Relaxometry & Image Processing

1. Relaxometry & Image Processing Competitive Info Technical Update • T1 (DESPOT1 and LL) parameter fitting via adaptive neural networks [137, 3907] • DESPOT improvements [2740: flip angle smoothness regularization, 4488: T1+B1, 4622: partial volume correction] • New T1/T2 methods at 3T [231-MP-DESS, 381-FSE] • Improved segmentation for any software [133: texture-based priors, 540-learning for automated editing] • Longitudinal qT2-MWF in NAWM for MS [2174] • U. of Wisconsin is developing many interesting extensions to the core DESPOT acquisition • mcDESPOT continues to look for a killer app • Brown U. - infant studies, AD • Oxford - cerebral neuron disease • Stanford/Dresden – MS Recommendations Clinical Findings/Product Need • Stanford should look into applying machine learning techniques to the mcDESPOT fitting problem • Stanford needs to put its 3T protocol through its paces • Stanford has expertise in B1 mapping, particularly with Bloch-Siegert: evaluate the joint fitting of DESPOT and BS data • Histological verification of mcDESPOT MWF • Proven repeatable 3T+ performance of DESPOT

2. 4488: Spoiling properties of the VAFI method for fast simultaneous T1 and B1 mapping from actual flip-angle imaging (AFI) and variable flip-angle (VFA) data. Hurley et al., University of Wisconsin The work evaluates the issue of spoiling in SPGR for DESPOT1 and AFI acquisitions as well as the benefit gained by fitting for both T1 and B1 simultaneously. Jointly fitting allows for the T1 estimate to be much immune to spoiling problems. There is still an optimum for AFI, but it is a much more robust one.

3. 137: MR Estimation of Longitudinal Relaxation Time (T1) in Spoiled Gradient Echo Using an Adaptive Neural Network Bagher-Ebadian et al., Henry Ford Hospital An artificial neural network trained on simulated DESPOT1 data with Gaussian noise is able to outperform straightforward fitting algorithms for the signal equation. The percent average error of estimation is better at all SNRs. In vivo values may be underestimated here compared to literature due to B1 at 3T, which is unaccounted for. High resolution data is processed in seconds vs. minutes with traditional methods.

4. 540: A General-Purpuse Learning-Based Wrapper Method to Correct Systematic Errors in Automatic Image Segmentation: Consistently Improved Performance in Hippocampus, Cortex and Brain Segmentation Wang et al., University of Pennsylvania Many segmentation tools have a systematic errors in their resulting masks that must always be manually edited out. Here the authors apply AdaBoost to develop a technique that uses already edited masks to learn these errors and automatically correct them. This method can be applied to any existing package. Substantial improvement is improved with just a few training masks.

5. 4622: Partial Volume Correction of Myelin Water Fraction values Bells et al., CUBRIC A free water content map is derived using DTI and then applied to correct mcD-MWF values at 3T. FWC distort MWF tissue compartment histograms. Strangely, there is a 10% underestimation of MWF throughout the brain, meaning mcD-MWF is even farther from qT2-MWF after this correction.