Magnetic resonance angiography with compressed sensing: An evaluation of moyamoya disease

PLoS One. 2018 Jan 19;13(1):e0189493. doi: 10.1371/journal.pone.0189493. eCollection 2018.

Abstract

Compressed sensing (CS) reconstructions of under-sampled measurements generate missing data based on assumptions of image sparsity. Non-contrast time-of-flight MR angiography (TOF-MRA) is a good candidate for CS based acceleration, as MRA images feature bright trees of sparse vessels over a well-suppressed anatomical background signal. A short scan time derived from CS is beneficial for patients of moyamoya disease (MMD) because of the frequency of MR scans. The purpose of this study was to investigate the reliability of TOF-MRA with CS in the evaluation of MMD. Twenty-two patients were examined using TOF-MRA with CS (CS-TOF) and parallel imaging (PI-TOF). The acceleration factors were 3 (CS3) and 5 (CS5) for CS-TOF, and 3 (PI3) for PI-TOF. Two neuroradiologists evaluated the MMD grading according to stenosis/occlusion scores using the modified Houkin's system, and the visibility of moyamoya vessels (MMVs) using a 3-point scale. Concordance was calculated with Cohen's κ. The numbers of MMVs in the basal ganglia were compared using Bland-Altman analysis and Wilcoxon's signed-rank tests. MRA scan times were 4:07, 3:53, and 2:42 for PI3, CS3, and CS5, respectively. CS-reconstruction completed within 10 minutes. MMD grading and MMV visibility scales showed excellent correlation (κ > .966). Although the number of MMVs was significantly higher in CS3 than in PI3 (p < .0001) and CS5 (p < .0001), Bland-Altman analysis showed a good agreement between PI3, CS3, and CS5. Compressed sensing can accelerate TOF-MRA with improved visualization of small collaterals in equivalent time (CS3) or equivalent results in a shorter scan time (CS5).

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Female
  • Humans
  • Magnetic Resonance Angiography / methods*
  • Male
  • Middle Aged
  • Moyamoya Disease / diagnosis*

Grants and funding

This work was supported by a Grant-in-Aid for Scientific Research on Innovative Areas “Initiative for High-Dimensional Data-Driven Science through Deepening of Sparse Modeling” of The Ministry of Education, Culture, Sports, Science and Technology, Japan was provided to K.T. (MEXT grant number 25120002) (http://sparse-modeling.jp/). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.