Claudiu V Schirda1, Tiejun Zhao2, Victor E Yushmanov1, Yoojin Lee1, Gena R Ghearing3, Frank S Lieberman3, Ashok Panigrahy4, Hoby P Hetherington1, Jullie W Pan1,3. 1. Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA. 2. Siemens Healthcare, Siemens Medical Solutions USA Inc, Pittsburgh, Pennsylvania, USA. 3. Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA. 4. Department of Pediatric Radiology, Children's Hospital of Pittsburgh, UPMC, Pittsburgh, Pennsylvania, USA.
Abstract
PURPOSE: To use a fast 3D rosette spectroscopic imaging acquisition to quantitatively evaluate how spectral quality influences detection of the endogenous variation of gray and white matter metabolite differences in controls, and demonstrate how rosette spectroscopic imaging can detect metabolic dysfunction in patients with neocortical abnormalities. METHODS: Data were acquired on a 3T MR scanner and 32-channel head coil, with rosette spectroscopic imaging covering a 4-cm slab of fronto-parietal-temporal lobes. The influence of acquisition parameters and filtering on spectral quality and sensitivity to tissue composition was assessed by LCModel analysis, the Cramer-Rao lower bound, and the standard errors from regression analyses. The optimized protocol was used to generate normative white and gray matter regressions and evaluate three patients with neocortical abnormalities. RESULTS: As a measure of the sensitivity to detect abnormalities, the standard errors of regression for Cr/NAA and Ch/NAA were significantly correlated with the Cramer-Rao lower bound values (R = 0.89 and 0.92, respectively, both with P < 0.001). The rosette acquisition with a duration of 9.6 min, produces a mean Cramer-Rao lower bound (%) over the entire slab of 4.6 ± 2.6 and 5.8 ± 2.3 for NAA and Cr, respectively. This enables a Cr/NAA standard error of 0.08 (i.e., detection sensitivity of 25% for a 50/50 mixed gray and white matter voxel). In healthy controls, the regression of Cr/NAA versus fraction gray matter in the cingulate differs from frontal and parietal regions. CONCLUSIONS: Fast rosette spectroscopic imaging acquisitions with regression analyses are able to identify metabolic differences across 4-cm slabs of the brain centrally and over the cortical periphery with high efficiency, generating results that are consistent with clinical findings. Magn Reson Med 79:2470-2480, 2018.
PURPOSE: To use a fast 3D rosette spectroscopic imaging acquisition to quantitatively evaluate how spectral quality influences detection of the endogenous variation of gray and white matter metabolite differences in controls, and demonstrate how rosette spectroscopic imaging can detect metabolic dysfunction in patients with neocortical abnormalities. METHODS: Data were acquired on a 3T MR scanner and 32-channel head coil, with rosette spectroscopic imaging covering a 4-cm slab of fronto-parietal-temporal lobes. The influence of acquisition parameters and filtering on spectral quality and sensitivity to tissue composition was assessed by LCModel analysis, the Cramer-Rao lower bound, and the standard errors from regression analyses. The optimized protocol was used to generate normative white and gray matter regressions and evaluate three patients with neocortical abnormalities. RESULTS: As a measure of the sensitivity to detect abnormalities, the standard errors of regression for Cr/NAA and Ch/NAA were significantly correlated with the Cramer-Rao lower bound values (R = 0.89 and 0.92, respectively, both with P < 0.001). The rosette acquisition with a duration of 9.6 min, produces a mean Cramer-Rao lower bound (%) over the entire slab of 4.6 ± 2.6 and 5.8 ± 2.3 for NAA and Cr, respectively. This enables a Cr/NAA standard error of 0.08 (i.e., detection sensitivity of 25% for a 50/50 mixed gray and white matter voxel). In healthy controls, the regression of Cr/NAA versus fraction gray matter in the cingulate differs from frontal and parietal regions. CONCLUSIONS: Fast rosette spectroscopic imaging acquisitions with regression analyses are able to identify metabolic differences across 4-cm slabs of the brain centrally and over the cortical periphery with high efficiency, generating results that are consistent with clinical findings. Magn Reson Med 79:2470-2480, 2018.
Authors: Kevin M Koch; Scott McIntyre; Terence W Nixon; Douglas L Rothman; Robin A de Graaf Journal: J Magn Reson Date: 2006-03-30 Impact factor: 2.229
Authors: D Wiedermann; N Schuff; G B Matson; B J Soher; A T Du; A A Maudsley; M W Weiner Journal: Magn Reson Imaging Date: 2001-10 Impact factor: 2.546
Authors: Matthew L Zierhut; Esin Ozturk-Isik; Albert P Chen; Ilwoo Park; Daniel B Vigneron; Sarah J Nelson Journal: J Magn Reson Imaging Date: 2009-09 Impact factor: 4.813
Authors: Philipp Moser; Wolfgang Bogner; Lukas Hingerl; Eva Heckova; Gilbert Hangel; Stanislav Motyka; Siegfried Trattnig; Bernhard Strasser Journal: Magn Reson Med Date: 2019-06-10 Impact factor: 4.668