Purpose To develop and validate a quantitative MRI methodology for phenotyping animal models of obesity and fatty liver disease on 7T small animal MRI scanners. Materials and Methods A new MRI acquisition and image analysis technique, Relaxation-Compensated Fat Fraction (RCFF), was developed validated by both Magnetic Resonance Spectroscopy and histology. This new RCFF technique was then used to assess lipid biodistribution in two groups of mice on either a high fat (HFD) or low fat (LFD) diet. Results RCFF demonstrated excellent correlation in phantom studies (R2=0.99) and in vivo in comparison to histological evaluation of hepatic triglycerides (R2=0.90). RCFF images provided robust fat fraction maps with consistent adipose tissue values (82%±3%). HFD mice exhibited significant increases in peritoneal and subcutaneous adipose tissue volumes in comparison to LFD controls (peritoneal: 6.4±0.4 cm3 vs. 0.7±0.2, P<0.001; subcutaneous: 14.7±2.0 cm3 vs. 1.2±0.3 cm3, P<0.001). Hepatic fat fractions were also significantly different between HFD and LFD mice (3.1%±1.7% LFD vs. 27.2%±5.4% HFD, P = 0.002). Conclusion RCFF can be used to quantitatively assess adipose tissue volumes and hepatic fat fractions in rodent models at 7T.

Purpose Most objective image quality metrics average over a wide range of image degradations. However, human clinicians demonstrate bias toward different types of artifacts. Here, we aim to create a perceptual difference model based on Case-PDM that mimics the bias of human observers towards different artifacts. Method We measured artifact disturbance to observers and calibrated the novel PDM. To tune the new model, which we call Artifact-PDM, degradations were synthetically added to three healthy brain MR data sets. Four types of artifacts (noise, blur, aliasing, or “oil-painting” which shows up as flattened, over-smoothened regions) of standard Compressed Sensing (CS) reconstruction, within a reasonable range of artifact severity, as measured by both PDM and visual inspection, were considered. After the model parameters were tuned by each synthetic image, we used a Functional Measurement Theory pair-comparison experiment to measure the disturbance of each artifact to human observers and determine the weights of each artifact’s PDM score. To validate Artifact-PDM, human ratings obtained from a Double Stimulus Continuous Quality Scale experiment were compared to the model for noise, blur, aliasing, oil-painting and overall qualities using a large set of CS reconstructed MR images of varying quality. Finally, we used this new approach to compare CS to GRAPPA, a parallel MRI reconstruction algorithm. Results We found that for the same Artifact-PDM score, the human observer found incoherent aliasing to be the most disturbing and noise the least. Artifact-PDM results were highly correlated to human observers in both experiments. Optimized CS reconstruction quality compared favorably to GRAPPA’s for the same sampling ratio. Conclusions We conclude our novel metric can faithfully represent human observer artifact evaluation and can be useful in evaluating CS and GRAPPA reconstruction algorithms, especially in studying artifact trade-offs.

Purpose: To quickly and robustly separate fat/water components of 7T MR images in the presence of field inhomogeneity for the study of metabolic disorders in small animals. Materials and Methods:Starting with a Markov random field (MRF) based formulation for the 3-point Dixon separation problem, we incorporated new implementation strategies, including stability tracking, multiresolution image pyramid, and improved initial value generation. We term the new method FLAWLESS (Fast Lipid And Water Levels by Extraction with Spatial Smoothing).Results: Compared with non-MRF techniques, FLAW-LESS decreased the fat-water swapping mistakes in all of the three-dimensional (3D) animal volumes that we tested. FLAWLESS converged in approximately 1/60th of the computation time of other MRF approaches. The initial value generation of FLAWLESS further improved robustness to field inhomogeneity in 3D volume data. Conclusion:We have developed a novel 3-point Dixon technique found to be useful for high field small animal imaging. It is being used to assess lipid depots and metabolic disorders as a function of genes, diet, age, and therapy.

Purpose: To develop a fast and robust Iterative Decomposition of water and fat with Echo Asymmetry and Leastsquares (IDEAL) reconstruction algorithm using graphics processor unit (GPU) computation. Materials and Methods:The fat-water reconstruction was expedited by vectorizing the fat-water parameter estimation, which was implemented on a graphics card to evaluate potential speed increases due to data-parallelization. In addition, we vectorized and compared Brent's method with golden section search for the optimization of the unknown field inhomogeneity parameter (c) in the IDEAL equations. The algorithm was made more robust to fat-water ambiguities using a modified planar extrapolation (MPE) of c algorithm. As compared to simple planar extrapolation (PE), the use of an averaging filter in MPE made the reconstruction more robust to neighborhoods poorly fit by a two-dimensional plane.Results: Fat-water reconstruction time was reduced by up to a factor of 11.6 on a GPU as compared to CPU-only reconstruction. The MPE algorithms incorrectly assigned fewer pixels than PE using careful manual correction as a gold standard (0.7% versus 4.5%; P < 10 À4 ). Brent's method used fewer iterations than golden section search in the vast majority of pixels (6.8 6 1.5 versus 9.6 6 1.6 iterations).Conclusion: Data sets acquired on a high field scanner can be quickly and robustly reconstructed using our algorithm. A GPU implementation results in significant time savings, which will become increasingly important with the trend toward high resolution mouse and human imaging.

Compressed Sensing (CS) and partially parallel imaging (PPI) enable fast MR imaging by reducing the amount of k-space data required for reconstruction. Past attempts to combine these two have been limited by the incoherent sampling requirement of CS, since PPI routines typically sample on a regular (coherent) grid. Here, we developed a new method, “CS+GRAPPA,” to overcome this limitation. We decomposed sets of equidistant samples into multiple random subsets. Then, we reconstructed each subset using CS, and averaging the results to get a final CS k-space reconstruction. We used both a standard CS, and an edge and joint-sparsity guided CS reconstruction. We tested these intermediate results on both synthetic and real MR phantom data, and performed a human observer experiment to determine the effectiveness of decomposition, and to optimize the number of subsets. We then used these CS reconstructions to calibrate the GRAPPA complex coil weights. In vivo parallel MR brain and heart data sets were used. An objective image quality evaluation metric, Case-PDM, was used to quantify image quality. Coherent aliasing and noise artifacts were significantly reduced using two decompositions. More decompositions further reduced coherent aliasing and noise artifacts but introduced blurring. However, the blurring was effectively minimized using our new edge and joint-sparsity guided CS using two decompositions. Numerical results on parallel data demonstrated that the combined method greatly improved image quality as compared to standard GRAPPA, on average halving Case-PDM scores across a range of sampling rates. The proposed technique allowed the same Case-PDM scores as standard GRAPPA, using about half the number of samples. We conclude that the new method augments GRAPPA by combining it with CS, allowing CS to work even when the k-space sampling pattern is equidistant.