Honors & Awards
TBI2 Fellow, Radiological Sciences Laboratory
Graduate Research Fellowship Program, National Science Foundation
Education & Certifications
Master of Science, Stanford University, BIOE-MS (2020)
Bachelor of Arts and Sciences, Dartmouth College, Engineering Sciences
Optimizing the Frame Duration for Data-Driven Rigid Motion Estimation in Brain PET Imaging.
Data-driven rigid motion estimation for PET brain imaging is usually performed using data frames sampled at low temporal resolution to reduce the overall computation time and to provide adequate signal-to-noise ratio in the frames. In recent work it has been demonstrated that list-mode reconstructions of ultra-short frames are sufficient for motion estimation and can be performed very quickly. In this work we take the approach of using image-based registration of reconstructions of very short frames for data-driven motion estimation, and optimize a number of reconstruction and registration parameters (frame duration, MLEM iterations, image pixel size, post-smoothing filter, reference image creation, and registration metric) to ensure accurate registrations while maximizing temporal resolution and minimizing total computation time.Data from 18 F-uorodeoxyglucose (FDG) and 18 F-orbetaben (FBB) tracer studies with varying count rates are analysed, for PET/MR and PET/CT scanners. For framed reconstructions using various parameter combinations inter-frame motion is simulated and image-based registrations are performed to estimate that motion.For FDG and FBB tracers using 4 × 105 true and scattered coincidence events per frame ensures that 95% of the registrations will be accurate to within 1 mm of the ground truth. This corresponds to a frame duration of 0:5 - 1 sec for typical clinical PET activity levels. Using 4 MLEM iterations with no subsets, a transaxial pixel size of 4 mm, a post-smoothing filter with 4-6 mm full-width at half-maximum, and averaging two or more frames to create the reference image provides an optimal set of parameters to produce accurate registrations while keeping the reconstruction and processing time low.It is shown that very short frames (≤ 1 sec) can be used to provide accurate and quick data-driven rigid motion estimates for use in an event-by-event motion corrected reconstruction.
View details for DOI 10.1002/mp.14889
View details for PubMedID 33880778
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
Simultaneous FDG-PET/MRI detects hippocampal subfield metabolic differences in AD/MCI.
2020; 10 (1): 12064
The medial temporal lobe is one of the most well-studied brain regions affected by Alzheimer's disease (AD). Although the spread of neurofibrillary pathology in the hippocampus throughout the progression of AD has been thoroughly characterized and staged using histology and other imaging techniques, it has not been precisely quantified in vivo at the subfield level using simultaneous positron emission tomography (PET) and magnetic resonance imaging (MRI). Here, we investigate alterations in metabolism and volume using [18F]fluoro-deoxyglucose (FDG) and simultaneous time-of-flight (TOF) PET/MRI with hippocampal subfield analysis of AD, mild cognitive impairment (MCI), and healthy subjects. We found significant structural and metabolic changes within the hippocampus that can be sensitively assessed at the subfield level in a small cohort. While no significant differences were found between groups for whole hippocampal SUVr values (p=0.166), we found a clear delineation in SUVr between groups in the dentate gyrus (p=0.009). Subfield analysis may be more sensitive for detecting pathological changes using PET-MRI in AD compared to global hippocampal assessment.
View details for DOI 10.1038/s41598-020-69065-0
View details for PubMedID 32694602
Neuroinflammation PET imaging: Current opinion and future directions.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Neuroinflammation is a pathological hallmark of numerous neurologic diseases. Positron emission tomography (PET) imaging enables a non-invasive means to investigate, quantify, and track the spatiotemporal dynamics of various immune cells in living subjects. Translocator protein 18 kDa (TSPO)-PET is a technique for detecting glial activation that has yielded valuable clinical data linking neuroinflammation to cognitive decline in neurodegenerative diseases and has also been used preliminarily as a therapy monitoring tool. However, considerable limitations of TSPO-PET have prompted identification of other more cell-specific and functionally relevant biomarkers. This review analyzes the clinical potential of available and emerging PET biomarkers of innate and adaptive immune responses, with mention of exciting future directions for the field.
View details for DOI 10.2967/jnumed.119.229443
View details for PubMedID 32620705
A within-coil optical prospective motion-correction system for brain imaging at 7T.
Magnetic resonance in medicine
Motion artifact limits the clinical translation of high-field MR. We present an optical prospective motion correction system for 7 Tesla MRI using a custom-built, within-coil camera to track an optical marker mounted on a subject.The camera was constructed to fit between the transmit-receive coils with direct line of sight to a forehead-mounted marker, improving upon prior mouthpiece work at 7 Tesla MRI. We validated the system by acquiring a 3D-IR-FSPGR on a phantom with deliberate motion applied. The same 3D-IR-FSPGR and a 2D gradient echo were then acquired on 7 volunteers, with/without deliberate motion and with/without motion correction. Three neuroradiologists blindly assessed image quality. In 1 subject, an ultrahigh-resolution 2D gradient echo with 4 averages was acquired with motion correction. Four single-average acquisitions were then acquired serially, with the subject allowed to move between acquisitions. A fifth single-average 2D gradient echo was acquired following subject removal and reentry.In both the phantom and human subjects, deliberate and involuntary motion were well corrected. Despite marked levels of motion, high-quality images were produced without spurious artifacts. The quantitative ratings confirmed significant improvements in image quality in the absence and presence of deliberate motion across both acquisitions (P < .001). The system enabled ultrahigh-resolution visualization of the hippocampus during a long scan and robust alignment of serially acquired scans with interspersed movement.We demonstrate the use of a within-coil camera to perform optical prospective motion correction and ultrahigh-resolution imaging at 7 Tesla MRI. The setup does not require a mouthpiece, which could improve accessibility of motion correction during 7 Tesla MRI exams.
View details for DOI 10.1002/mrm.28211
View details for PubMedID 32077521