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Register a new userModel-Based Reconstruction for Simultaneous Multi-Slice T1 Mapping using Single-Shot Inversion-Recovery Radial FLASH
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Purpose: To develop a single-shot multi-slice T1 mapping method by combing simultaneous multi-slice (SMS) excitations, single-shot inversion-recovery (IR) radial fast low-angle shot (FLASH) and a nonlinear model-based reconstruction method. Methods: SMS excitations are combined with a single-shot IR radial FLASH sequence for data acquisition. A previously developed single-slice calibrationless model-based reconstruction is extended to SMS, formulating the estimation of parameter maps and coil sensitivities from all slices as a single nonlinear inverse problem. Joint-sparsity constraints are further applied to the parameter maps to improve T1 precision. Validations of the proposed method are performed for a phantom and for the human brain and liver in six healthy adult subjects. Results: Phantom results confirm good T1 accuracy and precision of the simultaneously acquired multi-slice T1 maps in comparison to single-slice references. In-vivo human brain studies demonstrate the better performance of SMS acquisitions compared to the conventional spoke-interleaved multi-slice acquisition using model-based reconstruction. Apart from good accuracy and precision, the results of six healthy subjects in both brain and abdominal studies confirm good repeatability between scan and re-scans. The proposed method can simultaneously acquire T1 maps for five slices of a human brain ($0.75 times 0.75 times 5$ mm$^3$) or three slices of the abdomen ($1.25 times 1.25 times 6$ mm$^3$) within four seconds. Conclusion: The IR SMS radial FLASH acquisition together with a non-linear model-based reconstruction enable rapid high-resolution multi-slice T1 mapping with good accuracy, precision, and repeatability.
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Purpose: The development of a calibrationless parallel imaging method for accelerated simultaneous multi-slice (SMS) MRI based on Regularized Nonlinear Inversion (NLINV), evaluated using Cartesian and radial FLASH. Theory and Methods: NLINV is a parallel imaging method that jointly estimates image content and coil sensitivities using a Newton-type method with regularization. Here, NLINV is extended to SMS-NLINV for reconstruction and separation of all simultaneously acquired slices. The performance of the extended method is evaluated for different sampling schemes using phantom and in-vivo experiments based on Cartesian and radial SMS-FLASH sequences. Results: The basic algorithm was validated in Cartesian experiments by comparison with ESPIRiT. For Cartesian and radial sampling, improved results are demonstrated compared to single-slice experiments, and it is further shown that sampling schemes using complementary samples outperform schemes with the same samples in each partition. Conclusion: The extension of the NLINV algorithm for SMS data was implemented and successfully demonstrated in combination with a Cartesian and radial SMS-FLASH sequence.
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.
Purpose: To develop and evaluate MyoMapNet, a rapid myocardial T1 mapping approach that uses neural networks (NN) to estimate voxel-wise myocardial T1 and extracellular (ECV) from T1-weighted images collected after a single inversion pulse over 4-5 heartbeats. Method: MyoMapNet utilizes a simple fully-connected NN to estimate T1 values from 5 (native) or 4 (post-contrast) T1-weighted images. Native MOLLI-5(3)3 T1 was collected in 717 subjects (386 males, 55$pm$16.5 years) and post-contrast MOLLI-4(1)3(1)2 in 535 subjects (232 male, 56.5$pm$15 years). The dataset was divided into training (80%) and testing (20%), where 20% of the training set was used to optimize MyoMapNet architecture (size and loss functions). We used MyoMapNet to estimate T1 and ECV maps with the first 5 (native) or 4 (post-contrast) T1-weighted images from the corresponding MOLLI sequence compared to the conventional and an abbreviated MOLLI using similar number of T1-weighted images with 3-parameter curve-fitting. Results: In our preliminary optimizaiton step, we determined that a 5-layers NN trained using mean-absolute-error loss yields lower estimation errors and was used subsequently in independent testing study. The myocardial T1 by MyoMapNet was similar to MOLLI (1200$pm$45ms vs. 1199$pm$46ms; P=0.3 for native T1, and 27.3$pm$3.5% vs. 27.1$pm$4%; P=0.4 for ECV). MyoMapNet had significantly smaller errors in T1 estimations compared to abbreviated-MOLLI (1$pm$17ms vs. 31$pm$34ms, P<0.01 for in native T1, and 0.1$pm$1.3% vs. 1.9$pm$2.5%, P<0.01 for ECV). The duration of T1 estimation was approximately 2 ms per slice using MyoMapNet. Conclusion: MyoMapNet T1 mapping enables myocardial T1 quantification in 4-5 heartbeats with near-instantaneous map estimation time with similar accuracy and precision as MOLLI. Keywords: Myocardial T1 mapping, MOLLI, T1 reconstruction, Neural network, Deep Learning.
Spin-echo functional MRI (SE-fMRI) has the potential to improve spatial specificity when compared to gradient-echo fMRI. However, high spatiotemporal resolution SE-fMRI with large slice-coverage is challenging as SE-fMRI requires a long echo time (TE) to generate blood oxygenation level-dependent (BOLD) contrast, leading to long repetition times (TR). The aim of this work is to develop an acquisition method that enhances the slice-coverage of SE-fMRI at high spatiotemporal resolution. An acquisition scheme was developed entitled Multisection Excitation by Simultaneous Spin-echo Interleaving (MESSI) with complex-encoded generalized SLIce Dithered Enhanced Resolution (cgSlider). MESSI utilizes the dead-time during the long TE by interleaving the excitation and readout of two slices to enable 2x slice-acceleration, while cgSlider utilizes the stable temporal background phase in SE-fMRI to encode and decode two adjacent slices simultaneously with a phase-constrained reconstruction method. The proposed cgSlider-MESSI was also combined with Simultaneous Multi-Slice (SMS) to achieve further slice-acceleration. This combined approach was used to achieve 1.5mm isotropic whole-brain SE-fMRI with a temporal resolution of 1.5s and was evaluated using sensory stimulation and breath-hold tasks at 3T. Compared to conventional SE-SMS, cgSlider-MESSI-SMS provides four-fold increase in slice-coverage for the same TR, with comparable temporal signal-to-noise ratio. Corresponding fMRI activation from cgSlider-MESSI-SMS for both fMRI tasks were consistent with those from conventional SE-SMS. Overall, cgSlider-MESSI-SMS achieved a 32x encoding-acceleration by combining RinplanexMBxcgSliderxMESSI=4x2x2x2. High-quality, high-resolution whole-brain SE-fMRI was acquired at a short TR using cgSlider-MESSI-SMS.
In radial fast spin-echo MRI, a set of overlapping spokes with an inconsistent T2 weighting is acquired, which results in an averaged image contrast when employing conventional image reconstruction techniques. This work demonstrates that the problem may be overcome with the use of a dedicated reconstruction method that further allows for T2 quantification by extracting the embedded relaxation information. Thus, the proposed reconstruction method directly yields a spin-density and relaxivity map from only a single radial data set. The method is based on an inverse formulation of the problem and involves a modeling of the received MRI signal. Because the solution is found by numerical optimization, the approach exploits all data acquired. Further, it handles multi-coil data and optionally allows for the incorporation of additional prior knowledge. Simulations and experimental results for a phantom and human brain in vivo demonstrate that the method yields spin-density and relaxivity maps that are neither affected by the typical artifacts from TE mixing, nor by streaking artifacts from the incomplete k-space coverage at individual echo times.
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