Radhouene Neji

TL;DR: A novel 4D (3D + time) deep learning-based reconstruction network, termed 4D CINENet, for prospectively undersampled 3D Cartesian CINE imaging, which outperforms iterative reconstructions in visual image quality and contrast and finds good agreement in LV function.

TL;DR: To enable whole‐heart 3D coronary magnetic resonance angiography (CMRA) with isotropic sub‐millimeter resolution in a clinically feasible scan time by combining respiratory motion correction with highly accelerated variable density sampling in concert with a novel 3D patch‐based undersampled reconstruction (3D‐PROST).

TL;DR: Develop a framework for efficient free‐breathing simultaneous whole‐heart coronary magnetic resonance angiography (CMRA) and cardiac positron emission tomography (PET) on a 3 Tesla PET‐MR system.

TL;DR: Simultaneous-Multi-Slice perfusion imaging has the potential to acquire multiple slices, increasing myocardial coverage without sacrificing in-plane spatial resolution and application of gradient-controlled local Larmor adjustment can ensure robustness against off-resonance artifacts and SNR loss can be mitigated by applying iterative reconstruction with spatial and temporal regularisation.

TL;DR: In this paper, the authors describe the key principles for imaging biomarker development and discuss the current status with respect to magnetic resonance imaging (MRI) with regards to personalized cancer medicine.