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Register a new userFat fraction mapping using bSSFP Signal Profile Asymmetries for Robust multi-Compartment Quantification (SPARCQ)
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Added by
Jessica A.M. Bastiaansen
Publication date
2020
fields
Physics
and research's language is
English
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Purpose: To develop a novel quantitative method for detection of different tissue compartments based on bSSFP signal profile asymmetries (SPARCQ) and to provide a validation and proof-of-concept for voxel-wise water-fat separation and fat fraction mapping. Methods: The SPARCQ framework uses phase-cycled bSSFP acquisitions to obtain bSSFP signal profiles. For each voxel, the profile is decomposed into a weighted sum of simulated profiles with specific off-resonance and relaxation time ratios. From the obtained set of weights, voxel-wise estimations of the fractions of the different components and their equilibrium magnetization are extracted. For the entire image volume, component-specific quantitative maps as well as banding-artifact-free images are generated. A SPARCQ proof-of-concept was provided for water-fat separation and fat fraction mapping. Noise robustness was assessed using simulations. A dedicated water-fat phantom was used to validate fat fractions estimated with SPARCQ against gold-standard 1H MRS. Quantitative maps were obtained in knees of six healthy volunteers, and SPARCQ repeatability was evaluated in scan rescan experiments. Results: Simulations showed that fat fraction estimations are accurate and robust for signal-to-noise ratios above 20. Phantom experiments showed good agreement between SPARCQ and gold-standard (GS) fat fractions (fF(SPARCQ) = 1.02*fF(GS) + 0.00235). In volunteers, quantitative maps and banding-artifact-free water-fat-separated images obtained with SPARCQ demonstrated the expected contrast between fatty and non-fatty tissues. The coefficient of repeatability of SPARCQ fat fraction was 0.0512. Conclusion: The SPARCQ framework was proposed as a novel quantitative mapping technique for detecting different tissue compartments, and its potential was demonstrated for quantitative water-fat separation.
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Tracking the migration of superparamagnetic iron oxide (SPIO) labeled immune cells in vivo is valuable for understanding the immunogenic response to cancer and therapies. Quantitative cell tracking using compressed sensing TurboSPI-based R2* mapping is a promising development to improve accuracy in longitudinal studies on immune recruitment. The phase-encoded TurboSPI sequence provides high fidelity relaxation data in the form of signal time-courses with high temporal resolution. However, early in vivo applications of this method revealed that simple mono-exponential R2* fitting performs poorly due to the contaminant fat signal in voxels surrounding regions of interest, such as flank tumors and lymph nodes adjacent to adipose tissue. This is especially problematic if there is poor infiltration to the tumor such that immune cells remain near the periphery. The presence of an off-resonance fat isochromat results in modulations in the signal time-course can be erroneously fit as R2* signal decay, thereby overestimating the density of SPIO labeled cells. Simply excluding any voxel with fat-typical modulations results in underestimates in voxels that have mixed content. We propose using a more comprehensive dual-decay (R2f* and R2w*) Dixon-based signal model that accounts for the potential presence of fat in a voxel to better estimate SPIO induced de-phasing. In silico single voxel simulations illustrate how the proposed signal model provides stable R2w* estimates that are invariant to fat content. The proposed dual-decay model outperforms previous methods when applied to in vitro samples of SPIO labeled cells and oil prepared with oil content >15%. Preliminary in vivo results show that, compared to previous methods, the dual-decay Dixon model improves the balance of R2* specificity versus sensitivity, which in turn will result in more reliable analysis in future cell tracking studies.
Purpose: Investigation of the feasibility of the R2* mapping techniques by using latest theoretical models corrected for confounding factors and optimized for signal to noise ratio. Theory and Methods: The improvement of the performance of state of the art MRI relaxometry algorithms is challenging because of a non-negligible bias and still unresolved numerical instabilities. Here, R2* mapping reconstructions, including complex-fitting with multi-spectral fat-correction by using single-decay (1D) and double-decay (2D) formulation, are studied in order to identify optimal configuration parameters and minimize numerical artifacts. The effects of echo number, echo spacing, and fat/water relaxation model are evaluated through simulated and in-vivo data. We also explore the stability of such models by analyzing the impact of high percentage of fat infiltrations and local transverse relaxation differences among biological species. Results: The main limits of the MRI relaxometry are the presence of bias and the occurrence of artifacts which affect its accuracy. Chemical-shift complex reconstructions R2*-corrected with 1D formulation exhibit a large bias in presence of a significant difference in the relaxation rates of fat and water and with fat concentration larger than 30%. We find that for fat-dominated tissues or in patients affected by iron overload, MRI reconstructions accounting for multi-exponential relaxation time provide accurate R2* measurements and are less prone to numerical artifacts. Conclusions: Complex fitting 2D formulation outperforms the conventional 1D approximation in various diagnostic scenarios. Although it still lacks of numerical stability which requires model enhancement and support from spectroscopy, it offers promising perspectives for the development of relaxometry as a reliable tool to improve tissue characterization and monitoring of neuromuscular disorders.
The quantification of liver fat as a diagnostic assessment of steatosis remains an important priority for noninvasive imaging systems. We derive a framework in which the unknown fat volume percentage can be estimated from a pair of ultrasound measurements. The precise estimation of ultrasound speed of sound and attenuation within the liver are shown to be sufficient for estimating fat volume assuming a classical model of the properties of a composite elastic material. In this model, steatosis is represented as a random dispersion of spherical fat vacuoles with acoustic properties similar to those of edible oils. Using values of speed of sound and attenuation from the literature where normal and steatotic livers were studied near 3.5 MHz, we demonstrate agreement of the new estimation method with independent measures of fat. This framework holds the potential for translation to clinical scanners where the two ultrasound measurements can be made and utilized for improved quantitative assessment of steatosis.
We suggest to utilize the rich information content about microstructural tissue properties entangled in asymmetric balanced steady-state free precession (bSSFP) profiles to estimate multiple diffusion metrics simultaneously by neural network (NN) parameter quantification. A 12-point bSSFP phase-cycling scheme with high-resolution whole-brain coverage is employed at 3 T and 9.4 T for NN input. Low-resolution target diffusion data are derived based on diffusion-weighted spin-echo echo-planar-imaging (SE-EPI) scans, i.e., mean, axial, and radial diffusivity (MD, AD, RD), fractional anisotropy (FA) as well as the spherical coordinates (azimuth ${Phi}$ and inclination ${Theta}$) of the principal diffusion eigenvector. A feedforward NN is trained with incorporated probabilistic uncertainty estimation. The NN predictions yielded highly reliable results in white matter (WM) and gray matter (GM) structures for MD. The quantification of FA, AD, and RD was overall in good agreement with the reference but the dependence of these parameters on WM anisotropy was somewhat biased, e.g., in corpus callosum. The inclination ${Theta}$ was well predicted for anisotropic WM structures while the azimuth ${Phi}$ was overall poorly predicted. The findings were highly consistent across both field strengths. Application of the optimized NN to high-resolution input data provided whole-brain maps with rich structural details. In conclusion, the proposed NN-driven approach showed potential to provide distortion-free high-resolution whole-brain maps of multiple diffusion metrics at high to ultra-high field strengths in clinically relevant scan times.
Purpose: To achieve free-breathing quantitative fat and $R_2^*$ mapping of the liver using model-based iterative reconstruction, dubbed as MERLOT. Methods: For acquisition, we use a multi-echo radial FLASH (fast low-angle shot) sequence that acquires multiple echoes with different complementary radial spoke encodings. We investigate real-time single-slice and volumetric multi-echo radial acquisition. Model-based reconstruction based on generalized nonlinear inversion is used to jointly estimate water, fat, $R_2^*$, $B_0$ field inhomogeneity, and coil sensitivity maps from the multi-coil multi-echo radial spokes. Spatial smoothness regularization is applied onto the $B_0$ field and coil sensitivity maps, whereas joint sparsity regularization is employed for the other parameter maps. The method integrates calibration-less parallel imaging and compressed sensing and was implemented in Berkeley Advanced Reconstruction Toolbox (BART). For the volumetric acquisition, the respiratory motion is resolved with self-gating using Adapted Singular Spectrum Analysis (SSA-FARY). The quantitative accuracy of the proposed method was validated via numerical simulation, the NIST phantom, a water/fat phantom, and in in-vivo liver studies. Results: For real-time acquisition, the proposed model-based reconstruction allowed acquisition of dynamic liver fat fraction and $R_2^*$ maps at a temporal resolution of 0.3 seconds per frame. For the volumetric acquisition, whole liver coverage could be achieved in under 2 minutes using the self-gated motion-resolved reconstruction. Conclusion: The proposed multi-echo radial sampling sequence achieves fast k-space coverage and is robust to motion. The proposed model-based reconstruction yields spatially and temporally resolved liver fat fraction, $R_2^*$ and $B_0$ field maps at high undersampling factor and with volume coverage.
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