Faculty Bibliography

At very low diffusion weighting the diffusion MRI signal is affected by intravoxel incoherent motion (IVIM) caused by dephasing of magnetization due to incoherent blood flow in capillaries or other sources of microcirculation. While IVIM measurements at low diffusion weightings have been frequently used to investigate perfusion in the body as well as in malignant tissue, the effect and origin of IVIM in normal brain tissue is not completely established. We investigated the IVIM effect on the brain diffusion MRI signal in a cohort of 137 radiologically-normal patients (62 male; mean age = 50.2 ± 17.8, range = 18 to 94). We compared the diffusion tensor parameters estimated from a mono-exponential fit at b = 0 and 1000 s/mm² versus at b = 250 and 1000 s/mm². The asymptotic fitting method allowed for quantitative assessment of the IVIM signal fraction f* in specific brain tissue and regions. Our results show a mean (median) percent difference in the mean diffusivity of about 4.5 (4.9)% in white matter (WM), about 7.8 (8.7)% in cortical gray matter (GM), and 4.3 (4.2)% in thalamus. Corresponding perfusion fraction f* was estimated to be 0.033 (0.032) in WM, 0.066 (0.065) in cortical GM, and 0.033 (0.030) in the thalamus. The effect of f* with respect to age was found to be significant in cortical GM (Pearson correlation ρ = 0.35, p = 3*10^-5) and the thalamus (Pearson correlation ρ = 0.20, p = 0.022) with an average increase in f* of 5.17*10^-4/year and 3.61*10^-4/year, respectively. Significant correlations between f* and age were not observed for WM, and corollary analysis revealed no effect of gender on f*. Possible origins of the IVIM effect in normal brain tissue are discussed.

BACKGROUND AND PURPOSE/OBJECTIVE:Diagnostic errors in radiology are classified as perception or interpretation errors. This study determined whether specific conditions differed when perception or interpretation errors occurred during neuroradiology image interpretation. MATERIALS AND METHODS/METHODS:In a sample of 254 clinical error cases in diagnostic neuroradiology, we classified errors as perception or interpretation errors, then characterized imaging technique, interpreting radiologist's experience, anatomic location of the abnormality, disease etiology, time of day, and day of the week. Interpretation and perception errors were compared with hours worked per shift, cases read per shift, average cases read per shift hour, and the order of case during the shift when the error occurred. RESULTS:= .04). CONCLUSIONS:Among diagnostic neuroradiology error cases, interpretation-versus-perception errors are affected by the neuroradiologist's experience, technique, and the volume and rate of cases read. Recognition of these risk factors may help guide programs for error reduction in clinical neuroradiology services.

BACKGROUND AND PURPOSE/OBJECTIVE:The basal forebrain contains multiple structures of great interest to emerging functional neurosurgery applications, yet many neuroradiologists are unfamiliar with this neuroanatomy because it is not resolved with current clinical MR imaging. MATERIALS AND METHODS/METHODS:= 13) to demonstrate and characterize the detailed anatomy of the basal forebrain using a clinical 3T MR imaging scanner. We measured the size of selected internal myelinated pathways and measured subthalamic nucleus size, oblique orientation, and position relative to the intercommissural point. RESULTS:= .084 and .047, respectively). Individual variability for the subthalamic nucleus was greatest for angulation within the sagittal plane (range, 15°-37°), transverse dimension (range, 2-6.7 mm), and most inferior border (range, 4-7 mm below the intercommissural plane). CONCLUSIONS:Direct identification of basal forebrain structures in multiple planes using the TSE T2 sequence makes this challenging neuroanatomy more accessible to practicing neuroradiologists. This protocol can be used to better define individual variations relevant to functional neurosurgical targeting and validate/complement advanced MR imaging methods being developed for direct visualization of these structures in living patients.

Diffusion tractography is routinely used to study white matter architecture and brain connectivity in vivo. A key step for successful tractography of neuronal tracts is the correct identification of tract directions in each voxel. Here we propose a fingerprinting-based methodology to identify these fiber directions in Orientation Distribution Functions, dubbed ODF-Fingerprinting (ODF-FP). In ODF-FP, fiber configurations are selected based on the similarity between measured ODFs and elements in a pre-computed library. In noisy ODFs, the library matching algorithm penalizes the more complex fiber configurations. ODF simulations and analysis of bootstrapped partial and whole-brain in vivo datasets show that the ODF-FP approach improves the detection of fiber pairs with small crossing angles while maintaining fiber direction precision, which leads to better tractography results. Rather than focusing on the ODF maxima, the ODF-FP approach uses the whole ODF shape to infer fiber directions to improve the detection of fiber bundles with small crossing angle. The resulting fiber directions aid tractography algorithms in accurately displaying neuronal tracts and calculating brain connectivity.

Aim: Determine if MR-guided FDG-PET reconstruction improves diagnostic accuracy and epileptogenic lesion localization for patients with focal epilepsy. Introduction: Abnormalities detected on MRI or FDG PET alter clinical management and prognosis in patients with focal epilepsy considering surgery (1). Concordant MRI findings are not always present, whereas -80% of adult patients with chronic seizures have FDG PET abnormalities. State-of-art FDG PET, however, remains limited by partial volume effects (PVEs) that reduce sensitivity particularly for extra-temporal epilepsy (2). MR-guided (MRG) PET reconstruction reduces PVEs (3). We tested the hypothesis that MRG PET reconstruction increases correct localization of epileptogenic lesions across readers with different levels of clinical experience.
Method(s): After IRB approval, a neuroradiologist with 1000+ brain PET interpretations identified 26 epilepsy subjects that underwent simultaneous FDG PET-MRI (Siemens Biograph mMR, Siemens Healthcare, Erlangen, Germany) with final adjudicated diagnosis either as normal (N=10) or cortical dysplasia (N=16). PET emission images were reconstructed using conventional OSEM and MRG PET reconstructions (asymmetric Bowsher prior with 3D MPRAGE as anatomical prior image). Then, 3 blinded readers (with 12, 6 & 18 years of experience; respectively) evaluated cases containing either OSEM or MRG PET in the sagittal, axial and coronal planes for each case (MRI data was not provided). Readers determined if there were focal FDG abnormalities consistent with an epileptogenic zone, then assigned ordinal values to image quality (0-3; where 3 was "excellent") and diagnostic confidence (1-3; where 3 = "definite" abnormality or normal study).
Result(s): The figure below shows coronal OSEM and MRG PET reconstructions (A & B respectively) with co-registered MRI (C) - MRG PET better demonstrated the focal FDG abnormality associated with right frontal cortical dysplasia. All 3 readers rated MRG PET images higher in overall quality (2.6 +/- 0.7 vs 2.0 +/- 0.5, Mann-Whitney test, P<0.00001). Reconstruction method did not affect diagnostic confidence (2.6 +/- 0.7 vs 2.9 +/- 0.4, Mann-Whitney test, P=0.555). Readers 2 & 3 (with less experience reading brain FDG PET), improved their localization of the seizure focus using MRG PET images from 42.9 to 75%, and 50 to 75% correct respectively. Reader 1, with the most experience, demonstrated no change in correct localization (85.7 vs 83.3%), but reported more confidence in the diagnosis (P=0.033). Global percentage correct for all 3 raters increased from 59.5% to 77.8% (chi-squared test, P=0.086). MRG PET images increased interpretation sensitivity from 69% to 75%, specificity from 70% to 83% and accuracy from 70% to 78%, but these changes did not reach statistical significance.
Conclusion(s): These initial results demonstrate that MRG PET reconstruction of FDG data can increase correct seizure localization for PET readers with less experience. Study limitations include that clinical history, anatomical correlation and non-attenuation corrected FDG PET images were not available to blinded readers. Future work will increase the number of subjects evaluated by the 3 readers to increase statistical power

Spinal cord lesions detected on MRI hold important diagnostic and prognostic value for multiple sclerosis. Previous attempts to correlate lesion burden with clinical status have had limited success, however, suggesting that lesion location may be a contributor. Our aim was to explore the spatial distribution of multiple sclerosis lesions in the cervical spinal cord, with respect to clinical status. We included 642 suspected or confirmed multiple sclerosis patients (31 clinically isolated syndrome, and 416 relapsing-remitting, 84 secondary progressive, and 73 primary progressive multiple sclerosis) from 13 clinical sites. Cervical spine lesions were manually delineated on T2- and T2*-weighted axial and sagittal MRI scans acquired at 3 or 7 T. With an automatic publicly-available analysis pipeline we produced voxelwise lesion frequency maps to identify predilection sites in various patient groups characterized by clinical subtype, Expanded Disability Status Scale score and disease duration. We also measured absolute and normalized lesion volumes in several regions of interest using an atlas-based approach, and evaluated differences within and between groups. The lateral funiculi were more frequently affected by lesions in progressive subtypes than in relapsing in voxelwise analysis (P < 0.001), which was further confirmed by absolute and normalized lesion volumes (P < 0.01). The central cord area was more often affected by lesions in primary progressive than relapse-remitting patients (P < 0.001). Between white and grey matter, the absolute lesion volume in the white matter was greater than in the grey matter in all phenotypes (P < 0.001); however when normalizing by each region, normalized lesion volumes were comparable between white and grey matter in primary progressive patients. Lesions appearing in the lateral funiculi and central cord area were significantly correlated with Expanded Disability Status Scale score (P < 0.001). High lesion frequencies were observed in patients with a more aggressive disease course, rather than long disease duration. Lesions located in the lateral funiculi and central cord area of the cervical spine may influence clinical status in multiple sclerosis. This work shows the added value of cervical spine lesions, and provides an avenue for evaluating the distribution of spinal cord lesions in various patient groups.

BACKGROUND AND PURPOSE/OBJECTIVE:The brain stem is compactly organized with life-sustaining sensorimotor and autonomic structures that can be affected by numerous pathologies but can be difficult to resolve on conventional MR imaging. MATERIALS AND METHODS/METHODS:We applied an optimized TSE T2 sequence to washed postmortem brain samples to reveal exquisite and reproducible brain stem anatomic MR imaging contrast comparable with histologic atlases. This resource-efficient approach can be performed across multiple whole-brain samples with relatively short acquisition times (2 hours per imaging plane) using clinical 3T MR imaging systems. RESULTS:< .10). CONCLUSIONS:Compared with traditional atlases, multiplanar MR imaging contrast has advantages for learning and retaining brain stem anatomy for clinicians and trainees. Direct TSE MR imaging sequence discrimination of brain stem anatomy can help validate other MR imaging contrasts, such as diffusion tractography, or serve as a structural template for extracting quantitative MR imaging data in future postmortem investigations.

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T₁-, T₂-, and T₂^∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.

BACKGROUND AND PURPOSE/OBJECTIVE:High-resolution T2-weighted sequences are frequently used in magnetic resonance imaging (MRI) studies to assess the cerebellopontine angle and internal auditory canal (IAC) in sensorineural hearing loss patients but have low yield and lengthened examinations. Because image content in the Wavelet domain is sparse, compressed sensing (CS) that uses incoherent undersampling of k-space and iterative reconstruction can accelerate MRI acquisitions. We hypothesized that an accelerated CS T2 Sampling Perfection with Application optimized Contrasts using different flip angle Evolution (SPACE) sequence would produce acceptable diagnostic quality for IAC screening protocols. MATERIAL AND METHODS/METHODS:Seventy-six patients underwent 3 T MRI using conventional SPACE and a CS T2 SPACE prototype sequence for screening the IACs were identified retrospectively. Unilateral reconstructions for each sequence were separated, then placed into mixed folders for independent, blinded review by 3 neuroradiologists during 2 sessions 4 weeks apart. Radiologists reported if a lesion was present. Motion and visualization of specific structures were rated using ordinal scales. McNemar, Wilcoxon, Cohen κ, and Mann-Whitney U tests were performed for accuracy, equivalence, and interrater and intrarater reliability. RESULTS:T2 SPACE using CS reconstruction reduced scan time by 80% to 50 seconds and provided 98.7% accuracy for IAC mass detection by 3 raters. Radiologists preferred conventional images (0.7-1.0 reduction on 5-point scale, P < 0.001), but rated CS SPACE acceptable. The 95% confidence for reduction in any cerebellopontine angle, IAC, or fluid-filled inner ear structure assessment with CS SPACE did not exceed 0.5. CONCLUSIONS:Internal auditory canal screening MRI protocols can be performed using a 5-fold accelerated T2 SPACE sequence with compressed sensing while preserving diagnostic image quality and acceptable lesion detection rate.