Publications

Daniel Bruce Ennis
Daniel Bruce Ennis
Professor of Radiology (Veterans Affairs) and, by courtesy, of Bioengineering
dbe@stanford.edu

Bio

Daniel Ennis is a Professor in the Department of Radiology and Bioengineering (by courtesy). As an MRI scientist for nearly twenty-five years, he has worked to develop advanced basic science and translational cardiovascular MRI methods for quantitatively assessing structure, function, flow, and remodeling in both adult and pediatric populations. He began his research career as a Ph.D. student in the Department of Biomedical Engineering at Johns Hopkins University during which time he formed an active collaboration with investigators in the Laboratory of Cardiac Energetics at the National Heart, Lung, and Blood Institute (NIH/NHLBI). Thereafter, he joined the Departments of Radiological Sciences and Cardiothoracic Surgery at Stanford University as a postdoc and began to establish an independent research program with an NIH K99/R00 award focused on “Myocardial Structure, Function, and Remodeling in Mitral Regurgitation.” For ten years he led a group of clinicians and scientists at UCLA working to develop and evaluate advanced cardiovascular MRI exams as PI of several NIH funded studies. In 2018 he returned to the Department of Radiology at Stanford University as faculty in the Radiological Sciences Lab to bolster programs in cardiovascular MRI. He was a member of the committee that founded the Biomedical Physics (BMP) PhD program in the School of Medicine and continues to serve on the Executive Committee. He is also the Director of Radiology Research for the Veterans Administration Palo Alto Health Care System where he oversees a growing radiology research program. In 2025 he became the Division Chief for the Radiological Sciences Lab, a division within the Department of Radiology and home to about 80 faculty, staff, and trainees focused on advanced medical imaging research.

Publications

  • Personalized biventricular mechanics and sensitivity to model morphology. bioRxiv : the preprint server for biology Brown, A. L., Shi, L., Salvador, M., Kong, F., Ennis, D. B., Chen, I., Vedula, V., Marsden, A. L. 2025

    Abstract

    We present a computational framework for constructing patient-specific models of cardiac mechanics based on standard clinical data, including electrocardiogram (ECG), cuff blood pressure, and electrocardiography-gated computed tomography angiography (CTA) imaging. The model is coupled to a closed-loop lumped parameter network (LPN) circulatory model and incorporates rule-based fiber architecture, as well as spatially varying epicardial boundary conditions to approximate surrounding tissue support. Model parameters are personalized through a multistep procedure that sequentially tunes circulatory dynamics, passive mechanics, and active contraction. The resulting personalized BiV model closely matches clinical pressure and volume measurements and reasonably agrees with image-based myocardial deformation. To assess the impact of anatomical model choice, we compare the BiV model to two commonly-used simplifications: a truncated BiV (t-BiV) model cut at the basal plane and a left ventricle-only (LV) model. For these models, we also evaluate their sensitivity to plausible variations in boundary conditions and contractile strength. With all other inputs held fixed, the LV model exhibits similar global pressure/volume behavior, despite moderate differences in regional deformation. In contrast, the t-BiV model produces substantial differences in both global function and local myocardial mechanics. These results suggest that while LV-only models may be sufficient for biomechanical studies, truncation at the basal plane strongly impacts model outputs and should be used with caution.

    View details for DOI 10.64898/2025.12.11.693778

    View details for PubMedID 41446240

    View details for PubMedCentralID PMC12724658

  • The Effect of Voxel Volume and Voxel Shape on Cardiac Diffusion Tensor Imaging Metrics MAGNETIC RESONANCE IN MEDICINE Hannum, A. J., Cork, T. E., Setsompop, K., Ennis, D. B. 2025

    Abstract

    Cardiac diffusion tensor imaging (cDTI) is signal-to-noise ratio (SNR)-limited due to diffusion signal attenuation, long echo times from gradient moment nulling, and moderate myocardial T 2

  • Regional heterogeneity in left atrial stiffness impacts passive deformation in a cohort of patient-specific models. PLoS computational biology Baptiste, T. M., Rodero, C., Sillett, C. P., Strocchi, M., Lanyon, C. W., Augustin, C. M., Lee, A. W., Alonso Solís-Lemus, J., Roney, C. H., Ennis, D. B., Rajani, R., Rinaldi, C. A., Plank, G., Wilkinson, R. D., Williams, S. E., Niederer, S. A. 2025; 21 (11): e1013656

    Abstract

    In atrial fibrillation (AF), atrial biomechanics are altered, reducing atrial movement. It remains unclear whether these changes are due to altered anatomy, myocardial stiffness, or constraints from surrounding structures. Understanding the causes of changed atrial deformation in AF could enhance tissue characterization and inform AF diagnosis, stratification, and treatment. We created patient-specific anatomical models of the left atrium (LA) from CT images. Passive LA biomechanics were simulated using finite deformation continuum mechanics equations. LA stiffness was represented by the Guccione material law, where α scaled the anisotropic stiffness parameters. Regional passive stiffness parameters were calibrated to peak regional deformation during the reservoir phase and validated against deformation transients derived from retrospective gated CT images during the reservoir and conduit phase. Physiological LA deformation varies regionally, with the roof deforming significantly less than other regions during the reservoir phase. The fitted model matched peak patient deformations globally and regionally with an average error of [Formula: see text] mm over our cohort. We compared deformation transients through the reservoir and conduit phases and found that the simulated deformation transients were within an average of [Formula: see text] mm per unit time of the CT-derived deformation transients. Regional stiffness varied across the atria with average α values of 1.8, 1.6, 2.2, 1.6 and 2.1 across the cohort in the anterior, posterior, septum, lateral and roof regions respectively. Using mixed effect models, we found no correlation between regional patient LA deformation and regional estimates of wall thickness or regional volumes of epicardial adipose tissue. We found a significant correlation between regionally calibrated stiffness and CT-derived LA biomechanics (p = 0.023). We have shown that regional heterogeneity in stiffness contributes to regional LA biomechanics, while anatomical features appeared less important. These findings provide insight into the underlying causes of altered LA biomechanics in AF.

    View details for DOI 10.1371/journal.pcbi.1013656

    View details for PubMedID 41191659

  • Evaluation of EPI-Based Distortion Correction Techniques for Cardiac Diffusion Tensor Imaging. NMR in biomedicine Cork, T. E., Middione, M. J., Loecher, M., Liao, C., Setsompop, K., Ennis, D. B. 2025; 38 (11): e70147

    Abstract

    Cardiac diffusion tensor imaging (cDTI) is susceptible to image distortion while using an echo planar imaging (EPI) readouts. FSL TOPUP and TORTOISE DR-BUDDI are image distortion correction techniques that have been implemented to correct EPI distortion in neurological applications. We sought to establish which EPI-based distortion correction technique is most suitable for cDTI. Using a free-breathing second-order moment-compensated spin-echo technique, cDTI was acquired in healthy volunteers (N = 10) using both blip-up (BU) and blip-down (BD) EPI readouts. These datasets were then distortion corrected using the TOPUP and DR-BUDDI software packages. BU, BD, TOPUP, and DR-BUDDI images were then characterized by (1) geometric fidelity using the Dice Similarity coefficient (DSC), epicardial average Hausdorff distance (AHD epi

  • Cardiac mechanics modeling: recent developments and current challenges. ArXiv Brown, A. L., Liu, J., Ennis, D. B., Marsden, A. L. 2025

    Abstract

    Patient-specific computational models of the heart are powerful tools for cardiovascular research and medicine, with demonstrated applications in treatment planning, device evaluation, and surgical decision-making. Yet constructing such models is inherently difficult, reflecting the extraordinary complexity of the heart itself. Numerous considerations are required, including reconstructing the anatomy from medical images, representing myocardial mesostructure, capturing material behavior, defining model geometry and boundary conditions, coupling multiple physics, and selecting numerical methods. Many of these choices involve a tradeoff between physiological fidelity and modeling complexity. In this review, we summarize recent advances and unresolved questions in each of these areas, with particular emphasis on cardiac tissue mechanics. We argue that clarifying which complexities are essential, and which can be safely simplified, will be key to enabling clinical translation of these models.

    View details for DOI 10.1093/eurheartj/ehae619

    View details for PubMedID 40964080

    View details for PubMedCentralID PMC12440063