Technical note: use of a double inversion recovery pulse sequence to image selectively grey or white brain matter.
ABSTRACT The design of a double inversion recovery (DIR) sequence, to image selectively grey or white brain matter, is described. Suitable choice of inversion times allows either cerebrospinal fluid (CSF) and white matter to be suppressed, to image the cortex alone, or CSF and grey matter to be suppressed, to image the white matter. The DIR sequence was found to give clear delineation of the cerebral cortex.
- SourceAvailable from: Paul Polak
Conference Paper: 3D DIR: 3D Double Inversion Recovery in Multiple SclerosisInternation Society of Magnetic Resonance in Medicine; 05/2011
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ABSTRACT: To investigate the efficacy of the double inversion recovery sequence (DIR) in breast cancer detection. Fifty-six patients with biopsy-proven breast cancers underwent preoperative breast MRI, including sagittal DIR and contrast-enhanced T1-weighted images (CE-T1WI). Twenty-four of the 56 patients additionally underwent sagittal T1WI and T2WI. The signal intensities of the lesion (L) and ipsilateral normal breast tissue (N) were measured. The lesion-to-normal ratio (LNR) was defined as LNR = 100(L-N)/N. We compared LNRs among the four sequences, and then assessed the differences of LNRs between CE-T1WI and DIR in each pathologic subgroup (IDC and non-IDC group). Multiple regression analysis was performed to identify predictors of the signal-to-noise ratios (SNR) of the normal tissue or lesion and LNRs. The mean LNR did not differ significantly between DIR (58.65 ± 71.55) and CE-T1WI (59.78 ±31.04), nor did the LNRs between DIR and CE-T1WI in the two subgroups. The LNRs of DIR did not differ significantly between the two subgroups (P = 0.247). The SNR of lesions in DIR was correlated with the intraductal component percentage (r(2) = 0.485, P = 0.037). DIR and CE-T1WI showed similar tumor detection efficacy, and DIR could complement dynamic MRI for detecting breast cancer without a contrast agent. J. Magn. Reson. Imaging 2013;. © 2013 Wiley Periodicals, Inc.Journal of Magnetic Resonance Imaging 10/2013; · 2.57 Impact Factor
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ABSTRACT: Visual but subjective reading of longitudinal relaxation time (T1) weighted magnetic resonance images is commonly used for the detection of brain pathologies. For this non-quantitative measure, diagnostic quality depends on hardware configuration, imaging parameters, radio frequency transmission field (B1+) uniformity, as well as observer experience. Parametric quantification of the tissue T1 relaxation parameter offsets the propensity for these effects, but is typically time consuming. For this reason, this study examines the feasibility of rapid 2D T1 quantification using a variable flip angles (VFA) approach at magnetic field strengths of 1.5 Tesla, 3 Tesla, and 7 Tesla. These efforts include validation in phantom experiments and application for brain T1 mapping. T1 quantification included simulations of the Bloch equations to correct for slice profile imperfections, and a correction for B1+. Fast gradient echo acquisitions were conducted using three adjusted flip angles for the proposed T1 quantification approach that was benchmarked against slice profile uncorrected 2D VFA and an inversion-recovery spin-echo based reference method. Brain T1 mapping was performed in six healthy subjects, one multiple sclerosis patient, and one stroke patient. Phantom experiments showed a mean T1 estimation error of (-63±1.5)% for slice profile uncorrected 2D VFA and (0.2±1.4)% for the proposed approach compared to the reference method. Scan time for single slice T1 mapping including B1+ mapping could be reduced to 5 seconds using an in-plane resolution of (2×2) mm2, which equals a scan time reduction of more than 99% compared to the reference method. Our results demonstrate that rapid 2D T1 quantification using a variable flip angle approach is feasible at 1.5T/3T/7T. It represents a valuable alternative for rapid T1 mapping due to the gain in speed versus conventional approaches. This progress may serve to enhance the capabilities of parametric MR based lesion detection and brain tissue characterization.PLoS ONE 01/2014; 9(3):e91318. · 3.53 Impact Factor