1. ISMRM2012 Review Jun 4, 2012 Jason Su

2. Outline Parametric Mapping – Kumar et al. A Bayesian Algorithm Using Spatial Priors for Multi-Exponential T2 Relaxometry from Multi-Echo Spin Echo MRI. [0360] – Ott et al. Phase sensitive PC-bSSFP: simultaneous quantification of T1, T2 and spin density M0. [2387] – Rudrapatna et al. Robust estimation of T1 and T2 parameters from complex datasets. [3418] Image Reconstruction and Acceleration Methods – Kayvanrad et al. T1 Map Reconstruction from Under-sampled KSpace Data using a Similarity Constraint. [0015] – Weavers et al. Optimal Apportionment of Acceleration in 2D SENSE. [2224] Myelin Sensitive Sequences – Bluestein et al. Can T1-Differentiation in a Magnetization Prepared Turbo Field Echo Sequence at 7T Predict "Persistent Black Hole” White Matter Lesions in Multiple Sclerosis? [3115]

3. Parametric Mapping

4. A Bayesian Algorithm Using Spatial Priors for Multi-Exponential T2 Relaxometry from Multi-Echo Spin Echo MRI [0360] Dushyant Kumar et al. University of Hamburg Goal is to improve noisiness of MWF maps in qT2 The first term is the data fidelity term The second term is the conventional temporal regularization term which penalizes large values in inferred T2 distributions The third term imposes spatial constraints: first difference smoothness Resulting maps are reminiscent of mcDESPOT MWF and values are similar – However vanilla qT2 MWF values seem overestimated

5. A Bayesian Algorithm Using Spatial Priors for Multi-Exponential T2 Relaxometry from Multi-Echo Spin Echo MRI [0360]

6. Phase sensitive PC-bSSFP: simultaneous quantification of T1, T2 and spin density M0 [2387] Martin Ott et al. Magnetic Resonance Bavaria e.V, Würzburg, Bayern, Germany Goal is a new mapping sequence using phase cycled SSFP at two angles, don’t need a T1 map like DESPOT2 Solution is approximately to fit an ellipse to the data Still need to compare it against reference techniques

7. Phase sensitive PC-bSSFP: simultaneous quantification of T1, T2 and spin density M0 [2387]

8. Notable Mention Rudrapatna et al. Robust estimation of T1 and T2 parameters from complex datasets. [3418] – Incorporating complex data into the model fit improves bias and variance robustness in low (about 10) SNR

9. Image Reconstruction and Acceleration Methods

10. Optimal Apportionment of Acceleration in 2D SENSE. [2224] Weaver et al. Mayo Clinic, Rochester, MN Goal is to optimize the selection of Ry and Rz for a SENSE acquisition given coil sensitivities Choose Ry and Rz that minimizes the cost: maximum g-factor * scan time Simple, effective abstract, shouldn’t we have been doing this all along?

11. Optimal Apportionment of Acceleration in 2D SENSE. [2224]

12. Myelin Sensitive Sequences

13. Can T1-Differentiation in a Magnetization Prepared Turbo Field Echo Sequence at 7T Predict "Persistent Black Hole” White Matter Lesions in Multiple Sclerosis? [3115] Bluestein et al. The Ohio State University, Columbus, Ohio Goal was to develop a better sequence parameters for a T2w scan that better shows different types of lesions Simulated behavior of some lesion types with multicomponent signals and then chose parameters Compared against: – FLAIR (TR = 11000 ms, TI = 2800 ms, TE = 125 ms, TSE factor = 31) – WHAT (TS/TI = 4550/500 ms “That simulation showed that setting TS = 4500 ms and TI ranging from 880-980 ms, results in images with the long T1 WMLs brighter and short T1 WMLs darker than surrounding NAWM.”

14. Can T1-Differentiation in a Magnetization Prepared Turbo Field Echo Sequence at 7T Predict "Persistent Black Hole” White Matter Lesions in Multiple Sclerosis? [3115]