High-resolution hippocampal diffusion tensor imaging of mesial temporal sclerosis in refractory epilepsy.
OBJECTIVE: We explore the possibility of using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) to discern microstructural abnormalities in the hippocampus indicative of mesial temporal sclerosis (MTS) at the subfield level.METHODS: We analyzed data from 57 patients with refractory epilepsy who previously underwent 3.0-T magnetic resonance imaging (MRI) including DTI as a standard part of presurgical workup. We collected information about each subject's seizure semiology, conventional electroencephalography (EEG), high-density EEG, positron emission tomography reports, surgical outcome, and available histopathological findings to assign a final diagnostic category. We also reviewed the radiology MRI report to determine the radiographic category. DTI- and NODDI-based metrics were obtained in the hippocampal subfields.RESULTS: By examining diffusion characteristics among subfields in the final diagnostic categories, we found lower orientation dispersion indices and elevated axial diffusivity in the dentate gyrus in MTS compared to no MTS. By similarly examining among subfields in the different radiographic categories, we found all diffusion metrics were abnormal in the dentate gyrus and CA1. We finally examined whether diffusion imaging would better inform a radiographic diagnosis with respect to the final diagnosis, and found that dentate diffusivity suggested subtle changes that may help confirm a positive radiologic diagnosis.SIGNIFICANCE: The results suggest that diffusion metric analysis at the subfield level, especially in dentate gyrus and CA1, maybe useful for clinical confirmation of MTS.
View details for DOI 10.1111/epi.17330
View details for PubMedID 35751514
Hippocampal subfield imaging and fractional anisotropy show parallel changes in Alzheimer's disease tau progression using simultaneous tau-PET/MRI at 3T.
Alzheimer's & dementia (Amsterdam, Netherlands)
2021; 13 (1): e12218
Introduction: Alzheimer's disease (AD) is the most common form of dementia, characterized primarily by abnormal aggregation of two proteins, tau and amyloid beta. We assessed tau pathology and white matter connectivity changes in subfields of the hippocampus simultaneously in vivo in AD.Methods: Twenty-four subjects were scanned using simultaneous time-of-flight 18F-PI-2620 tau positron emission tomography/3-Tesla magnetic resonance imaging and automated segmentation.Results: We observed extensive tau elevation in the entorhinal/perirhinal regions, intermediate tau elevation in cornu ammonis 1/subiculum, and an absence of tau elevation in the dentate gyrus, relative to controls. Diffusion tensor imaging showed parahippocampal gyral fractional anisotropy was lower in AD and mild cognitive impairment compared to controls and strongly correlated with early tau accumulation in the entorhinal and perirhinal cortices.Discussion: This study demonstrates the potential for quantifiable patterns of 18F-PI2620 binding in hippocampus subfields, accompanied by diffusion and volume metrics, to be valuable markers of AD.
View details for DOI 10.1002/dad2.12218
View details for PubMedID 34337132
A Locally Adaptive Phase Aberration Correction (LAPAC) Method for Synthetic Aperture Sequences.
Phase aberration is a phenomenon caused by heterogeneity of the speed of sound in tissue, in which the actual speed of sound of the tissue is different than the assumed speed of sound used for beamforming. It reduces the quality and resolution of ultrasonic images and impairs clinical diagnostic capabilities. Although phase aberration correction (PAC) methods can reduce these detrimental effects, most practical implementations of PAC methods are based on the near field phase screen model, which have limited ability to represent the true aberration induced by inhomogeneous tissue. Accordingly, we propose a locally adaptive phase aberration correction (LAPAC) method that is applied through the use of synthetic aperture. The method is tested using full-wave simulations of models of human abdominal wall, experiments with tissue aberrators, and in vivo carotid images. LAPAC is compared with conventional phase aberration correction (cPAC) where aberration profiles are computed at a preselected depth and applied to the beamformer's time delays. For all experiments, LAPAC shows an average of 1 to 2 dB higher contrast than cPAC, and enhancements of 3 to 7 dB with respect to the uncorrected cases. We conclude that LAPAC may be a viable option to enhance ultrasound image quality images even in the presence of clinically relevant aberrating conditions.
View details for PubMedID 30222052
Local speed of sound estimation in tissue using pulse-echo ultrasound: Model-based approach.
The Journal of the Acoustical Society of America
2018; 144 (1): 254
A model and method to accurately estimate the local speed of sound in tissue from pulse-echo ultrasound data is presented. The model relates the local speeds of sound along a wave propagation path to the average speed of sound over the path, and allows one to avoid bias in the sound-speed estimates that can result from overlying layers of subcutaneous fat and muscle tissue. Herein, the average speed of sound using the approach by Anderson and Trahey is measured, and then the authors solve the proposed model for the local sound-speed via gradient descent. The sound-speed estimator was tested in a series of simulation and ex vivo phantom experiments using two-layer media as a simple model of abdominal tissue. The bias of the local sound-speed estimates from the bottom layers is less than 6.2m/s, while the bias of the matched Anderson's estimates is as high as 66m/s. The local speed-of-sound estimates have higher standard deviation than the Anderson's estimates. When the mean local estimate is computed over a 5-by-5mm region of interest, its standard deviation is reduced to less than 7m/s.
View details for PubMedID 30075660
B-line detection using amplitude modulation-frequency modulation (AM-FM) features
SPIE MEDICAL IMAGING
View details for DOI 10.1117/12.2285224
Effects of Phase Aberration and Phase Aberration Correction on the Minimum Variance Beamformer.
2018; 40 (1): 15-34
The minimum variance (MV) beamformer has the potential to enhance the resolution and contrast of ultrasound images but is sensitive to steering vector errors. Robust MV beamformers have been proposed but mainly evaluated in the presence of gross sound speed mismatches, and the impact of phase aberration correction (PAC) methods in mitigating the effects of phase aberration in MV beamformed images has not been explored. In this study, an analysis of the effects of aberration on conventional MV and eigenspace MV (ESMV) beamformers is carried out. In addition, the impact of three PAC algorithms on the performance of MV beamforming is analyzed. The different beamformers were tested on simulated data and on experimental data corrupted with electronic and tissue-based aberration. It is shown that all gains in performance of the MV beamformer with respect to delay-and-sum (DAS) are lost at high aberration strengths. For instance, with an electronic aberration of 60 ns, the lateral resolution of DAS degrades by 17% while MV degrades by 73% with respect to the images with no aberration. Moreover, although ESMV shows robustness at low aberration levels, its degradation at higher aberrations is approximately the same as that of regular MV. It is also shown that basic PAC methods improve the aberrated MV beamformer. For example, in the case of electronic aberration, multi-lag reduces degradation in lateral resolution from 73% to 28% and contrast loss from 85% to 25%. These enhancements allow the combination of MV and PAC to outperform DAS and PAC and ESMV in moderate and strong aberrations. We conclude that the effect of aberration on the MV beamformer is stronger than previously reported in the literature and that PAC is needed to improve its clinical potential.
View details for DOI 10.1177/0161734617717768
View details for PubMedID 28703644
Robust Minimum Variance Beamformer Using Locally Adaptive Phase Aberration Correction
View details for Web of Science ID 000387497400200