bibtext: @InProceedings{ Lee_ADeep_MICCAI2024,
                   author = { Lee, Sangyoon and Branzoli, Francesca and Nguyen, Thanh and Andronesi, Ovidiu and Lin, Alexander and Liserre, Roberto and Melkus, Gerd and Chen, Clark and Marjańska, Małgorzata and Bolan, Patrick J. },
                   title = { { A Deep Learning Approach for Placing Magnetic Resonance Spectroscopy Voxels in Brain Tumors } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15003 },
                   month = {October},
                   pages = { pending },
                }
abstract: Magnetic resonance spectroscopy (MRS) of brain tumors provides useful metabolic information for diagnosis, treatment response, and prognosis. Single-voxel MRS requires precise planning of the acquisition volume to produce a high-quality signal localized in the pathology of interest. Appropriate placement of the voxel in a brain tumor is determined by the size and morphology of the tumor, and is guided by MR imaging. Consistent placement of a voxel precisely within a tumor requires substantial expertise in neuroimaging interpretation and MRS methodology. The need for such expertise at the time of scan has contributed to low usage of MRS in clinical practice. In this study, we propose a deep learning method to perform voxel placements in brain tumors. The network is trained in a supervised fashion using a database of voxel placements performed by MRS experts. Our proposed method accurately replicates the voxel placements of experts in tumors with comparable tumor coverage, voxel volume, and voxel position to that of experts. This novel deep learning method can be easily applied without an extensive external validation as it only requires a segmented tumor mask as input.
title: A Deep Learning Approach for Placing Magnetic Resonance Spectroscopy Voxels in Brain Tumors
authors: Lee, Sangyoon Branzoli, Francesca Nguyen, Thanh Andronesi, Ovidiu Lin, Alexander Liserre, Roberto Melkus, Gerd Chen, Clark Marjańska, Małgorzata Bolan, Patrick J.