Project

RESPECT - Renal MRI standardization to improve personalised management of CKD patients

Goal: 1) to develop a standardised physiologically relevant multi-parametric renal MRI protocol for personalised CKD management, 2) to harmonise across MRI vendors and technically validate the standardised renal MRI protocol, 3) to set up an open-access cloud-based platform for renal MRI data sharing, quality control and processing, 4) to develop novel artificial intelligence (AI) techniques to automate renal MRI processing, 5) to provide preliminary cross-institutional evidence of renal MRI feasibility and utility in characterising and staging CKD, 6) to develop MRI data sharing guidelines ensuring ethics and confidentiality, and assess patient, health care and ethic professionals’ perspective on data sharing and the use of AI in image data processing.

Date: 1 June 2021 - 31 May 2024

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Project log

Frank G Zöllner
added a research item
Accurate quantification of perfusion is crucial for diagnosis and monitoring of kidney function. Arterial spin labeling (ASL), a completely non-invasive magnetic resonance imaging technique, is a promising method for this application. However, differences in acquisition (e.g., ASL parameters, readout) and processing (e.g., registration, segmentation) between studies impede the comparison of results. To alleviate challenges arising solely from differences in processing pipelines, synthetic data are of great value. In this work, synthetic renal ASL data were generated using body models from the XCAT phantom and perfusion was added using the general kinetic model. Our in-house developed processing pipeline was then evaluated in terms of registration, quantification, and segmentation using the synthetic data. Registration performance was evaluated qualitatively with line profiles and quantitatively with mean structural similarity index measures (MSSIMs). Perfusion values obtained from the pipeline were compared to the values assumed when generating the synthetic data. Segmentation masks obtained by semi-automated procedure of the processing pipeline were compared to the original XCAT organ masks using the Dice index. Overall, the pipeline evaluation yielded good results. After registration, line profiles were smoother and, on average, MSSIMs increased by 25%. Mean perfusion values for cortex and medulla were close to the assumed perfusion of 250 mL/100 g/min and 50 mL/100 g/min, respectively. Dice indices ranged 0.80–0.93, 0.78–0.89, and 0.64–0.84 for whole kidney, cortex, and medulla, respectively. The generation of synthetic ASL data allows flexible choice of parameters and the generated data are well suited for evaluation of processing pipelines.
Frank G Zöllner
added a research item
Early detection of the autosomal dominant polycystic kidney disease (ADPKD) is crucial as it is one of the most common causes of end-stage renal disease (ESRD) and kidney failure. The total kidney volume (TKV) can be used as a biomarker to quantify disease progression. The TKV calculation requires accurate delineation of kidney volumes, which is usually performed manually by an expert physician. However, this is time-consuming and automated segmentation is warranted. Furthermore, the scarcity of large annotated datasets hinders the development of deep learning solutions. In this work, we address this problem by implementing three attention mechanisms into the U-Net to improve TKV estimation. Additionally, we implement a cosine loss function that works well on image classification tasks with small datasets. Lastly, we apply a technique called sharpness aware minimization (SAM) that helps improve the generalizability of networks. Our results show significant improvements (p-value < 0.05) over the reference kidney segmentation U-Net. We show that the attention mechanisms and/or the cosine loss with SAM can achieve a dice score (DSC) of 0.918, a mean symmetric surface distance (MSSD) of 1.20 mm with the mean TKV difference of −1.72%, and R2 of 0.96 while using only 100 MRI datasets for training and testing. Furthermore, we tested four ensembles and obtained improvements over the best individual network, achieving a DSC and MSSD of 0.922 and 1.09 mm, respectively.
Anna Caroli
added a project goal
1) to develop a standardised physiologically relevant multi-parametric renal MRI protocol for personalised CKD management, 2) to harmonise across MRI vendors and technically validate the standardised renal MRI protocol, 3) to set up an open-access cloud-based platform for renal MRI data sharing, quality control and processing, 4) to develop novel artificial intelligence (AI) techniques to automate renal MRI processing, 5) to provide preliminary cross-institutional evidence of renal MRI feasibility and utility in characterising and staging CKD, 6) to develop MRI data sharing guidelines ensuring ethics and confidentiality, and assess patient, health care and ethic professionals’ perspective on data sharing and the use of AI in image data processing.