Automated Acquisition Planning for Magnetic Resonance Spectroscopy in Brain Cancer

Patrick J. Bolan, Francesca Branzoli, Anna Luisa Di Stefano, Lucia Nichelli, Romain Valabregue, Sara L. Saunders, Mehmet Akçakaya, Marc Sanson, Stéphane Lehéricy, Małgorzata Marjańska

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In vivo magnetic resonance spectroscopy (MRS) can provide clinically valuable metabolic information from brain tumors that can be used for prognosis and monitoring response to treatment. Unfortunately, this technique has not been widely adopted in clinical practice or even clinical trials due to the difficulty in acquiring and analyzing the data. In this work we propose a computational approach to solve one of the most critical technical challenges: the problem of quickly and accurately positioning an MRS volume of interest (a cuboid voxel) inside a tumor using MR images for guidance. The proposed automated method comprises a convolutional neural network to segment the lesion, followed by a discrete optimization to position an MRS voxel optimally within the lesion. In a retrospective comparison, the novel automated method is shown to provide improved lesion coverage compared to manual voxel placement.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages730-739
Number of pages10
ISBN (Print)9783030597276
DOIs
StatePublished - 2020
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: Oct 4 2020Oct 8 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12267 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/TerritoryPeru
CityLima
Period10/4/2010/8/20

Bibliographical note

Funding Information:
Acknowledgements. This work was supported by NIH grants P41 EB027061, P41 EB015894, and P30 NS076408; Investissements d’avenir ANR-10-IAIHU-06 and ANR-11-INBS-0006; INCa-DGOS-Inserm_12560 (SiRIC CURAMUS)

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

  • Brain cancer
  • Image guided intervention
  • Medical image segmentation

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