Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization
Authors: Michal Balcerak, Tamaz Amiranashvili, Andreas Wagner, Jonas Weidner, Petr Karnakov, Johannes C. Paetzold, Ivan Ezhov, Petros Koumoutsakos, Benedikt Wiestler, bjoern menze
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate enhanced coverage of tumor recurrence areas using real-world data from a patient cohort, highlighting the potential of our method to improve model-driven treatment planning for glioblastoma in clinical practice. |
| Researcher Affiliation | Academia | 1University of Zurich, 2Technical University of Munich 3Harvard University, 4Imperial College London |
| Pseudocode | No | The paper describes procedures and methods in paragraph text but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | *Code is available at https://github.com/m1balcerak/PhysRegTumor |
| Open Datasets | Yes | We use a publicly available dataset of 58 patients with preoperative MRI scans and preoperative FETPET imaging, along with follow-up MRI scans at the time of the first visible tumor recurrence [22]. |
| Dataset Splits | No | The paper mentions using synthetic data for hyperparameter tuning ('All α parameters were determined based on experiments with synthetic data') and generating '100 synthetic patients' but does not specify exact train/validation/test split percentages or sample counts for this dataset for validation. |
| Hardware Specification | Yes | Using the Adam optimizer, convergence takes around 3 hours on an NVIDIA RTX A6000 GPU. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' but does not specify any software names with version numbers for libraries, frameworks, or programming languages used in the implementation. |
| Experiment Setup | Yes | Our computational model uses a multi-resolution method [37] with additional, coarser grids to accelerate convergence. We employ four levels of grid refinement, resulting in 152,880,048 unknowns. At the finest grid level of 72 x 72 x 72 x 96, each grid point contains four unknowns: three for particle positions and one for tumor cell density. ... Using the Adam optimizer... ...β modulates the steepness of the transition and is set to β = 50. ...α offsets the thresholds and is set to α = 0.05. |