Compressed sensing for body MRI Feng, Li; Benkert, Thomas; Block, Kai Tobias ...
Journal of magnetic resonance imaging,
April 2017, Letnik:
45, Številka:
4
Journal Article
Recenzirano
Odprti dostop
The introduction of compressed sensing for increasing imaging speed in magnetic resonance imaging (MRI) has raised significant interest among researchers and clinicians, and has initiated a large ...body of research across multiple clinical applications over the last decade. Compressed sensing aims to reconstruct unaliased images from fewer measurements than are traditionally required in MRI by exploiting image compressibility or sparsity. Moreover, appropriate combinations of compressed sensing with previously introduced fast imaging approaches, such as parallel imaging, have demonstrated further improved performance. The advent of compressed sensing marks the prelude to a new era of rapid MRI, where the focus of data acquisition has changed from sampling based on the nominal number of voxels and/or frames to sampling based on the desired information content. This article presents a brief overview of the application of compressed sensing techniques in body MRI, where imaging speed is crucial due to the presence of respiratory motion along with stringent constraints on spatial and temporal resolution. The first section provides an overview of the basic compressed sensing methodology, including the notion of sparsity, incoherence, and nonlinear reconstruction. The second section reviews state‐of‐the‐art compressed sensing techniques that have been demonstrated for various clinical body MRI applications. In the final section, the article discusses current challenges and future opportunities.
Level of Evidence: 5
J. Magn. Reson. Imaging 2017;45:966–987
Purpose
To study the effects of magnetization transfer (MT, in which a semi‐solid spin pool interacts with the free pool), in the context of magnetic resonance fingerprinting (MRF).
Methods
...Simulations and phantom experiments were performed to study the impact of MT on the MRF signal and its potential influence on T1 and T2 estimation. Subsequently, an MRF sequence implementing off‐resonance MT pulses and a dictionary with an MT dimension, generated by incorporating a two‐pool model, were used to estimate the fractional pool size in addition to the B1+, T1, and T2 values. The proposed method was evaluated in the human brain.
Results
Simulations and phantom experiments showed that an MRF signal obtained from a cross‐linked bovine serum sample is influenced by MT. Using a dictionary based on an MT model, a better match between simulations and acquired MR signals can be obtained (NRMSE 1.3% vs. 4.7%). Adding off‐resonance MT pulses can improve the differentiation of MT from T1 and T2. In vivo results showed that MT affects the MRF signals from white matter (fractional pool‐size ~16%) and gray matter (fractional pool‐size ~10%). Furthermore, longer T1 (~1060 ms vs. ~860 ms) and T2 values (~47 ms vs. ~35 ms) can be observed in white matter if MT is accounted for.
Conclusion
Our experiments demonstrated a potential influence of MT on the quantification of T1 and T2 with MRF. A model that encompasses MT effects can improve the accuracy of estimated relaxation parameters and allows quantification of the fractional pool size.
Purpose
This study aimed to (i) develop Magnetization‐Prepared Golden‐angle RAdial Sparse Parallel (MP‐GRASP) MRI using a stack‐of‐stars trajectory for rapid free‐breathing T1 mapping and (ii) extend ...MP‐GRASP to multi‐echo acquisition (MP‐Dixon‐GRASP) for fat/water‐separated (water‐specific) T1 mapping.
Methods
An adiabatic non‐selective 180° inversion‐recovery pulse was added to a gradient‐echo‐based golden‐angle stack‐of‐stars sequence for magnetization‐prepared 3D single‐echo or 3D multi‐echo acquisition. In combination with subspace‐based GRASP‐Pro reconstruction, the sequence allows for standard T1 mapping (MP‐GRASP) or fat/water‐separated T1 mapping (MP‐Dixon‐GRASP), respectively. The accuracy of T1 mapping using MP‐GRASP was evaluated in a phantom and volunteers (brain and liver) against clinically accepted reference methods. The repeatability of T1 estimation was also assessed in the phantom and volunteers. The performance of MP‐Dixon‐GRASP for water‐specific T1 mapping was evaluated in a fat/water phantom and volunteers (brain and liver).
Results
ROI‐based mean T1 values are correlated between the references and MP‐GRASP in the phantom (R2 = 1.0), brain (R2 = 0.96), and liver (R2 = 0.73). MP‐GRASP achieved good repeatability of T1 estimation in the phantom (R2 = 1.0), brain (R2 = 0.99), and liver (R2 = 0.82). Water‐specific T1 is different from in‐phase and out‐of‐phase composite T1 (composite T1 when fat and water signal are mixed in phase or out of phase) both in the phantom and volunteers.
Conclusion
This work demonstrated the initial performance of MP‐GRASP and MP‐Dixon‐GRASP MRI for rapid 3D T1 mapping and 3D fat/water‐separated T1 mapping in the brain (without motion) and in the liver (during free breathing). With fat/water‐separated T1 estimation, MP‐Dixon‐GRASP could be potentially useful for imaging patients with fatty‐liver diseases.
Purpose
To develop a free‐breathing hepatic fat and
R2∗ quantification method by extending a previously described stack‐of‐stars model‐based fat‐water separation technique with additional modeling of ...the transverse relaxation rate
R2∗.
Methods
The proposed technique combines motion‐robust radial sampling using a stack‐of‐stars bipolar multi‐echo 3D GRE acquisition with iterative model‐based fat‐water separation. Parallel‐Imaging and Compressed‐Sensing principles are incorporated through modeling of the coil‐sensitivity profiles and enforcement of total‐variation (TV) sparsity on estimated water, fat, and
R2∗ parameter maps. Water and fat signals are used to estimate the confounder‐corrected proton‐density fat fraction (PDFF). Two strategies for handling respiratory motion are described: motion‐averaged and motion‐resolved reconstruction. Both techniques were evaluated in patients (n = 14) undergoing a hepatobiliary research protocol at 3T. PDFF and
R2∗ parameter maps were compared to a breath‐holding Cartesian reference approach.
Results
Linear regression analyses demonstrated strong (r > 0.96) and significant (P ≪ .01) correlations between radial and Cartesian PDFF measurements for both the motion‐averaged reconstruction (slope: 0.90; intercept: 0.07%) and the motion‐resolved reconstruction (slope: 0.90; intercept: 0.11%). The motion‐averaged technique overestimated hepatic
R2∗ values (slope: 0.35; intercept: 30.2 1/s) compared to the Cartesian reference. However, performing a respiratory‐resolved reconstruction led to better
R2∗ value consistency (slope: 0.77; intercept: 7.5 1/s).
Conclusions
The proposed techniques are promising alternatives to conventional Cartesian imaging for fat and
R2∗ quantification in patients with limited breath‐holding capabilities. For accurate
R2∗ estimation, respiratory‐resolved reconstruction should be used.
Purpose
To describe an inversion‐recovery T1‐weighted radial stack‐of‐stars 3D gradient echo (GRE) sequence with comparable image quality to conventional MP‐RAGE and to demonstrate how the radial ...acquisition scheme can be utilized for additional retrospective motion correction to improve robustness to head motion.
Methods
The proposed sequence, named MP‐RAVE, has been derived from a previously described radial stack‐of‐stars 3D GRE sequence (RAVE) and includes a 180° inversion recovery pulse that is generated once for every stack of radial views. The sequence is combined with retrospective 3D motion correction to improve robustness. The effectiveness has been evaluated in phantoms and healthy volunteers and compared to conventional MP‐RAGE acquisition.
Results
MP‐RAGE and MP‐RAVE anatomical images were rated “good” to “excellent” in overall image quality, with artifact level between “mild” and “no artifacts”, and with no statistically significant difference between methods. During head motion, MP‐RAVE showed higher inherent robustness with artifacts confined to local brain regions. In combination with motion correction, MP‐RAVE provided noticeably improved image quality during different head motion and showed statistically significant improvement in image sharpness.
Conclusion
MP‐RAVE provides comparable image quality and contrast to conventional MP‐RAGE with improved robustness to head motion. In combination with retrospective 3D motion correction, MP‐RAVE can be a useful alternative to MP‐RAGE, especially in non‐cooperative or pediatric patients.
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