AutoVOI: real-time automatic prescription of volume-of-interest for single voxel spectroscopy

Young Woo Park, Dinesh K. Deelchand, James M. Joers, Brian Hanna, Adam Berrington, Joseph S. Gillen, Kejal Kantarci, Brian J. Soher, Peter B. Barker, Hyunwook Park, Gülin Öz, Christophe Lenglet

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1787-1798
Number of pages12
JournalMagnetic resonance in medicine
Issue number5
StatePublished - Nov 2018

Bibliographical note

Publisher Copyright:
© 2018 International Society for Magnetic Resonance in Medicine


  • MRS
  • automation
  • generalized dice coefficient
  • prescription
  • registration


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