Compressed sensing CPMG with group-sparse reconstruction for myelin water imaging

Stripe noise removal in conductive atomic force microscopy

Conductive atomic force microscopy (c-AFM) can provide simultaneous maps of the topography and electrical current flow through materials with high spatial resolution and it is playing an increasingly important role in the characterization of novel materials that are being investigated for novel memory devices. However, noise in the form of stripe features often appear in c-AFM images, challenging the quantitative analysis of conduction or topographical information. To remove stripe noise without losing interesting information, as many as sixteen destriping methods are investigated in this paper, including three additional models that we propose based on the stripes characteristics, and thirteen state-of-the-art destriping methods. We have also designed a gradient stripe noise model and obtained a ground truth dataset consisting of 800 images, generated by rotating and cropping a clean image, and created a noisy image dataset by adding random intensities of simulated noise to the ground truth dataset. In addition to comparing the results of the stripe noise removal visually, we performed a quantitative image quality comparison using simulated datasets and 100 images with very different strengths of simulated noise. All results show that the Low-Rank Recovery method has the best performance and robustness for removing gradient stripe noise without losing useful information. Furthermore, a detailed performance comparison of Polynomial fitting and Low-Rank Recovery at different levels of real noise is presented.

Whole Brain Myelin Water Mapping in One Minute Using Tensor Dictionary Learning With Low-Rank Plus Sparse Regularization

The quantification of myelin water content in the brain can be obtained by the multi-echo [Formula: see text] weighted images ( [Formula: see text]WIs). To accelerate the long acquisition, a novel tensor dictionary learning algorithm with low-rank and sparse regularization (TDLLS) is proposed to reconstruct the [Formula: see text]WIs from the undersampled data. The proposed algorithm explores the local and nonlocal similarity and the global temporal redundancy in the real and imaginary parts of the complex relaxation signals. The joint application of the low-rank constraints on the dictionaries and the sparse constraints on the core coefficient tensors improves the performance of the tensor-based recovery. Parallel imaging is incorporated into the TDLLS algorithm (pTDLLS) for further acceleration. A pulse sequence is proposed to prospectively undersample the Ky-t space to obtain the whole brain high-quality myelin water fraction (MWF) maps within 1 minute at an undersampling rate (R) of 6.

Fast multicomponent 3D-T1ρ relaxometry

NMR relaxometry can provide information about the relaxation of the magnetization in different tissues, increasing our understanding of molecular dynamics and biochemical composition in biological systems. In general, tissues have complex and heterogeneous structures composed of multiple pools. As a result, bulk magnetization returns to its original state with different relaxation times, in a multicomponent relaxation. Recovering the distribution of relaxation times in each voxel is a difficult inverse problem; it is usually unstable and requires long acquisition time, especially on clinical scanners. MRI can also be viewed as an inverse problem, especially when compressed sensing (CS) is used. The solution of these two inverse problems, CS and relaxometry, can be obtained very efficiently in a synergistically combined manner, leading to a more stable multicomponent relaxometry obtained with short scan times. In this paper, we will discuss the details of this technique from the viewpoint of inverse problems.

Multi-spin echo T2 relaxation imaging with compressed sensing (METRICS) for rapid myelin water imaging

PURPOSE

Myelin water imaging (MWI) provides a valuable biomarker for myelin, but clinical application has been restricted by long acquisition times. Accelerating the standard multi-echo T₂ acquisition with gradient echoes (GRASE) or by 2D multi-slice data collection results in image blurring, contrast changes, and other issues. Compressed sensing (CS) can vastly accelerate conventional MRI. In this work, we assessed the use of CS for in vivo human MWI, using a 3D multi spin-echo sequence.

METHODS

We implemented multi-echo T₂ relaxation imaging with compressed sensing (METRICS) and METRICS with partial Fourier acceleration (METRICS-PF). Scan-rescan data were acquired from 12 healthy controls for assessment of repeatability. MWI data were acquired for METRICS in 9 m:58 s and for METRICS-PF in 7 m:25 s, both with 1.5 × 2 × 3 mm³ voxels, 56 echoes, 7 ms ΔTE, and 240 × 240 × 170 mm³ FOV. METRICS was compared with a novel multi-echo spin-echo gold-standard (MSE-GS) MWI acquisition, acquired for a single additional subject in 2 h:2 m:40 s.

RESULTS

METRICS/METRICS-PF myelin water fraction had mean: repeatability coefficient 1.5/1.1, coefficient of variation 6.2/4.5%, and intra-class correlation coefficient 0.79/0.84. Repeatability metrics comparing METRICS with METRICS-PF were similar, and both sequences agreed with reference values from literature. METRICS images and quantitative maps showed excellent qualitative agreement with those of MSE-GS.

CONCLUSION

METRICS and METRICS-PF provided highly repeatable MWI data without the inherent disadvantages of GRASE or 2D multi-slice acquisition. CS acceleration allows MWI data to be acquired rapidly with larger FOV, higher estimated SNR, more isotropic voxels and more echoes than with previous techniques. The approach introduced here generalizes to any multi-component T₂ mapping application.

Quantitative neuroimaging measures of myelin in the healthy brain and in multiple sclerosis

Quantitative magnetic resonance imaging (MRI) techniques have been developed as imaging biomarkers, aiming to improve the specificity of MRI to underlying pathology compared to conventional weighted MRI. For assessing the integrity of white matter (WM), myelin, in particular, several techniques have been proposed and investigated individually. However, comparisons between these methods are lacking. In this study, we compared four established myelin‐sensitive MRI techniques in 56 patients with relapsing–remitting multiple sclerosis (MS) and 38 healthy controls. We used T2‐relaxation with combined GRadient And Spin Echoes (GRASE) to measure myelin water fraction (MWF‐G), multi‐component driven equilibrium single pulse observation of T₁ and T₂ (mcDESPOT) to measure MWF‐D, magnetization‐transfer imaging to measure magnetization‐transfer ratio (MTR), and T₁ relaxation to measure quantitative T₁ (qT₁). Using voxelwise Spearman correlations, we tested the correspondence of methods throughout the brain. All four methods showed associations that varied across tissue types; the highest correlations were found between MWF‐D and qT₁ (median ρ across tissue classes 0.8) and MWF‐G and MWF‐D (median ρ = 0.59). In eight WM tracts, all measures showed differences (p < 0.05) between MS normal‐appearing WM and healthy control WM, with qT1 showing the highest number of different regions (8), followed by MWF‐D and MTR (6), and MWF‐G (n = 4). Comparing the methods in terms of their statistical sensitivity to MS lesions in WM, MWF‐D demonstrated the best accuracy (p < 0.05, after multiple comparison correction). To aid future power analysis, we provide the average and standard deviation volumes of the four techniques, estimated from the healthy control sample.

Evaluating the accuracy of multicomponent T2 parameters for luminal water imaging of the prostate with acceleration using inner-volume 3D GRASE

PURPOSE

Prostate cancer can be detected using a multicomponent T₂ mapping technique termed luminal water imaging. The purpose of this study is twofold: 1) To accelerate the luminal water imaging acquisition by using inner volume selection as part of a gradient and spin echo sequence, and 2) to evaluate the accuracy of luminal water fractions and multicomponent T₂ relaxation times.

METHODS

The accuracy of parameter estimates was assessed using Monte Carlo simulations, in phantom experiments and in the prostate (in 5 healthy subjects). Two fitting methods, nonnegative least squares and biexponential fitting with stimulated echo correction, were compared.

RESULTS

Results demonstrate that inner volume selection in a gradient and spin echo sequence is effective for accelerating prostate luminal water imaging by at least threefold. Evaluation of the accuracy shows that the estimated luminal water fractions are relatively accurate, but the short- and long-T₂ relaxation times should be interpreted with caution in noisy scenarios (SNR < 100) and when the corresponding fractions are small ( < 0.5). The mean luminal water fractions obtained at SNR above 100 are 0.27 ± 0.07 for the peripheral zone for both fitting methods, 0.16 ± 0.04 for the transition zone with nonnegative least squares, and 0.16 ± 0.03 for the transition zone with biexponential fitting including stimulated echo correction.

CONCLUSION

The shortened scan duration allows the luminal water imaging sequence to be easily integrated into a standard multiparametric prostate MRI protocol.