I am a Postdoctoral Scholar at Stanford University with solid expertise on signal processing, image processing, computer vision and machine learning centralized on cardiac magnetic resonance imaging (CMR). Specifically, my PhD dissertation is about advanced image acquisition and reconstruction techniques for first-pass CMR perfusion imaging.

CMR is a medical imaging technology for non-invasive assessment of the function and structure of the cardiovascular system. The development of CMR is an active field of research and continues to see a rapid expansion of new and emerging techniques.

High-resolution Spiral First-pass Myocardial Perfusion Imaging

Coronary artery disease (CAD) is a major public health concern. According to a report from the American Heart Association in 2021, an estimated 18.2 million adult Americans have CAD. CAD is responsible for 1 in every 7 deaths in the United States. Cardiac magnetic resonance (CMR) quantitative myocardial first-pass perfusion imaging is a non-invasive and non-ionizing technique for diagnosing CAD which provides an accurate assessment of myocardial ischemia and a comprehensive evaluation of myocardial function and infarction.

Despite multiple potential advantages of CMR perfusion imaging, current clinically available techniques have limited in-plane spatial resolution (~2-3 mm) and incomplete heart coverage, which impede the assessment of transmural perfusion differences and underestimate the extent of ischemia. With higher spatial resolution, there is an increased ability to detect transmural perfusion differences between the epicardium and the endocardium, which could improve the ability to detecting obstructive CAD.

We aim to develop fast and high-resolution quantitative perfusion imaging techniques with whole-heart coverage. Specifically, we have demonstrated excellent performances utilizing variable density spiral trajectories, parallel imaging, compressed sensing, outer volume suppression (OVS) and simultaneous multi-slice (SMS) techniques.

Related publications:

1. Wang, J, Yang, Y, Weller, DS, et al. High spatial resolution spiral first-pass myocardial perfusion imaging with whole-heart coverage at 3 T. Magn Reson Med. 2021; 86: 648– 662. https://doi.org/10.1002/mrm.28701

2. Wang J, Yang Y, Zhou R, Sun C, Jacob M, Weller DS, Epstein F, Salerno M. High resolution spiral simultaneous multi-slice first-pass perfusion imaging with whole-heart coverage at 1.5 T and 3 T. In Proceedings of the ISMRM 28th Annual Meeting and Exhibition, 2020, p. 1313 (oral presentation, Summa cum Laude Awards).

DEep learning-based rapid Spiral Image REconstruction (DESIRE) for High-resolution Spiral Real-time Cine Imaging

Cardiac magnetic resonance (CMR) real-time cine imaging, which does not require breath-holding or ECG gating, is clinically useful particularly for patients with impaired breath-hold capacity and/or arrhythmias.

Spiral acquisitions, which provide high acquisition efficiency and insensitivity to motion artifacts, can require a long reconstruction time particularly for compressed-sensing or other iterative reconstruction techniques. As such they cannot provide immediate feedback to the imager.

In this work, we sought to develop high-resolution real-time cardiac cine imaging technique using rapid spiral acquisitions and deep learning-based rapid image reconstruction and quantification. We extended the DESIRE technique developed in spiral perfusion imaging to the spiral real-time imaging. The proposed reconstruction technique showed good performance on both bSSFP imaging at 1.5 T and GRE imaging at 1.5 T and 3 T.

Related publications:

1. Wang, J, Weller, DS, Kramer, CM, Salerno, M. DEep learning-based rapid Spiral Image REconstruction (DESIRE) for high-resolution spiral first-pass myocardial perfusion imaging. NMR in Biomedicine. 2022;e4661. doi:10.1002/nbm.4661

2. Wang J, Zhou R, Wang X, Awad M, Salerno M. Free-breathing High-resolution Spiral Real-time Cardiac Cine Imaging at 1.5 T with DEep learning-based Spiral Image REconstruction (DESIRE). In Proceedings of the ISMRM 30th Annual Scientific Sessions, London, England, UK, 2022 (oral Power Pitch).

3. Wang J, Zhou R, Salerno M. Free-breathing High-resolution Spiral Real-time Cardiac Cine Imaging using DEep learning-based rapid Spiral Image REconstruction (DESIRE). In Proceedings of the ISMRM 29th Annual Meeting and Exhibition, 2021, p. 877 (digital poster).

High-resolution Cartesian Perfusion Imaging with Deep learning-based Rapid Image Reconstruction

As Cartesian perfusion techniques are most commonly deployed clinically, we also sought to evaluate the proposed compressed sensing (CS), SMS, and deep learning techniques for Cartesian high-resolution perfusion imaging for both 2D and SMS acquisitions with an MB factor of 2 using a 2D Poisson-Disc incoherent sampling pattern along temporal dimension.

Currently, clinical 2D Cartesian perfusion imaging using CS-based image reconstruction such as L1-SENSE provides fast and high-resolution imaging, but it cannot provide whole-heart coverage. CS-based Cartesian SMS perfusion imaging can provide whole-heart coverage but is limited by slow reconstruction, with both techniques taking ~30 minutes to reconstruct each dynamic series. Motion correction (MOCO) is essential for perfusion imaging such that better qualitative and quantitative analysis can be conducted. However, this is relatively time consuming.

These limitations impede the clinical on-line translation of high-resolution Cartesian perfusion imaging. To address this, we sought to develop a fully automatic deep learning-based (DL) respiratory motion-corrected image reconstruction technique for Cartesian 2D and SMS high-resolution perfusion imaging at 3 T with 1.6 mm in-plane resolution to provide rapid and high-quality reconstruction, advancing the clinical workflow.

Related publications:

1. Wang J, Salerno M. Deep learning-based Rapid Image Reconstruction for High-resolution Cartesian First-pass Myocardial Perfusion Imaging at 3 Tesla. In Proceedings of the SCMR 26th Annual Scientific Sessions, 2023 (rapid fire presentation).