS score evaluation in a large group of neuroradiologists and neurologists. e-ASPECTS software was more predictive of final infarct/ASPECTS than the overall group interpreting the CT scans with and without e-ASPECTS assistance.

Abstract Background: MR fingerprinting (MRF) is a novel imaging method proposed for the diagnosis of Multiple Sclerosis (MS). This study aims to determine if MR Fingerprinting (MRF) relaxometry can differentiate frontal normal appearing white matter (F-NAWM) and splenium in patients diagnosed with MS as compared to controls and to characterize the relaxometry of demyelinating plaques relative to the time of diagnosis.Methods: Three-dimensional (3D) MRF data were acquired on a 3.0T MRI system resulting in isotropic voxels (1x1x1mm3) and a total acquisition time of 4min 38s. Data were collected on 18 subjects paired with 18 controls. Regions of interested were drawn over MRF-derived T1 relaxometry maps encompassing selected MS lesions, F-NAWM and splenium. T1 and T2 relaxometry features from those segmented areas were used to classify MS lesions from F-NAWM and splenium with T-distributed stochastic neighbor embedding algorithms (T-SNE). Partial least squares discriminant analysis (PLS-DA) was performed to discriminate NAWM and Splenium in MS compared with controls. Results: Mean out-of-fold machine learning prediction accuracy for discriminant results between MS patients and controls for F-NAWM was 65% and approached 90% for the splenium. There was significant positive correlation between time since diagnosis and MS lesions mean T2 (p=0.015), minimum T1 (p=0.03) and negative correlation with splenium uniformity (p=0.04). Perfect discrimination (AUC=1) was achieved between selected features from MS lesions and F-NAWM.Conclusions: 3D-MRF has the ability to differentiate between MS and controls based on relaxometry properties from the F-NAWM and splenium. Whole brain coverage allows the assessment of quantitative properties within lesions that provide chronological assessment of the time from MS diagnosis.

Arterial spin labeling (ASL) is a powerful noncontrast magnetic resonance imaging (MRI) technique that enables quantitative evaluation of brain perfusion. To optimize the clinical and research utilization of ASL, radiologists and physicists must understand the technical considerations and age‐related variations in normal and disease states. We discuss advanced applications of ASL across the lifespan, with example cases from children and adults covering a wide variety of pathologies. Through literature review and illustrated clinical cases, we highlight the subtleties as well as pitfalls of ASL interpretation. First, we review basic physical principles, techniques, and artifacts. This is followed by a discussion of normal perfusion variants based on age and physiology. The three major categories of perfusion abnormalities—hypoperfusion, hyperperfusion, and mixed patterns—are covered with an emphasis on clinical interpretation and relationship to the disease process. Major etiologies of hypoperfusion include large artery, small artery, and venous disease; other vascular conditions; global hypoxic–ischemic injury; and neurodegeneration. Hyperperfusion is characteristic of vascular malformations and tumors. Mixed perfusion patterns can be seen with epilepsy, migraine, trauma, infection/inflammation, and toxic‐metabolic encephalopathy.Level of Evidence4Technical Efficacy Stage3

A carotid web is a shelf-like intraluminal filling defect typically arising from the posterolateral wall of the proximal internal carotid artery. It is recognized as a possible cause of ischemic stroke in young adults. However, its etiopathogenesis is controversial and remains to be fully elucidated. The authors report de novo formation of a carotid web from an intimal dissection documented on serial imaging studies. The findings in this case suggest that a focal intimal dissection could be the underlying cause of a carotid web. Lower shear stress at the posterolateral wall of the proximal internal carotid artery is hypothesized to be a predisposing factor and explains the predilection of a carotid web for this specific location.

The authors report on a patient with craniospinal hypovolemia and inferior vena cava obstruction, and describe how the two conditions may be linked. This unique report further advances the emerging literature on spinal CSF venous fistulae.