Through his doctoral and postdoctoral studies Dr Wilson has focused on understanding and modeling the pathophysiology of cardiovascular disease, particularly the microstructural and biomechanical changes that underpin cardiac remodeling. Dr Wilson completed his PhD in bioengineering/physiology at the University of Auckland (New Zealand), and has held postdoctoral positions at the University of South Florida Heart Institute (2018-2019) and Stanford University Department of Radiology (2019-present).

Dr Wilson is currently a member of the Cardiac MRI Research Group at Stanford University (PI: Professor Daniel Ennis), and works on a range of projects including (i) using tissue clearing techniques to understand the fundamental branching structure of the myocardium (ii) developing new diffusion tensor MRI reconstruction techniques for extracting cardiac microstructure and pathology (iii) using MRI and histology to understand the myocardial structural and functional improvements brought about by ACE inhibitor treatment.

Dr Wilson’s research interests include:
●Cardiac mesostructure and mesofunction (doi: 10.1152/ajpheart.00059.2022)
●Perfused ex vivo human heart preparations
●Diffusion tensor imaging
●Cardiomyocyte connectivity (doi: 10.1007/978-3-030-78710-3)
●Ventricular torsion
●Machine learning techniques for cardiac MRI (doi: 10.3390/bioengineering10020166)
●Machine learning techniques for myocardial histology (doi: 10.13140/RG.2.2.17606.34883)
●Analysis of collagen structure (doi: 10.1161/res.129.suppl_1.P377)
●Assessment of diastolic function (doi: 10.13140/RG.2.2.11415.50081)

Honors & Awards

  • First Prize, AIMI-HIAE COVID-19 Researchathon, Stanford University (2020)
  • Finalist, John Hubbard Memorial Prize in recognition of excellence in studies towards a PhD, New Zealand Medical Sciences Congress (2017)
  • Travel Fellowship, World Congress of Biomechanics (2014)
  • First Class Honors, Master of Operations Research, University of Auckland (2012)
  • First Prize, John Carman Prize for best oral presentation by a graduate student, New Zealand Medical Sciences Congress (2012)
  • Distinction in Theoretical Statistics, University of Auckland (2009)
  • Merit, Postgraduate Diploma in Science (Medical Sciences), University of Auckland (2009)

Boards, Advisory Committees, Professional Organizations

  • Member, Society for Cardiovascular Magnetic Resonance (2020 - Present)
  • Member, International Society for Magnetic Resonance in Medicine (2020 - Present)
  • Trainee Committee Member, Functional Imaging and Modeling of the Heart (2020 - 2021)
  • Organization Committee Member, 2020 Radiological Sciences Laboratory Retreat, Stanford University (2020 - 2020)
  • Member, American Heart Association (2019 - Present)

Stanford Advisors

Lab Affiliations

All Publications

  • Myocardial Segmentation of Tagged Magnetic Resonance Images with Transfer Learning Using Generative Cine-To-Tagged Dataset Transformation. Bioengineering (Basel, Switzerland) Dhaene, A. P., Loecher, M., Wilson, A. J., Ennis, D. B. 2023; 10 (2)


    The use of deep learning (DL) segmentation in cardiac MRI has the potential to streamline the radiology workflow, particularly for the measurement of myocardial strain. Recent efforts in DL motion tracking models have drastically reduced the time needed to measure the heart's displacement field and the subsequent myocardial strain estimation. However, the selection of initial myocardial reference points is not automated and still requires manual input from domain experts. Segmentation of the myocardium is a key step for initializing reference points. While high-performing myocardial segmentation models exist for cine images, this is not the case for tagged images. In this work, we developed and compared two novel DL models (nnU-net and Segmentation ResNet VAE) for the segmentation of myocardium from tagged CMR images. We implemented two methods to transform cardiac cine images into tagged images, allowing us to leverage large public annotated cine datasets. The cine-to-tagged methods included (i) a novel physics-driven transformation model, and (ii) a generative adversarial network (GAN) style transfer model. We show that pretrained models perform better (+2.8 Dice coefficient percentage points) and converge faster (6×) than models trained from scratch. The best-performing method relies on a pretraining with an unpaired, unlabeled, and structure-preserving generative model trained to transform cine images into their tagged-appearing equivalents. Our state-of-the-art myocardium segmentation network reached a Dice coefficient of 0.828 and 95th percentile Hausdorff distance of 4.745 mm on a held-out test set. This performance is comparable to existing state-of-the-art segmentation networks for cine images.

    View details for DOI 10.3390/bioengineering10020166

    View details for PubMedID 36829660

  • Myocardial Mesostructure and Mesofunction. American journal of physiology. Heart and circulatory physiology Wilson, A. J., Sands, G. B., LeGrice, I. J., Young, A. A., Ennis, D. B. 2022


    The complex and highly organized structural arrangement of some five billion cardiomyocytes directs the coordinated electrical activity and mechanical contraction of the human heart. The characteristic transmural change in cardiomyocyte orientation underlies base-to-apex shortening, circumferential shortening, and left ventricular torsion during contraction. Individual cardiomyocytes shorten approximately 15% and increase in diameter approximately 8%. Remarkably, however, the left ventricular wall thickens by up to 30-40%. To accommodate this, the myocardium must undergo significant structural rearrangement during contraction. At the mesoscale, collections of cardiomyocytes are organized into sheetlets, and sheetlet shear is the fundamental mechanism of rearrangement that produces wall thickening. Herein we review the histological and physiological studies of myocardial mesostructure that have established the sheetlet shear model of wall thickening. Recent developments in tissue clearing techniques allow for imaging of whole hearts at the cellular scale, while magnetic resonance imaging (MRI) and computed tomography (CT) can image the myocardium at the mesoscale (tens to hundreds of microns) to resolve cardiomyocyte orientation and organization. Through histology, cardiac diffusion tensor imaging (cDTI) and other modalities, mesostructural sheetlets have been confirmed in both animal and human hearts. Recent in vivo cDTI methods have measured reorientation of sheetlets during the cardiac cycle. We also examine the role of pathological cardiac remodeling on sheetlet organization and reorientation, and the impact this has on ventricular function and dysfunction. We also review the unresolved mesostructural questions and challenges that may direct future work in the field.

    View details for DOI 10.1152/ajpheart.00059.2022

    View details for PubMedID 35657613

  • Formulation and Characterization of Antithrombin Perfluorocarbon Nanoparticles. Methods in molecular biology (Clifton, N.J.) Wilson, A. J., Zhou, Q., Vargas, I., Palekar, R., Grabau, R., Pan, H., Wickline, S. A. 2020; 2118: 111-120


    Thrombin, a major protein involved in the clotting cascade by the conversion of inactive fibrinogen to fibrin, plays a crucial role in the development of thrombosis. Antithrombin nanoparticles enable site-specific anticoagulation without increasing bleeding risk. Here we outline the process of making and the characterization of bivalirudin and D-phenylalanyl-L-prolyl-L-arginyl-chloromethyl ketone (PPACK) nanoparticles. Additionally, the characterization of these nanoparticles, including particle size, zeta potential, and quantification of PPACK/bivalirudin loading, is also described.

    View details for DOI 10.1007/978-1-0716-0319-2_8

    View details for PubMedID 32152974

  • Myocardial Laminar Organization Is Retained in Angiotensin-Converting Enzyme Inhibitor Treated SHRs Experimental Mechanics Wilson, A. J., Sands, G. B., Wang, V. Y., Hasaballa, A. I., Pontre, B., Young, A. A., Nash, M. P., LeGrice, I. J. 2020
  • Microstructurally Motivated Constitutive Modeling of Heart Failure Mechanics. Biophysical journal Hasaballa, A. I., Wang, V. Y., Sands, G. B., Wilson, A. J., Young, A. A., LeGrice, I. J., Nash, M. P. 2019


    Heart failure (HF) is one of the leading causes of death worldwide. HF is associated with substantial microstructural remodeling, which is linked to changes in left ventricular geometry and impaired cardiac function. The role of myocardial remodeling in altering the mechanics of failing hearts remains unclear. Structurally based constitutive modeling provides an approach to improve understanding of the relationship between biomechanical function and tissue organization in cardiac muscle during HF. In this study, we used cardiac magnetic resonance imaging and extended-volume confocal microscopy to quantify the remodeling of left ventricular geometry and myocardial microstructure of healthy and spontaneously hypertensive rat hearts at the ages of 12 and 24months. Passive cardiac mechanical function was characterized using left ventricular pressure-volume compliance measurements. We have developed a, to our knowledge, new structurally based biomechanical constitutive equation built on parameters quantified directly from collagen distributions observed in confocal images of the myocardium. Three-dimensional left ventricular finite element models were constructed from subject-specific invivo magnetic resonance imaging data. The structurally based constitutive equation was integrated into geometrically subject-specific finite element models of the hearts and used to investigate the underlying mechanisms of ventricular dysfunction during HF. Using a single pair of material parameters for all hearts, we were able to produce compliance curves that reproduced all of the experimental compliance measurements. The value of this study is not limited to reproducing the mechanical behavior of healthy and diseased hearts, but it also provides important insights into the structure-function relationship of diseased myocardium that will help pave the way toward more effective treatments for HF.

    View details for DOI 10.1016/j.bpj.2019.09.038

    View details for PubMedID 31653449

  • Increased cardiac work provides a link between systemic hypertension and heart failure PHYSIOLOGICAL REPORTS Wilson, A. J., Wang, V. Y., Sands, G. B., Young, A. A., Nash, M. P., LeGrice, I. J. 2017; 5 (1)


    The spontaneously hypertensive rat (SHR) is an established model of human hypertensive heart disease transitioning into heart failure. The study of the progression to heart failure in these animals has been limited by the lack of longitudinal data. We used MRI to quantify left ventricular mass, volume, and cardiac work in SHRs at age 3 to 21 month and compared these indices to data from Wistar-Kyoto (WKY) controls. SHR had lower ejection fraction compared with WKY at all ages, but there was no difference in cardiac output at any age. At 21 month the SHR had significantly elevated stroke work (51 ± 3 mL.mmHg SHR vs. 24 ± 2 mL.mmHg WKY; n = 8, 4; P < 0.001) and cardiac minute work (14.2 ± 1.2 L.mmHg/min SHR vs. 6.2 ± 0.8 L.mmHg/min WKY; n = 8, 4; P < 0.001) compared to control, in addition to significantly larger left ventricular mass to body mass ratio (3.61 ± 0.15 mg/g SHR vs. 2.11 ± 0.008 mg/g WKY; n = 8, 6; P < 0.001). SHRs showed impaired systolic function, but developed hypertrophy to compensate and successfully maintained cardiac output. However, this was associated with an increase in cardiac work at age 21 month, which has previously demonstrated fibrosis and cell death. The interplay between these factors may be the mechanism for progression to failure in this animal model.

    View details for DOI 10.14814/phy2.13104

    View details for Web of Science ID 000392243200001

    View details for PubMedID 28082430

    View details for PubMedCentralID PMC5256162

  • Three-Dimensional Quantification of Myocardial Collagen Morphology from Confocal Images Hasaballa, A. I., Sands, G. B., Wilson, A. J., Young, A. A., Wang, V. Y., LeGrice, I. J., Nash, M. P., Pop, M., Wright, G. A. SPRINGER INTERNATIONAL PUBLISHING AG. 2017: 3–12
  • Image-driven constitutive modeling of myocardial fibrosis INTERNATIONAL JOURNAL FOR COMPUTATIONAL METHODS IN ENGINEERING SCIENCE & MECHANICS Wang, V. Y., Niestrawska, J. A., Wilson, A. J., Sands, G. B., Young, A. A., LeGrice, I. J., Nash, M. P. 2016; 17 (3): 211–21
  • Microstructural Remodelling and Mechanics of Hypertensive Heart Disease Wang, V. Y., Wilson, A. J., Sands, G. B., Young, A. A., LeGrice, I. J., Nash, M. P., VanAssen, H., Bovendeerd, P., Delhaas, T. SPRINGER-VERLAG BERLIN. 2015: 382–89
  • Field-Based Parameterisation of Cardiac Muscle Structure from Diffusion Tensors Freytag, B., Wang, V. Y., Christie, G., Wilson, A. J., Sands, G. B., LeGrice, I. J., Young, A. A., Nash, M. P., VanAssen, H., Bovendeerd, P., Delhaas, T. SPRINGER-VERLAG BERLIN. 2015: 146–54