Balm Paints at the GMH power house, Elizabeth, South Australia, 1961 [picture] /

Condition: good.; Title from verso.; Sievers number: AC-3111-KA.; Title devised by cataloguer from information on verso.; Also available in an electronic version via the Internet at: http://nla.gov.au/nla.pic-vn3966012

Model-based Acceleration of Parameter mapping (MAP) for saturation prepared radially acquired data

A reconstruction technique called Model-based Acceleration of Parameter mapping (MAP) is presented allowing for quantification of longitudinal relaxation time and proton density from radial single-shot measurements after saturation recovery magnetization preparation. Using a mono-exponential model in image space, an iterative fitting algorithm is used to reconstruct one well resolved and consistent image for each of the projections acquired during the saturation recovery relaxation process. The functionality of the algorithm is examined in numerical simulations, phantom experiments, and in-vivo studies. MAP reconstructions of single-shot acquisitions feature the same image quality and resolution as fully sampled reference images in phantom and in-vivo studies. The longitudinal relaxation times obtained from the MAP reconstructions are in very good agreement with the reference values in numerical simulations as well as phantom and in-vivo measurements. Compared to available contrast manipulation techniques, no averaging of projections acquired at different time points of the relaxation process is required in MAP imaging. The proposed technique offers new ways of extracting quantitative information from single-shot measurements acquired after magnetization preparation. The reconstruction simultaneously yields images with high spatiotemporal resolution fully consistent with the acquired data as well as maps of the effective longitudinal relaxation parameter and the relative proton density. Magn Reson Med 70:1524-1534, 2013. © 2013 Wiley Periodicals, Inc. Copyrigh

Density weighted turbo spin echo imaging

Purpose To optimize the spatial response function (SRF) while maintaining optimal signal to noise ratio (SNR) in T weighted turbo spin echo (TSE) imaging by prospective density weighting. Materials and Methods Density weighting optimizes the SRF by sampling the k-space with variable density without the need of retrospective filtering, which would typically result in nonoptimal SNR. For TSE, the T decay needs to be considered when calculating an optimized sampling pattern. Simulations were carried out and T weighted in vivo TSE measurements were performed on a 3 Tesla MRI system. To evaluate the SNR, reversed centric density weighted and retrospectively filtered Cartesian acquisitions with identical measurement parameters and SRFs were compared with TE = 90 ms and a density weighted k-space sampling optimized to yield a Kaiser function for SRF side lobe suppression for white matter. Results Density weighting of a reversed centric reordering scheme resulted in an SNR increase of (43 ± 13)% compared with the Cartesian acquisition with retrospective filtering while maintaining comparable contrast behavior. Conclusion Density weighting is applicable to TSE imaging and results in significantly increased SNR. The gain can be used to shorten the measurement time, which suggests applying density weighting in both time and SNR constrained MRI

High resolution myocardial first-pass perfusion imaging with extended anatomic coverage

Purpose To evaluate and to compare Parallel Imaging and Compressed Sensing acquisition and reconstruction frameworks based on simultaneous multislice excitation for high resolution contrast-enhanced myocardial first-pass perfusion imaging with extended anatomic coverage. Materials and Methods The simultaneous multislice imaging technique MS-CAIPIRINHA facilitates imaging with significantly extended anatomic coverage. For additional resolution improvement, equidistant or random undersampling schemes, associated with corresponding reconstruction frameworks, namely Parallel Imaging and Compressed Sensing can be used. By means of simulations and in vivo measurements, the two approaches were compared in terms of reconstruction accuracy. Comprehensive quality metrics were used, identifying statistical and systematic reconstruction errors. Results The quality measures applied allow for an objective comparison of the frameworks. Both approaches provide good reconstruction accuracy. While low to moderate noise enhancement is observed for the Parallel Imaging approach, the Compressed Sensing framework is subject to systematic errors and reconstruction induced spatiotemporal blurring. Conclusion Both techniques allow for perfusion measurements with a resolution of 2.0 × 2.0 mm and coverage of six slices every heartbeat. Being not affected by systematic deviations, the Parallel Imaging approach is considered to be superior for clinical studies