Speaker
Description
Objective
Upright radiotherapy may offer anatomical and infrastructural advantages, but its implementation requires treatment-planning images in the upright posture. Upright MRI remains rarely available, and gravity-induced displacement and deformation of pelvic targets and organs at risk are poorly characterised. This study quantifies supine-to-upright pelvic deformation and evaluates whether anatomy in the upright posture can be predicted from a single supine acquisition.
Methods
Paired 3D T1-weighted supine and standing MRI were acquired in ten healthy volunteers (five male, five female) using a 0.6T open-magnet system. Pelvic bones and five pelvic organs were manually segmented in both postures. After rigid skeletal prealignment using trimmed iterative closest point registration on dense bone surfaces, a multi-metric deformable registration combining a B-spline model with discrete Markov random field optimisation estimated dense supine-to-upright correspondences. Organ displacement and local deformation were quantified using organ-wise displacement and principal stretch. In a leave-one-out experiment, a VAE--nnU-Net was trained to predict the upright anatomy from the supine image alone and compared with a cohort-mean baseline.
Results
Registration accuracy was structure-dependent, with the highest Dice similarity coefficients (DSC) for the prostate ($0.79 \pm 0.05$) and bladder ($0.78 \pm 0.18$), and the lowest for the colon ($0.41 \pm 0.18$) and small bowel ($0.49 \pm 0.22$). All organs shifted caudally, with mean displacements ranging from $-13.1$ mm for the small bowel to $-4.7$ mm for the rectum. Bowel and colon shifted anteriorly, whereas the rectum and prostate shifted posteriorly. Maximum principal stretch ranged from $1.46$ in the small bowel to $1.15$ in the prostate. Bowel displacement should be interpreted as indicative because differences in delineated extent between acquisitions limit its quantitative reliability. Supine-only prediction achieved a four-organ DSC of $0.47$ versus $0.46$ for the cohort-mean baseline, indicating predominantly population-average rather than patient-specific prediction.
Outlook & Discussion
The deformable registration provides quantitative characterisation of posture-induced deformation but is not rigid within cortical bone, producing non-physical strain and potentially biasing soft-tissue measurements and supervision. More fundamentally, paired imaging is scarce, limiting purely data-driven prediction and providing no supervision for postures that have not been acquired. We are therefore developing a physics-informed biomechanical simulation based on physics-informed neural networks (PINNs), combining patient-specific anatomy with continuum mechanics, gravity, and anatomical interactions with the environment. The aim is to predict physically constrained, patient-specific deformation from a single supine acquisition and ultimately synthesise treatment-planning anatomy in previously un-imaged postures, including upright configurations.