Purpose: To develop a fast and automated volume-of-interest (VOI) prescription pipeline (AutoVOI) for single-voxel MRS that removes the need for manual VOI placement, allows flexible VOI planning in any brain region, and enables high inter- and intra-subject consistency of VOI prescription. Methods: AutoVOI was designed to transfer pre-defined VOIs from an atlas to the 3D anatomical data of the subject during the scan. The AutoVOI pipeline was optimized for consistency in VOI placement (precision), enhanced coverage of the targeted tissue (accuracy), and fast computation speed. The tool was evaluated against manual VOI placement using existing T1-weighted data sets and corresponding VOI prescriptions. Finally, it was implemented on 2 scanner platforms to acquire MRS data from clinically relevant VOIs that span the cerebrum, cerebellum, and the brainstem. Results: The AutoVOI pipeline includes skull stripping, non-linear registration of the atlas to the subject's brain, and computation of the VOI coordinates and angulations using a minimum oriented bounding box algorithm. When compared against manual prescription, AutoVOI showed higher intra- and inter-subject spatial consistency, as quantified by generalized Dice coefficients (GDC), lower intra- and inter-subject variability in tissue composition (gray matter, white matter, and cerebrospinal fluid) and higher or equal accuracy, as quantified by GDC of prescribed VOI with targeted tissues. High quality spectra were obtained on Siemens and Philips 3T systems from 6 automatically prescribed VOIs by the tool. Conclusion: Robust automatic VOI prescription is feasible and can help facilitate clinical adoption of MRS by avoiding operator dependence of manual selection.
Bibliographical noteFunding Information:
National Institutes of Health, Grant/ Award Numbers: R01 NS080816, R01 NS070815, P41 EB015894, P30 NS076408, and S10OD017974; Ministry of Science and ICT, Grant/Award Number: NRF 2014M3C7033999; Ministry of Health & Welfare of Republic of Korea, Grant/Award Number: KHIDI HI14C1135; Minnesota Partnership for Biotechnology and Medical Genomics; Grant/Award Number: MNP 12.15
We would like to thank Dr. Petr Bedna??k, Dr. Karim Snoussi and Mr. Ian Cheong for providing data and feedback during our work. National Institutes of Health, Grant/Award Numbers: R01 NS080816, R01 NS070815, P41 EB015894, P30 NS076408, and S10OD017974; Ministry of Science and ICT, Grant/Award Number: NRF 2014M3C7033999; Ministry of Health & Welfare of Republic of Korea, Grant/Award Number: KHIDI HI14C1135; Minnesota Partnership for Biotechnology and Medical Genomics; Grant/Award Number: MNP 12.15.
© 2018 International Society for Magnetic Resonance in Medicine
- generalized dice coefficient