Namer, I. J. | bioRxiv Science

bioRxiv2025

OT-Fusion: Cortex-conditioned Fusion of Matched PET and MRI Images Using Optimal Transport

Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) are crucial in diagnosing various medical conditions related to brain. FDG-PET provides essential functional information by capturing synaptic transmission activity, while MRI offers anatomical details. The integration of these modalities has become increasingly valuable, especially with the advent of hybrid PET/MRI systems that enable acquisition of both images in a single session. However, a notable challenge arises due to the inherent differences in spatial resolution between PET and MRI. PET images often have lower spatial resolution, leading to metabolic signals from active regions, such as the cerebral cortex and basal ganglia, appearing diffused and extended into adjacent areas like the white matter. This diffusion can complicate the accurate localization of metabolic activity, posing difficulties in clinical assessments, particularly in neurological pathologies such as epilepsy where precise mapping is crucial. To address this issue, we propose a novel approach that effectively maps the metabolic activity observed in PET onto the cortical structures delineated in MRI. We formulate this mapping as an optimal transport problem that corresponds to finding the optimum way to transform one probability distribution into another. Our method aims to enhance the structural quality of PET images, making them more detailed and informative for clinical decisions related to neurological disorders. The experiments using paired PET and MRI images indicate that our method performs better than the state-of-the-art image fusion methods on quantitative metrics and improves image quality based on visual qualitative evaluation by an expert physician.

bioinformatics↗

bioRxiv2022

Pathway-informed deep learning model for survivalanalysis and pathological classification of gliomas

Online assessment of tumor characteristics during surgery is important and has the potential to establish an intraoperative surgeon feedback mechanism. With the availability of such feedback, surgeons could decide to be more liberal or conservative regarding the resection of the tumor. While there are methods to perform metabolomics-based online tumor pathology prediction, their model complexity and, in turn, the predictive performance is limited by the small dataset sizes. Furthermore, the information conveyed by the feedback provided on the tumor tissue could be improved both in terms of content and accuracy. In this study, we propose a metabolic pathway-informed deep learning model, PiDeeL, to perform survival analysis and pathology assessment based on metabolite concentrations. We show that incorporating pathway information into the model architecture substantially reduces parameter complexity and achieves better survival analysis and pathological classification performance. With these design decisions, we show that PiDeeL improves tumor pathology prediction performance of the state-of-the-art in terms of the Area Under the ROC Curve (AUC-ROC) by 3.38% and the Area Under the Precision-Recall Curve (AUC-PR) by 4.06%. Similarly, with respect to the time-dependent concordance index (c-index), we observe that PiDeeL achieves better survival analysis performance (improvement up to 4.3%) when compared to the state-of-the-art. Moreover, we show that importance analyses performed on input metabolite features as well as pathway-specific hidden-layer neurons of PiDeeL provide insights into tumor metabolism. We foresee that the use of this model in the surgery room will help surgeons adjust the surgery plan on the fly and will result in better prognosis estimates tailored to surgical procedures. AvailabilityThe code is released at https://github.com/ciceklab/PiDeeL. The data used in this study is released at https://zenodo.org/record/7228791. Contactcicek@cs.bilkent.edu.tr Supplementary informationSupplementary data are available at Briefings in Bioinformatics online.

bioinformatics↗