On September 17 2021 at 16.00 Jakub Ceranka will defend his PhD entitled “Advancements in Whole-Body Multi-Modal MRI: Towards Computer-Aided Diagnosis of Metastatic Bone Disease”.

Everybody is invited to attend the online presentation via  this teams link.

Cancer that begins in an organ, such as the lungs, breast or prostate, and then spreads to the bone or other organs, marks the beginning of metastatic disease. The confident detection of metastatic bone disease and the reliable assessment of the tumour load and treatment response is essential to improve patients’ quality of life and increase life expectancy. Magnetic resonance imaging (MRI) has been successfully used for monitoring of metastatic bone disease. Anatomical whole-body sequences offer excellent resolution and sensitivity for the detection of neoplastic cells within the bone marrow. In combination with spatially prealigned functional diffusion-weighted whole-body MRI and apparent diffusion coefficient maps, it allows for focused, efficient, multi-parametric and holistic evaluation of the total tumour volume, diffusion volume and treatment response assessment. One of the major challenges of radiological reading of whole-body MRI in the clinical routine comes from the large amount of data to be reviewed, making lesion detection and quantification demanding for a radiologist, but also prone to error. Additionally, whole-body MR images are often corrupted with multiple spatial and intensity artifacts, which degrade the performance of medical image processing algorithms.

This PhD thesis proposes number of contributions in the medical image processing domain aiming at improving the quality and extending the usability of whole-body multi-modal MRI in the clinical routine. These include spatial groupwise image registration (to align multiple MRI modalities), multi-atlas segmentation (to define the skeleton region of interest), image standardization (to map MRI intensities into comparable ranges) and a deep learning framework for detection and segmentation of metastatic bone disease, as it is pathology of choice for this work. Combined, proposed contributions provide building blocks for a fully automated computer-aided diagnosis (CAD) system for the detection and segmentation of metastatic bone disease using whole-body multi-modal MRI. Finally, an ablation study describing the impact of different CAD system components on detection and segmentation accuracy is provided.