I received my B.S. in Biomedical Engineering and M.S. in Electrical Engineering from the Catholic University of America in Washington DC. While attending graduate school at CUA, I worked as a post-baccalaureate fellow at the National Institutes of Health in the Laboratory of Diagnostic Radiology Research under Dr. David A. Bluemke. During my fellowship at the NIH, I primarily worked on quantitative image analysis for a prototype whole-body photon-counting computed tomography scanner. After my time at CUA and the NIH, I received my M.S. in Bioengineering from the University of California - Los Angeles while conducting research under Dr. Daniel Ennis.
Education & Certifications
Master of Science, University of California - Los Angeles, Bioengineering (2018)
Master of Science, The Catholic University of America, Electrical Engineering (2017)
Bachelor of Science, The Catholic University of America, Biomedical Engineering (2015)
Snowboarding, surfing, and electric guitar.
Current Research and Scholarly Interests
Currently, I am involved in two main projects. The first is developing 3D printing techniques to improve the accuracy of ex vivo geometrical and microstructural cardiac modeling from in vivo cardiac MR acquisitions. The second is applying machine learning applications to MRI data as a way to improve overall image quality and reduce acquisition time.
Graduate Student Researcher, The University of California - Los Angeles (July 2017 - September 2018)
A graduate student researcher in the Magnetic Resonance Research Labs under the supervisor of Dr. Daniel B. Ennis.
Los Angeles, California
Post-Baccalaureate Fellow, The National Institutes of Health (September 2015 - May 2017)
Post-baccalaureate fellow in the Laboratory of Diagnostic Radiology Research under the supervision of Dr. David A. Bluemke.
Cardiac cine CT approaching 1mSv: implementation and assessment of a 58-ms temporal resolution protocol.
The international journal of cardiovascular imaging
Clinical use of cardiac cine CT imaging is limited by high radiation dose and low temporal resolution. To evaluate a low radiation dose, high temporal resolution cardiac cine CT protocol in human cardiac CT and phantom scans. CT scans of a circulating iodine target were reconstructed using the conventional single heartbeat half-scan (HS, approx. 175ms temporal resolution) and the 3-heartbeat multi-segment (MS, approx. 58ms) algorithms. Motion artifacts were quantified by the root-mean-square error (RMSE). Low-dose cardiac cine CT scans were performed in 55 subjects at a tube potential of 80 kVp and current of 80mA. Image quality of HS and MS scans was assessed by blinded reader quality assessment, left ventricular (LV) free wall motion, and LV ejection rate. Motion artifacts in phantom scans were higher in HS than in MS reconstructions (RSME 188 and 117 HU, respectively; p=0.001). Median radiation dose in human scans was 1.2mSv. LV late diastolic filling was observed more frequently in MS than in HS images (42 vs. 26 subjects, respectively; p<0.001). LV free wall systolic motion was more physiologic and had less error in MS than in HS reconstructions (sum-of-squared errors 34 vs. 45 mm2, respectively; p<0.001), and LV peak ejection rate was higher in MS than in HS reconstructions (166 vs. 152mL/s, respectively; p<0.001). Cardiac cine CT imaging is feasible at a low radiation dose of 1.2mSv. MS reconstruction showed improved imaging of rapid motion in phantom studies and human cardiac CTs.
View details for DOI 10.1007/s10554-020-01863-z
View details for PubMedID 32367189
Estimating cardiomyofiber strain in vivo by solving a computational model.
Medical image analysis
2020; 68: 101932
Since heart contraction results from the electrically activated contraction of millions of cardiomyocytes, a measure of cardiomyocyte shortening mechanistically underlies cardiac contraction. In this work we aim to measure preferential aggregate cardiomyocyte ("myofiber") strains based on Magnetic Resonance Imaging (MRI) data acquired to measure both voxel-wise displacements through systole and myofiber orientation. In order to reduce the effect of experimental noise on the computed myofiber strains, we recast the strains calculation as the solution of a boundary value problem (BVP). This approach does not require a calibrated material model, and consequently is independent of specific myocardial material properties. The solution to this auxiliary BVP is the displacement field corresponding to assigned values of myofiber strains. The actual myofiber strains are then determined by minimizing the difference between computed and measured displacements. The approach is validated using an analytical phantom, for which the ground-truth solution is known. The method is applied to compute myofiber strains using in vivo displacement and myofiber MRI data acquired in a mid-ventricular left ventricle section in N=8 swine subjects. The proposed method shows a more physiological distribution of myofiber strains compared to standard approaches that directly differentiate the displacement field.
View details for DOI 10.1016/j.media.2020.101932
View details for PubMedID 33383331
Estimating Aggregate Cardiomyocyte Strain Using In Vivo Diffusion and Displacement Encoded MRI.
IEEE transactions on medical imaging
Changes in left ventricular (LV) aggregate cardiomyocyte orientation and deformation underlie cardiac function and dysfunction. As such, in vivo aggregate cardiomyocyte "myofiber" strain (Eff) has mechanistic significance, but currently there exists no established technique to measure in vivo Eff. The objective of this work is to describe and validate a pipeline to compute in vivo Eff from magnetic resonance imaging (MRI) data. Our pipeline integrates LV motion from multi-slice Displacement ENcoding with Stimulated Echoes (DENSE) MRI with in vivo LV microstructure from cardiac Diffusion Tensor Imaging (cDTI) data. The proposed pipeline is validated using an analytical deforming heart-like phantom. The phantom is used to evaluate 3D cardiac strains computed from a widely available, open-source DENSE Image Analysis Tool. Phantom evaluation showed that a DENSE MRI signal-to-noise ratio (SNR) ≥ 20 is required to compute Eff with near-zero median strain bias and within a strain tolerance of 0.06. Circumferential and longitudinal strains are also accurately measured under the same SNR requirements, however, radial strain exhibits a median epicardial bias of -0.10 even in noise-free DENSE data. The validated framework is applied to experimental DENSE MRI and cDTI data acquired in eight (N = 8) healthy swine. The experimental study demonstrated that Eff has decreased transmural variability compared to radial and circumferential strains. The spatial uniformity and mechanistic significance of in vivo Eff make it a compelling candidate for characterization and early detection of cardiac dysfunction.
View details for DOI 10.1109/TMI.2019.2933813
View details for PubMedID 31398112
High-Resolution Ex Vivo Microstructural MRI After Restoring Ventricular Geometry via 3D Printing.
Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH
2019; 11504: 177–86
Computational modeling of the heart requires accurately incorporating both gross anatomical detail and local microstructural information. Together, these provide the necessary data to build 3D meshes for simulation of cardiac mechanics and electrophysiology. Recent MRI advances make it possible to measure detailed heart motion in vivo, but in vivo microstructural imaging of the heart remains challenging. Consequently, the most detailed measurements of microstructural organization and microanatomical infarct details are obtained ex vivo. The objective of this work was to develop and evaluate a new method for restoring ex vivo ventricular geometry to match the in vivo configuration. This approach aids the integration of high-resolution ex vivo microstructural information with in vivo motion measurements. The method uses in vivo cine imaging to generate surface meshes, then creates a 3D printed left ventricular (LV) blood pool cast and a pericardial mold to restore the ex vivo cardiac geometry to a mid-diastasis reference configuration. The method was evaluated in healthy (N = 7) and infarcted (N = 3) swine. Dice similarity coefficients were calculated between in vivo and ex vivo images for the LV cavity (0.93 ± 0.01), right ventricle (RV) cavity (0.80 ± 0.05), and the myocardium (0.72 ± 0.04). The R 2 coefficient between in vivo and ex vivo LV and RV cavity volumes were 0.95 and 0.91, respectively. These results suggest that this method adequately restores ex vivo geometry to match in vivo geometry. This approach permits a more precise incorporation of high-resolution ex vivo anatomical and microstructural data into computational models that use in vivo data for simulation of cardiac mechanics and electrophysiology.
View details for DOI 10.1007/978-3-030-21949-9_20
View details for PubMedID 31432042
View details for PubMedCentralID PMC6701689
Photon-Counting Computed Tomography for Vascular Imaging of the Head and Neck First In Vivo Human Results
2018; 53 (3): 135–42
The purpose of this study was to evaluate image quality of a spectral photon-counting detector (PCD) computed tomography (CT) system for evaluation of major arteries of the head and neck compared with conventional single-energy CT scans using energy-integrating detectors (EIDs).In this institutional review board-approved study, 16 asymptomatic subjects (7 men) provided informed consent and received both PCD and EID contrast-enhanced CT scans of the head and neck (mean age, 58 years; range, 46-75 years). Tube settings were (EID: 120 kVp/160 mA vs PCD: 140 kVp/108 mA) for all volunteers. Quantitative analysis included measurements of mean attenuation, image noise, and contrast-to-noise ratio (CNR). Spectral PCD data were used to reconstruct virtual monoenergetic images and iodine maps. A head phantom was used to validate iodine concentration measurements in PCD images only. Two radiologists blinded to detector type independently scored the image quality of different segments of the arteries, as well as diagnostic acceptability, image noise, and severity of artifacts of the PCD and EID images. Reproducibility was assessed with intraclass correlation coefficient. Linear mixed models that account for within-subject correlation of analyzed arterial segments were used. Linear regression and Bland-Altman analysis with 95% limits of agreement were used to calculate the accuracy of material decomposition.Photon-counting detector image quality scores were significantly higher compared with EID image quality scores with lower image noise (P < 0.01) and less image artifacts (P < 0.001). Photon-counting detector image noise was 9.1% lower than EID image noise (8.0 ± 1.3 HU vs 8.8 ± 1.5 HU, respectively, P < 0.001). Arterial segments showed artifacts on EID images due to beam hardening that were not present on PCD images. On PCD images of the head phantom, there was excellent correlation (R = 0.998) between actual and calculated iodine concentrations without significant bias (bias: -0.4 mg/mL [95% limits of agreements: -1.1 to 0.4 mg/mL]). Iodine maps had 20.7% higher CNR compared with nonspectral PCD (65.2 ± 9.0 vs 54.0 ± 4.5, P = 0.01), and virtual monoenergetic image at 70 keV showed similar CNR to nonspectral images (52.6 ± 4.2 vs 54.0 ± 4.5, P = 0.39).Photon-counting CT has the potential to improve the image quality of carotid and intracranial CT angiography compared with single-energy EID CT.
View details for DOI 10.1097/RLI.0000000000000418
View details for Web of Science ID 000425366500001
View details for PubMedID 28926370
View details for PubMedCentralID PMC5792306
Optimized energy of spectral coronary CT angiography for coronary plaque detection and quantification
JOURNAL OF CARDIOVASCULAR COMPUTED TOMOGRAPHY
2018; 12 (2): 108–14
To optimize spectral coronary computed tomography angiography (CTA) for quantification of coronary artery plaque components.Fifty-one subjects were prospectively enrolled (88.2% male) (NCT02740699). Dual energy coronary CTA was performed at 90/Sn150 kVp using a 3rd generation dual-source CT scanner (SOMATOM Force, Siemens Healthcare). Dual energy images were reconstructed with a) linear mixed blending of 90 and Sn150 kVp data, b) virtual monoenergetic algorithm from 40 to 150 keV (at 10- keV intervals), and c) noise-optimized virtual monoenergetic algorithm from 40 to 150 keV. Image noise, iodine signal-to-noise-ratio (SNR), and contrast-to-noise ratio (CNR) for calcified and non-calcified plaque were measured. Qualitative readings of image quality were performed. Semi-automated software (QAngioCT, Medis) was used to quantify coronary plaque. Linear mixed-models that account for within-subject correlation of plaques were used to compare the results.100-150 keV noise-optimized virtual monoenergetic images had lower image noise than linear mixed images (all P < 0.05). The highest iodine SNR was achieved in 40 keV noise-optimized virtual monoenergetic images (33.3 ± 0.6 vs 23.3 ± 0.7 for linear mixed images, P < 0.001). 40-70 keV noise-optimized virtual monoenergetic images and 70 keV virtual monoenergetic images had superior coronary plaque CNR versus linear mixed images (all P < 0.01) with a maximum improvement of 20.1% and 22.7% for calcified plaque and non-calcified plaque (38.8 ± 2.2 vs 32.3 ± 2.3 and 17.3 ± 1.3 vs 14.1 ± 1.4, respectively). Using 90/Sn150 kVp linear mixed images as a reference, the plaque quantity was similar for 70 keV noise-optimized virtual monoenergetic images whereas low keV images (e.g. 40 keV) yielded significantly higher coronary plaque volumes (all P < 0.001).Spectral coronary CTA with low energy (40-70 keV) post-processing can improve the CNR of coronary plaque components. However, low energies (such as 40 keV) resulted in different absolute volumes of coronary plaque compared to "conventional" mixed 90/Sn150 kVp images.
View details for DOI 10.1016/j.jcct.2018.01.006
View details for Web of Science ID 000428247900003
View details for PubMedID 29397334
Quarter-millimeter spectral coronary stent imaging with photon-counting CT: Initial experience.
Journal of cardiovascular computed tomography
2018; 12 (6): 509–15
To evaluate the performance and clinical feasibility of 0.25 mm resolution mode of a dual-energy photon-counting detector (PCD) computed tomography (CT) system for coronary stent imaging and to compare the results to state-of-the-art dual-energy energy-integrating detector (EID) CT.Coronary stents with different diameters (2.0-4.0 mm) were examined inside a coronary artery phantom consisting of plastic tubes filled with iodine-based and gadolinium-based contrast material diluted to approximate clinical concentrations (n = 18). EID images were acquired using 2nd and 3rd generation dual-source CT systems (SOMATOM Flash and SOMATOM Force, Siemens Healthcare) at 0.60 mm (defined as standard-resolution (SR)) isotropic voxel size. Radiation-dose matched PCD images were acquired using a human prototype PCD system (Siemens Healthcare) at 0.50 mm (SR) and 0.25 mm (HR) imaging modes. Images were reconstructed using optimized convolution kernels.Dual-energy HR PCD images significantly better stent lumen visualization (median: 69.5%, IQR: 61.2-78.9%) over dual-energy EID, and standard-resolution PCD images (median: 53.2-57.4%, all P < 0.01). HR PCD acquisitions reconstructed at SR image voxel size showed 25.3% lower image noise compared to SR PCD acquisitions (P < 0.001). High-resolution iodine and gadolinium maps, as well as virtual monoenergetic images, were calculated from the PCD data and enabled estimation of contrast agent concentration in the lumen without interference from the coronary stent.HR spectral PCD imaging significantly improves coronary stent lumen visibility over dual-energy EID. When the PCD-HR data was reconstructed into standard voxel sizes (0.5 mm isotropic) the image noise decreased by 25% compared to SR acquisition of PCD. Both dual-energy systems were consistent in estimating contrast agent concentrations.
View details for DOI 10.1016/j.jcct.2018.10.008
View details for PubMedID 30509378
Photon-Counting CT of the Brain: In Vivo Human Results and Image-Quality Assessment
AMERICAN JOURNAL OF NEURORADIOLOGY
2017; 38 (12): 2257–63
Photon-counting detectors offer the potential for improved image quality for brain CT but have not yet been evaluated in vivo. The purpose of this study was to compare photon-counting detector CT with conventional energy-integrating detector CT for human brains.Radiation dose-matched energy-integrating detector and photon-counting detector head CT scans were acquired with standardized protocols (tube voltage/current, 120 kV(peak)/370 mAs) in both an anthropomorphic head phantom and 21 human asymptomatic volunteers (mean age, 58.9 ± 8.5 years). Photon-counting detector thresholds were 22 and 52 keV (low-energy bin, 22-52 keV; high-energy bin, 52-120 keV). Image noise, gray matter, and white matter signal-to-noise ratios and GM-WM contrast and contrast-to-noise ratios were measured. Image quality was scored by 2 neuroradiologists blinded to the CT detector type. Reproducibility was assessed with the intraclass correlation coefficient. Energy-integrating detector and photon-counting detector CT images were compared using a paired t test and the Wilcoxon signed rank test.Photon-counting detector CT images received higher reader scores for GM-WM differentiation with lower image noise (all P < .001). Intrareader and interreader reproducibility was excellent (intraclass correlation coefficient, ≥0.86 and 0.79, respectively). Quantitative analysis showed 12.8%-20.6% less image noise for photon-counting detector CT. The SNR of photon-counting detector CT was 19.0%-20.0% higher than of energy-integrating detector CT for GM and WM. The contrast-to-noise ratio of photon-counting detector CT was 15.7% higher for GM-WM contrast and 33.3% higher for GM-WM contrast-to-noise ratio.Photon-counting detector brain CT scans demonstrated greater gray-white matter contrast compared with conventional CT. This was due to both higher soft-tissue contrast and lower image noise for photon-counting CT.
View details for DOI 10.3174/ajnr.A5402
View details for Web of Science ID 000419258900015
View details for PubMedID 28982793
Photon-counting CT for simultaneous imaging of multiple contrast agents in the abdomen: An in vivo study
2017; 44 (10): 5120–27
To demonstrate the feasibility of spectral imaging using photon-counting detector (PCD) x-ray computed tomography (CT) for simultaneous material decomposition of three contrast agents in vivo in a large animal model.This Institutional Animal Care and Use Committee-approved study used a canine model. Bismuth subsalicylate was administered orally 24-72 h before imaging. PCD CT was performed during intravenous administration of 40-60 ml gadoterate meglumine; 3.5 min later, iopamidol 370 was injected intravenously. Renal PCD CT images were acquired every 2 s for 5-6 min to capture the wash-in and wash-out kinetics of the contrast agents. Least mean squares linear material decomposition was used to calculate the concentrations of contrast agents in the aorta, renal cortex, renal medulla and renal pelvis.Using reference vials with known concentrations of materials, we computed molar concentrations of the various contrast agents during each phase of CT scanning. Material concentration maps allowed simultaneous quantification of both arterial and delayed renal enhancement in a single CT acquisition. The accuracy of the material decomposition algorithm in a test phantom was -0.4 ± 2.2 mM, 0.3 ± 2.2 mM for iodine and gadolinium solutions, respectively. Peak contrast concentration of gadolinium and iodine in the aorta, renal cortex, and renal medulla were observed 16, 24, and 60 s after the start each injection, respectively.Photon-counting spectral CT allowed simultaneous material decomposition of multiple contrast agents in vivo. Besides defining contrast agent concentrations, tissue enhancement at multiple phases was observed in a single CT acquisition, potentially obviating the need for multiphase CT scans and thus reducing radiation dose.
View details for DOI 10.1002/mp.12301
View details for Web of Science ID 000412901300021
View details for PubMedID 28444761
View details for PubMedCentralID PMC5699215
Dual-contrast agent photon-counting computed tomography of the heart: initial experience
INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING
2017; 33 (8): 1253–61
To determine the feasibility of dual-contrast agent imaging of the heart using photon-counting detector (PCD) computed tomography (CT) to simultaneously assess both first-pass and late enhancement of the myocardium. An occlusion-reperfusion canine model of myocardial infarction was used. Gadolinium-based contrast was injected 10 min prior to PCD CT. Iodinated contrast was infused immediately prior to PCD CT, thus capturing late gadolinium enhancement as well as first-pass iodine enhancement. Gadolinium and iodine maps were calculated using a linear material decomposition technique and compared to single-energy (conventional) images. PCD images were compared to in vivo and ex vivo magnetic resonance imaging (MRI) and histology. For infarct versus remote myocardium, contrast-to-noise ratio (CNR) was maximal on late enhancement gadolinium maps (CNR 9.0 ± 0.8, 6.6 ± 0.7, and 0.4 ± 0.4, p < 0.001 for gadolinium maps, single-energy images, and iodine maps, respectively). For infarct versus blood pool, CNR was maximum for iodine maps (CNR 11.8 ± 1.3, 3.8 ± 1.0, and 1.3 ± 0.4, p < 0.001 for iodine maps, gadolinium maps, and single-energy images, respectively). Combined first-pass iodine and late gadolinium maps allowed quantitative separation of blood pool, scar, and remote myocardium. MRI and histology analysis confirmed accurate PCD CT delineation of scar. Simultaneous multi-contrast agent cardiac imaging is feasible with photon-counting detector CT. These initial proof-of-concept results may provide incentives to develop new k-edge contrast agents, to investigate possible interactions between multiple simultaneously administered contrast agents, and to ultimately bring them to clinical practice.
View details for DOI 10.1007/s10554-017-1104-4
View details for Web of Science ID 000405226700017
View details for PubMedID 28289990
Low-dose lung cancer screening with photon-counting CT: a feasibility study
PHYSICS IN MEDICINE AND BIOLOGY
2017; 62 (1): 202–13
To evaluate the feasibility of using a whole-body photon-counting detector (PCD) CT scanner for low-dose lung cancer screening compared to a conventional energy integrating detector (EID) system. Radiation dose-matched EID and PCD scans of the COPDGene 2 phantom were acquired at different radiation dose levels (CTDIvol: 3.0, 1.5, and 0.75 mGy) and different tube voltages (120, 100, and 80 kVp). EID and PCD images were compared for quantitative Hounsfield unit (HU) accuracy, noise levels, and contrast-to-noise ratios (CNR) for detection of ground-glass nodules (GGN) and emphysema. The PCD HU accuracy was better than EID for water at all scan parameters. PCD HU stability for lung, GGN and emphysema regions were superior to EID and PCD attenuation values were more reproducible than EID for all scan parameters (all P < 0.01), while HUs for lung, GGN and emphysema ROIs changed significantly for EID with decreasing dose (all P < 0.001). PCD showed lower noise levels at the lowest dose setting at 120, 100 and 80 kVp (15.2 ± 0.3 HU versus 15.8 ± 0.2 HU, P = 0.03; 16.1 ± 0.3 HU versus 18.0 ± 0.4 HU, P = 0.003; and 16.1 ± 0.3 HU versus 17.9 ± 0.3 HU, P = 0.001, respectively), resulting in superior CNR for evaluation of GGNs and emphysema at 100 and 80 kVp. PCD provided better HU stability for lung, ground-glass, and emphysema-equivalent foams at lower radiation dose settings with better reproducibility than EID. Additionally, PCD showed up to 10% less noise, and 11% higher CNR at 0.75 mGy for both 100 and 80 kVp. PCD technology may help reduce radiation exposure in lung cancer screening while maintaining diagnostic quality.
View details for DOI 10.1088/1361-6560/62/1/202
View details for Web of Science ID 000391567700004
View details for PubMedID 27991453
View details for PubMedCentralID PMC5237389
- Polynomial regression, Area and Length based filtering to remove misclassified pixels acquired in the crack segmentation process of 2D X-ray CT images of tested plaster specimens IEEE. 2015: 437–42