Alan B. McMillan

Voxel-based morphometry of unilateral temporal lobe epilepsy reveals abnormalities in cerebral white matter

Voxel-based morphometric (VBM) investigations of temporal lobe epilepsy have focused on the presence and distribution of gray matter abnormalities. VBM studies to date have identified the expected abnormalities in hippocampus and extrahippocampal temporal lobe, as well as more diffuse abnormalities in the thalamus, cerebellum, and extratemporal neocortical areas. To date, there has not been a comprehensive VBM investigation of cerebral white matter in nonlesional temporal lobe epilepsy. This study examined 25 lateralized temporal lobe epilepsy patients (13 left, 12 right) and 62 healthy controls in regard to both temporal and extratemporal lobe gray and white matter. Consistent with prior reports, gray matter abnormalities were evident in ipsilateral hippocampus and ipsilateral thalamus. Temporal and extratemporal white matter was affected ipsilateral to the side of seizure onset, in both left and right temporal lobe epilepsy groups. These findings indicate that chronic temporal lobe epilepsy is associated not only with abnormalities in gray matter, but also with concomitant abnormalities in cerebral white matter regions that may affect connectivity both within and between the cerebral hemispheres.

Independent component analysis applied to self-paced functional MR imaging paradigms☆

Self-paced functional MR imaging (fMRI) paradigms, in which the task timing is determined by the subjects performance, can offer several advantages over commonly applied paradigms with predetermined stimulus timing. Independent component analysis (ICA) does not require specification of a timed response function, and could be an advantageous method of deriving results from fMRI data sets with varying response timings and durations. In this study normal volunteers (N = 10) each performed two self-paced fMRI motor and arithmetic paradigms. Individual data sets were analyzed with the Infomax spatial ICA algorithm. Conventional regression analysis was performed for comparison purposes. Spatial ICA effectively produced task-related components from each of the self-paced data sets, even in a few cases where regression analysis yielded non-specific functional maps. For the motor paradigm, these components consistently mapped to primary motor areas. ICA of the arithmetic paradigm yielded multiple task-related components that variably mapped to regions of parietal and frontal lobes. Regression analysis generally yielded similar spatial maps. The multiple task-related ICA components that were sometimes produced from each self-paced data set can be challenging to identify and evaluate for significance. These preliminary results indicate that ICA is useful as an exploratory and complementary method to conventional regression analysis for fMRI of self-paced paradigms.

Deep Learning MR Imaging–based Attenuation Correction for PET/MR Imaging

Purpose To develop and evaluate the feasibility of deep learning approaches for magnetic resonance (MR) imaging-based attenuation correction (AC) (termed deep MRAC) in brain positron emission tomography (PET)/MR imaging. Materials and Methods A PET/MR imaging AC pipeline was built by using a deep learning approach to generate pseudo computed tomographic (CT) scans from MR images. A deep convolutional auto-encoder network was trained to identify air, bone, and soft tissue in volumetric head MR images coregistered to CT data for training. A set of 30 retrospective three-dimensional T1-weighted head images was used to train the model, which was then evaluated in 10 patients by comparing the generated pseudo CT scan to an acquired CT scan. A prospective study was carried out for utilizing simultaneous PET/MR imaging for five subjects by using the proposed approach. Analysis of covariance and paired-sample t tests were used for statistical analysis to compare PET reconstruction error with deep MRAC and two existing MR imaging-based AC approaches with CT-based AC. Results Deep MRAC provides an accurate pseudo CT scan with a mean Dice coefficient of 0.971 ± 0.005 for air, 0.936 ± 0.011 for soft tissue, and 0.803 ± 0.021 for bone. Furthermore, deep MRAC provides good PET results, with average errors of less than 1% in most brain regions. Significantly lower PET reconstruction errors were realized with deep MRAC (-0.7% ± 1.1) compared with Dixon-based soft-tissue and air segmentation (-5.8% ± 3.1) and anatomic CT-based template registration (-4.8% ± 2.2). Conclusion The authors developed an automated approach that allows generation of discrete-valued pseudo CT scans (soft tissue, bone, and air) from a single high-spatial-resolution diagnostic-quality three-dimensional MR image and evaluated it in brain PET/MR imaging. This deep learning approach for MR imaging-based AC provided reduced PET reconstruction error relative to a CT-based standard within the brain compared with current MR imaging-based AC approaches.

Single Acquisition Quantitative Single‐Point Electron Paramagnetic Resonance Imaging

Electron paramagnetic resonance imaging has emerged as a promising noninvasive technology to dynamically image tissue oxygenation. Owing to its extremely short spin–spin relaxation times, electron paramagnetic resonance imaging benefits from a single‐point imaging scheme where the entire free induction decay signal is captured using pure phase encoding. However, direct T2*/pO2 quantification is inhibited owing to constant magnitude gradients which result in time‐decreasing field of view. Therefore, conventional acquisition techniques require repeated imaging experiments with differing gradient amplitudes (typically 3), which results in long acquisition time.

Ramped hybrid encoding for improved ultrashort echo time imaging.

We propose a new acquisition to minimize the per‐excitation encoding duration and improve the imaging capability for short T2* species.

Accelerated 4D quantitative single point EPR imaging using model-based reconstruction

Electron paramagnetic resonance imaging has surfaced as a promising noninvasive imaging modality that is capable of imaging tissue oxygenation. Due to extremely short spin‐spin relaxation times, electron paramagnetic resonance imaging benefits from single‐point imaging and inherently suffers from limited spatial and temporal resolution, preventing localization of small hypoxic tissues and differentiation of hypoxia dynamics, making accelerated imaging a crucial issue.

Rapid dual‐echo ramped hybrid encoding MR‐based attenuation correction (dRHE‐MRAC) for PET/MR

In this study, we propose a rapid acquisition for MR‐based attenuation correction (MRAC) in positron emission tomography (PET)/MR imaging, in which an ultrashort echo time (UTE) image and an out‐of‐phase echo image are obtained within a single rapid scan (35 s) at high spatial resolution (1 mm3), which allows accurate estimation of a pseudo CT image using 4‐class tissue classification (discrete bone, discrete air, continuous fat, and continuous water).

Externally calibrated parallel imaging for 3D multispectral imaging near metallic implants using broadband ultrashort echo time imaging.

To develop an externally calibrated parallel imaging technique for three‐dimensional multispectral imaging (3D‐MSI) in the presence of metallic implants.

A rapid and robust gradient measurement technique using dynamic single-point imaging.

We propose a new gradient measurement technique based on dynamic single‐point imaging (SPI), which allows simple, rapid, and robust measurement of k‐space trajectory.

Fully phase‐encoded MRI near metallic implants using ultrashort echo times and broadband excitation

To develop a fully phase‐encoded MRI method for distortion‐free imaging near metallic implants, in clinically feasible acquisition times.