The aim of the task force is to write systematic reviews of software algorithms for specific image processing tasks.
Deliverable 1: review paper on renal image registration
Frank G. Zöllner, Amira Šerifović‑Trbalić, Gordian Kabelitz, Marek Kociński, Andrzej Materka and Peter Rogelj
Image registration in dynamic renal MRI—current status and prospects
Magnetic Resonance Materials in Physics, Biology and Medicine, https://doi.org/10.1007/s10334-019-00782-y
Abstract. Magnetic resonance imaging (MRI) modalities have achieved an increasingly important role in the clinical work-up of chronic kidney diseases (CKD). This comprises among others assessment of hemodynamic parameters by arterial spin labeling (ASL) or dynamic contrast-enhanced (DCE-) MRI. Especially in the latter, images or volumes of the kidney are acquired over time for up to several minutes. Therefore, they are hampered by motion, e.g., by pulsation, peristaltic, or breathing motion. This motion can hinder subsequent image analysis to estimate hemodynamic parameters like renal blood flow or glomerular filtration rate (GFR). To overcome motion artifacts in time-resolved renal MRI, a wide range of strategies have been proposed. Renal image registration approaches could be grouped into (1) image acquisition techniques, (2) post-processing methods, or (3) a combination of image acquisition and post-processing approaches. Despite decades of progress, the translation in clinical practice is still missing. The aim of the present article is to discuss the existing literature on renal image registration techniques and show today’s limitations of the proposed techniques that hinder clinical translation. This paper includes transformation, criterion function, and search types as traditional components and emerging registration technologies based on deep learning. The current trend points towards faster registrations and more accurate results. However, a standardized evaluation of image registration in renal MRI is still missing.
Deliverable 2: review on renal image segmentation
This paper is still under work and will likely be submitted Q1 2021.