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Register a new userOn the R2* Relaxometry in Complex Multi-Peak Multi-Echo Chemical Shift-Based Water-Fat Quantification: Applications to the Neuromuscular Diseases
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Giulio Siracusano Dr.
Publication date
2016
fields
Physics
and research's language is
English
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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.
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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.
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: 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.
Multi-echo Chemical Shift Encoded methods for Fat-Water quantification are growing in clinical use due to their ability to estimate and correct some confounding effects. State of the art CSE water-fat separation approaches rely on a multi-peak fat spectrum with peak frequencies and relative amplitudes kept constant over the entire MRI dataset. However, the latter approximation introduces a systematic error in fat percentage quantification in patients where the differences in lipid chemical composition are significant, such as for neuromuscular disorders, because of the spatial dependence of the peak amplitudes. The present work aims to overcome this limitation by taking advantage of an unsupervised clusterization-based approach offering a reliable criterion to carry out a data-driven segmentation of the input MRI dataset into multiple regions. The idea is to apply the clusterization for partitioning the multi-echo MRI dataset into a finite number of clusters whose internal voxels exhibit similar distance metrics. For each cluster, the estimation of the fat spectral properties are evaluated with a self-calibration technique and finally the fat-water percentages are computed via a non-linear fitting. The method is tested in ad-hoc and public datasets. The overall performance and results in terms of fitting accuracy, robustness and reproducibility are compared with other state-of-the-art CSE algorithms. This approach provides a more accurate and reproducible identification of chemical species, hence fat-water separation, when compared with other calibrated and non-calibrated approaches.
Purpose: To develop an approach for improving the resolution and sensitivity of hyperpolarized 13C MRSI based on a priori anatomical information derived from featured, water-based 1H images. Methods: A reconstruction algorithm exploiting 1H MRI for the redefinition of the 13C MRSI anatomies was developed, based on a modification of the Spectroscopy with Linear Algebraic Modeling (SLAM) principle. To enhance 13C spatial resolution and reduce spillover effects without compromising SNR, this model was extended by endowing it with a search allowing smooth variations in the 13C MR intensity within the targeted regions of interest. Results: Experiments were performed in vitro on enzymatic solutions and in vivo on rodents, based on the administration of 13C-enriched hyperpolarized pyruvate and urea. The spectral images reconstructed for these substrates and from metabolic products based on predefined 1H anatomical compartments using the new algorithm, compared favorably with those arising from conventional Fourier-based analyses of the same data. The new approach also delivered reliable kinetic 13C results, for the kind of processes and timescales usually targeted by hyperpolarized MRSI. Conclusions: A simple yet flexible strategy is introduced to boost the sensitivity and resolution provided by hyperpolarized 13C MRSI, based on readily available 1H MR information.
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