Publications

Associate Professor of Radiology (Veterans Affairs)

Bio

Daniel Ennis (Ph.D.) is an Associate Professor in the Department of Radiology. As an MRI scientist for nearly twenty years, he has worked to develop advanced 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 post doc 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 Stanford Radiology and the Radiological Sciences Lab to bolster programs in cardiovascular MRI. 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.

Publications

  • Diffusion biomarkers in chronic myocardial infarction. Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH Rahman, T., Moulin, K., Ennis, D. B., Perotti, L. E. 2021; 12738: 137-147

    Abstract

    Cardiac diffusion tensor magnetic resonance imaging (cDTI) allows estimating the aggregate cardiomyocyte architecture in healthy subjects and its remodeling as a result of cardiac disease. In this study, cDTI was used to quantify microstructural changes occurring in swine (N=7) six to ten weeks after myocardial infarction. Each heart was extracted and imaged ex vivo with 1mm isotropic spatial resolution. Microstructural changes were quantified in the border zone and infarct region by comparing diffusion tensor invariants - fractional anisotropy (FA), mode, and mean diffusivity (MD) - radial diffusivity, and diffusion tensor eigenvalues with the corresponding values in the remote myocardium. MD and radial diffusivity increased in the infarct and border regions with respect to the remote myocardium (p<0.01). In contrast, FA and mode decreased in the infarct and border regions (p<0.01). Diffusion tensor eigenvalues also increased in the infarct and border regions, with a larger increase in the secondary and tertiary eigenvalues.

    View details for DOI 10.1007/978-3-030-78710-3_14

    View details for PubMedID 34585174

  • Arbitrary Point Tracking with Machine Learning to Measure Cardiac Strains in Tagged MRI. Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH Loecher, M., Hannum, A. J., Perotti, L. E., Ennis, D. B. 2021; 12738: 213-222

    Abstract

    Cardiac tagged MR images allow for deformation fields to be measured in the heart by tracking the motion of tag lines throughout the cardiac cycle. Machine learning (ML) algorithms enable accurate and robust tracking of tag lines. Herein, the use of a massive synthetic physics-driven training dataset with known ground truth was used to train an ML network to enable tracking any number of points at arbitrary positions rather than anchored to the tag lines themselves. The tag tracking and strain calculation methods were investigated in a computational deforming cardiac phantom with known (ground truth) strain values. This enabled both tag tracking and strain accuracy to be characterized for a range of image acquisition and tag tracking parameters. The methods were also tested on in vivo volunteer data. Median tracking error was <0.26mm in the computational phantom, and strain measurements were improved in vivo when using the arbitrary point tracking for a standard clinical protocol.

    View details for DOI 10.1007/978-3-030-78710-3_21

    View details for PubMedID 34590079

  • Right Ventricular Function and T1-Mapping in Boys With Duchenne Muscular Dystrophy. Journal of magnetic resonance imaging : JMRI Dual, S. A., Maforo, N. G., McElhinney, D. B., Prosper, A., Wu, H. H., Maskatia, S., Renella, P., Halnon, N., Ennis, D. B. 2021

    Abstract

    BACKGROUND: Clinical management of boys with Duchenne muscular dystrophy (DMD) relies on in-depth understanding of cardiac involvement, but right ventricular (RV) structural and functional remodeling remains understudied.PURPOSE: To evaluate several analysis methods and identify the most reliable one to measure RV pre- and postcontrast T1 (RV-T1) and to characterize myocardial remodeling in the RV of boys with DMD.STUDY TYPE: Prospective.POPULATION: Boys with DMD (N=27) and age-/sex-matched healthy controls (N=17) from two sites.FIELD STRENGTH/SEQUENCE: 3.0T using balanced steady state free precession, motion-corrected phase sensitive inversion recovery and modified Look-Locker inversion recovery sequences.ASSESSMENT: Biventricular mass (Mi), end-diastolic volume (EDVi) and ejection fraction (EF) assessment, tricuspid annular excursion (TAE), late gadolinium enhancement (LGE), pre- and postcontrast myocardial T1 maps. The RV-T1 reliability was assessed by three observers in four different RV regions of interest (ROI) using intraclass correlation (ICC).STATISTICAL TESTS: The Wilcoxon rank sum test was used to compare RV-T1 differences between DMD boys with negative LGE(-) or positive LGE(+) and healthy controls. Additionally, correlation of precontrast RV-T1 with functional measures was performed. A P-value <0.05 was considered statistically significant.RESULTS: A 1-pixel thick RV circumferential ROI proved most reliable (ICC>0.91) for assessing RV-T1. Precontrast RV-T1 was significantly higher in boys with DMD compared to controls. Both LGE(-) and LGE(+) boys had significantly elevated precontrast RV-T1 compared to controls (1543 [1489-1597] msec and 1550 [1402-1699] msec vs. 1436 [1399-1473] msec, respectively). Compared to healthy controls, boys with DMD had preserved RVEF (51.8 [9.9]% vs. 54.2 [7.2]%, P=0.31) and significantly reduced RVMi (29.8 [9.7]g vs. 48.0 [15.7]g), RVEDVi (69.8 [29.7]mL/m2 vs. 89.1 [21.9]mL/m2 ), and TAE (22.0 [3.2]cm vs. 26.0 [4.7]cm). Significant correlations were found between precontrast RV-T1 and RVEF (beta=-0.48%/msec) and between LV-T1 and LVEF (beta=-0.51%/msec).DATA CONCLUSION: Precontrast RV-T1 is elevated in boys with DMD compared to healthy controls and is negatively correlated with RVEF.LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

    View details for DOI 10.1002/jmri.27729

    View details for PubMedID 34037289

  • Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping. Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance Ghadimi, S., Auger, D. A., Feng, X., Sun, C., Meyer, C. H., Bilchick, K. C., Cao, J. J., Scott, A. D., Oshinski, J. N., Ennis, D. B., Epstein, F. H. 2021; 23 (1): 20

    Abstract

    BACKGROUND: Cardiovascular magnetic resonance (CMR) cine displacement encoding with stimulated echoes (DENSE) measures heart motion by encoding myocardial displacement into the signal phase, facilitating high accuracy and reproducibility of global and segmental myocardial strain and providing benefits in clinical performance. While conventional methods for strain analysis of DENSE images are faster than those for myocardial tagging, they still require manual user assistance. The present study developed and evaluated deep learning methods for fully-automatic DENSE strain analysis.METHODS: Convolutional neural networks (CNNs) were developed and trained to (a) identify the left-ventricular (LV) epicardial and endocardial borders, (b) identify the anterior right-ventricular (RV)-LV insertion point, and (c) perform phase unwrapping. Subsequent conventional automatic steps were employed to compute strain. The networks were trained using 12,415 short-axis DENSE images from 45 healthy subjects and 19 heart disease patients and were tested using 10,510 images from 25 healthy subjects and 19 patients. Each individual CNN was evaluated, and the end-to-end fully-automatic deep learning pipeline was compared to conventional user-assisted DENSE analysis using linear correlation and Bland Altman analysis of circumferential strain.RESULTS: LV myocardial segmentation U-Nets achieved a DICE similarity coefficient of 0.87±0.04, a Hausdorff distance of 2.7±1.0 pixels, and a mean surface distance of 0.41±0.29 pixels in comparison with manual LV myocardial segmentation by an expert. The anterior RV-LV insertion point was detected within 1.38±0.9 pixels compared to manually annotated data. The phase-unwrapping U-Net had similar or lower mean squared error vs. ground-truth data compared to the conventional path-following method for images with typical signal-to-noise ratio (SNR) or low SNR (p<0.05), respectively. Bland-Altman analyses showed biases of 0.00±0.03 and limits of agreement of -0.04 to 0.05 or better for deep learning-based fully-automatic global and segmental end-systolic circumferential strain vs. conventional user-assisted methods.CONCLUSIONS: Deep learning enables fully-automatic global and segmental circumferential strain analysis of DENSE CMR providing excellent agreement with conventional user-assisted methods. Deep learning-based automatic strain analysis may facilitate greater clinical use of DENSE for the quantification of global and segmental strain in patients with cardiac disease.

    View details for DOI 10.1186/s12968-021-00712-9

    View details for PubMedID 33691739

  • Myofiber strain in healthy humans using DENSE and cDTI. Magnetic resonance in medicine Moulin, K., Croisille, P., Viallon, M., Verzhbinsky, I. A., Perotti, L. E., Ennis, D. B. 2021

    Abstract

    PURPOSE: Myofiber strain, Eff , is a mechanistically relevant metric of cardiac cell shortening and is expected to be spatially uniform in healthy populations, making it a prime candidate for the evaluation of local cardiomyocyte contractility. In this study, a new, efficient pipeline was proposed to combine microstructural cDTI and functional DENSE data in order to estimate Eff in vivo.METHODS: Thirty healthy volunteers were scanned with three long-axis (LA) and three short-axis (SA) DENSE slices using 2D displacement encoding and one SA slice of cDTI. The total acquisition time was 11 minutes ± 3 minutes across volunteers. The pipeline first generates 3D SA displacements from all DENSE slices which are then combined with cDTI data to generate a cine of myofiber orientations and compute Eff . The precision of the post-processing pipeline was assessed using a computational phantom study. Transmural myofiber strain was compared to circumferential strain, Ecc , in healthy volunteers using a Wilcoxon sign rank test.RESULTS: In vivo, computed Eff was found uniform transmurally compared to Ecc (-0.14[-0.15, -0.12] vs -0.18 [-0.20, -0.16], P < .001, -0.14 [-0.16, -0.12] vs -0.16 [-0.17, -0.13], P < .001 and -0.14 [-0.16, -0.12] vs Ecc_C = -0.14 [-0.15, -0.11], P = .002, Eff_C vs Ecc_C in the endo, mid, and epi layers, respectively).CONCLUSION: We demonstrate that it is possible to measure in vivo myofiber strain in a healthy human population in 10 minutes per subject. Myofiber strain was observed to be spatially uniform in healthy volunteers making it a potential biomarker for the evaluation of local cardiomyocyte contractility in assessing cardiovascular dysfunction.

    View details for DOI 10.1002/mrm.28724

    View details for PubMedID 33619807