Fast image reconstruction with L2-regularization

Prognostic prediction of carcinoma by a differential-regulatory-network-embedded deep neural network

The accurate prognostic prediction is essential for precise diagnosis and treatment of carcinoma. In addition to clinical survival prediction method, many computational methods based on transcriptomic data have been proposed to build the prediction models and study the prognosis of cancer patients. We propose a differential-regulatory-network-embedded deep neural network (DRE-DNN) method by integrating differential regulatory analysis based on gene co-expression network and deep neural network (DNN) method. From three public hepatocellular carcinoma (HCC) datasets, we derive differential regulatory network and embed regulatory information into DNN. By employing 1869 differential regulatory genes and survival data, we apply DRE-DNN to build a prediction model. We compare our method with the one which has all gene features in normal DNN, and results show that our method has better generalization ability and accuracy. We modify the normal DNN and develop an efficient method to predict prognosis of HCC from gene expression data. Our method decreases the inconsistence caused by the overfitting problem when the training sample size is small. DRE-DNN is also extendable for prognostic prediction of other cancers.

Clinical High-Resolution 3D-MR Spectroscopic Imaging of the Human Brain at 7 T

OBJECTIVES

Available clinical magnetic resonance spectroscopic imaging (MRSI) sequences are hampered by long scan times, low spatial resolution, strong field inhomogeneities, limited volume coverage, and low signal-to-noise ratio. High-resolution, whole-brain mapping of more metabolites than just N-acetylaspartate, choline, and creatine within clinically attractive scan times is urgently needed for clinical applications. The aim is therefore to develop a free induction decay (FID) MRSI sequence with rapid concentric ring trajectory (CRT) encoding for 7 T and demonstrate its clinical feasibility for mapping the whole cerebrum of healthy volunteers and patients.

MATERIALS AND METHODS

Institutional review board approval and written informed consent were obtained. Time-efficient, 3-dimensional encoding of an ellipsoidal k-space by in-plane CRT and through-plane phase encoding was integrated into an FID-MRSI sequence. To reduce scan times further, repetition times were shortened, and variable temporal interleaves were applied. Measurements with different matrix sizes were performed to validate the CRT encoding in a resolution phantom. One multiple sclerosis patient, 1 glioma patient, and 6 healthy volunteers were prospectively measured. For the healthy volunteers, brain segmentation was performed to quantify median metabolic ratios, Cramér-Rao lower bounds (CRLBs), signal-to-noise ratios, linewidths, and brain coverage among all measured matrix sizes ranging from a 32 × 32 × 31 matrix with 6.9 × 6.9 × 4.2 mm nominal voxel size acquired in ~3 minutes to an 80 × 80 × 47 matrix with 2.7 × 2.7 × 2.7 mm nominal voxel size in ~15 minutes for different brain regions.

RESULTS

Phantom structures with diameters down to 3 to 4 mm were visible. In vivo MRSI provided high spectral quality (median signal-to-noise ratios, >6.3 and linewidths, <0.082 ppm) and fitting quality. Cramér-Rao lower bounds were ranging from less than 22% for glutamine (highest CRLB in subcortical gray matter) to less than 9.5% for N-acetylaspartate for the 80 × 80 × 47 matrix (highest CRLB in the temporal lobe). This enabled reliable mapping of up to 8 metabolites (N-acetylaspartate, N-acetylaspartyl glutamate, total creatine, glutamine, glutamate, total choline, myo-inositol, glycine) and macromolecules for all resolutions. Coverage of the whole cerebrum allowed visualization of the full extent of diffuse and local multiple sclerosis-related neurochemical changes (eg, up to 100% increased myo-inositol). Three-dimensional brain tumor metabolic maps provided valuable information beyond that of single-slice MRSI, with up to 200% higher choline, up to 100% increased glutamine, and increased glycine in tumor tissue.

CONCLUSIONS

Seven Tesla FID-MRSI with time-efficient CRT readouts offers clinically attractive acquisition protocols tailored either for speed or for the investigation of small pathologic details and low-abundant metabolites. This can complement clinical MR studies of various brain disorders. Significant metabolic anomalies were demonstrated in a multiple sclerosis and a glioma patient for myo-inositol, glutamine, total choline, glycine, and N-acetylaspartate concentrations.

Effects of different macromolecular models on reproducibility of FID-MRSI at 7T

PURPOSE

A properly characterized macromolecular (MM) contribution is essential for accurate metabolite quantification in FID-MRSI. MM information can be included into the fitting model as a single component or parameterized and included over several individual MM resonances, which adds flexibility when pathologic changes are present but is prone to potential overfitting. This study investigates the effects of different MM models on MRSI reproducibility.

METHODS

Clinically feasible, high-resolution FID-MRSI data were collected in ~5 min at 7 Tesla from 10 healthy volunteers and quantified via LCModel (version 6.3) with 3 basis sets, each with a different approach for how the MM signal was handled: averaged measured whole spectrum (full MM), 9 parameterized components (param MM) with soft constraints to avoid overparameterization, or without any MM information included in the fitting prior knowledge. The test-retest reproducibility of MRSI scans was assessed voxel-wise using metabolite coefficients of variation and intraclass correlation coefficients and compared between the basis sets. Correlations of concentration estimates were investigated for the param MM fitting model.

RESULTS

The full MM model provided the most reproducible quantification of total NAA, total Cho, myo-inositol, and glutamate + glutamine ratios to total Cr (coefficients of variations ≤ 8%, intraclass correlation coefficients ≥ 0.76). Using the param MM model resulted in slightly lower reproducibility (up to +3% higher coefficients of variations, up to -0.1 decreased intraclass correlation coefficients). The quantification of the parameterized macromolecules did not affect quantification of the overlapping metabolites.

CONCLUSION

Clinically feasible FID-MRSI with an experimentally acquired MM spectrum included in prior knowledge provides highly reproducible quantification for the most common neurometabolites in healthy volunteers. Parameterization of the MM spectrum may be preferred as a compromise between quantification accuracy and reproducibility when the MM content is expected to be pathologically altered.

Robust outer volume suppression utilizing elliptical pulsed second order fields (ECLIPSE) for human brain proton MRSI

PURPOSE

The robust and reliable utilization of proton magnetic resonance spectroscopic imaging (MRSI) at high fields is hampered by several key technical difficulties, including contamination from extracranial lipids. To that end, this work presents novel lipid suppression sequences for proton MRSI in the human brain utilizing elliptical localization with pulsed second-order fields (ECLIPSE).

METHODS

Two lipid suppression methods were implemented with the ECLIPSE gradient insert. One method is a variable power, 4-pulse sequence optimized to achieve outer volume suppression (OVS) and compared against a standard, 8-slice OVS method. The second ECLIPSE method is implemented as an inversion recovery (IR) sequence with elliptical inner volume selection (IVS) and compared against a global IR method.

RESULTS

The ECLIPSE-OVS sequence provided a 116-fold mean lipid suppression (range, 104-134), whereas an optimized 8-slice OVS sequence achieved 15-fold suppression (range, 13-18). Furthermore, the superior ECLIPSE-OVS suppression was achieved at 30% of the radiofrequency (RF) power required by 8-slice OVS. The ECLIPSE-based IR sequence suppressed skull lipids by 155-fold (range, 122-257), compared to 16-fold suppression (range, 14-19) achieved with IR.

CONCLUSION

OVS and IVS executed with ECLIPSE provide robust and effective lipid suppression at reduced RF power with high immunity to variations in B₁ and T₁ .

Super-Resolution 1H Magnetic Resonance Spectroscopic Imaging Utilizing Deep Learning

Magnetic resonance spectroscopic imaging (SI) is a unique imaging technique that provides biochemical information from in vivo tissues. The 1H spectra acquired from several spatial regions are quantified to yield metabolite concentrations reflective of tissue metabolism. However, since these metabolites are found in tissues at very low concentrations, SI is often acquired with limited spatial resolution. In this work, we test the hypothesis that deep learning is able to upscale low resolution SI, together with the T1-weighted (T1w) image, to reconstruct high resolution SI. We report on a novel densely connected UNet (D-UNet) architecture capable of producing super-resolution spectroscopic images. The inputs for the D-UNet are the T1w image and the low resolution SI image while the output is the high resolution SI. The results of the D-UNet are compared both qualitatively and quantitatively to simulated and in vivo high resolution SI. It is found that this deep learning approach can produce high quality spectroscopic images and reconstruct entire 1H spectra from low resolution acquisitions, which can greatly advance the current SI workflow.

Methodological consensus on clinical proton MRS of the brain: Review and recommendations

Proton MRS (1 H MRS) provides noninvasive, quantitative metabolite profiles of tissue and has been shown to aid the clinical management of several brain diseases. Although most modern clinical MR scanners support MRS capabilities, routine use is largely restricted to specialized centers with good access to MR research support. Widespread adoption has been slow for several reasons, and technical challenges toward obtaining reliable good-quality results have been identified as a contributing factor. Considerable progress has been made by the research community to address many of these challenges, and in this paper a consensus is presented on deficiencies in widely available MRS methodology and validated improvements that are currently in routine use at several clinical research institutions. In particular, the localization error for the PRESS localization sequence was found to be unacceptably high at 3 T, and use of the semi-adiabatic localization by adiabatic selective refocusing sequence is a recommended solution. Incorporation of simulated metabolite basis sets into analysis routines is recommended for reliably capturing the full spectral detail available from short TE acquisitions. In addition, the importance of achieving a highly homogenous static magnetic field (B0 ) in the acquisition region is emphasized, and the limitations of current methods and hardware are discussed. Most recommendations require only software improvements, greatly enhancing the capabilities of clinical MRS on existing hardware. Implementation of these recommendations should strengthen current clinical applications and advance progress toward developing and validating new MRS biomarkers for clinical use.

Projection onto Epigraph Sets for Rapid Self-Tuning Compressed Sensing MRI

The compressed sensing (CS) framework leverages the sparsity of MR images to reconstruct from the undersampled acquisitions. CS reconstructions involve one or more regularization parameters that weigh sparsity in transform domains against fidelity to acquired data. While parameter selection is critical for reconstruction quality, the optimal parameters are subject and dataset specific. Thus, commonly practiced heuristic parameter selection generalizes poorly to independent datasets. Recent studies have proposed to tune parameters by estimating the risk of removing significant image coefficients. Line searches are performed across the parameter space to identify the parameter value that minimizes this risk. Although effective, these line searches yield prolonged reconstruction times. Here, we propose a new self-tuning CS method that uses computationally efficient projections onto epigraph sets of the l₁ and total-variation norms to simultaneously achieve parameter selection and regularization. In vivo demonstrations are provided for balanced steady-state free precession, time-of-flight, and T1-weighted imaging. The proposed method achieves an order of magnitude improvement in computational efficiency over line-search methods while maintaining near-optimal parameter selection.

Water removal in MR spectroscopic imaging with L2 regularization

PURPOSE

An L2-regularization based postprocessing method is proposed and tested for removal of residual or unsuppressed water signals in proton MR spectroscopic imaging (MRSI) data recorded from the human brain at 3T.

METHODS

Water signals are removed by implementation of the L2 regularization using a synthesized water-basis matrix that is orthogonal to metabolite signals of interest in the spectral dimension. Simulated spectra with variable water amplitude and in vivo brain MRSI datasets were used to demonstrate the proposed method. Results were compared with two commonly-used postprocessing methods for removing water signals.

RESULTS

The L2 method yielded metabolite signals that were close to true values for the simulated spectra. Residual/unsuppressed water signals in human brain short- and long-echo time MRSI datasets were efficiently removed by the proposed method allowing good quality metabolite maps to be reconstructed with minimized contamination from water signals. Significant differences of the creatine signal were observed between brain long-echo time MRSI without and with water saturation, attributable to the previously described magnetization transfer effect.

CONCLUSIONS

With usage of a synthesized water matrix generated based on reasonable prior knowledge about water and metabolite resonances, the L2 method is shown to be an effective way to remove water signals from MRSI of the human brain.

Fast quantitative susceptibility reconstruction via total field inversion with improved weighted L0 norm approximation

Quantitative susceptibility mapping (QSM) is a meaningful MRI technique owing to its unique relation to actual physical tissue magnetic properties. The reconstruction of QSM is usually decomposed into three sub-problems, which are solved independently. However, this decomposition does not conform to the causes of the problems, and may cause discontinuity of parameters and error accumulation. In this paper, a fast reconstruction method named fast TFI based on total field inversion was proposed. It can accelerate the total field inversion by using a specially selected preconditioner and advanced solution of the weighted L₀ regularization. Due to the employment of an effective model, the proposed method can efficiently reconstruct the QSM of brains with lesions, where other methods may encounter problems. Experimental results from simulation and in vivo data verified that the new method has better reconstruction accuracy, faster convergence ability and excellent robustness, which may promote clinical application of QSM.

Fast high-resolution brain metabolite mapping on a clinical 3T MRI by accelerated 1 H-FID-MRSI and low-rank constrained reconstruction

PURPOSE

Epitomizing the advantages of ultra short echo time and no chemical shift displacement error, high-resolution-free induction decay magnetic resonance spectroscopic imaging (FID-MRSI) sequences have proven to be highly effective in providing unbiased characterizations of metabolite distributions. However, its merits are often overshadowed in high-resolution settings by reduced signal-to-noise ratios resulting from the smaller voxel volumes procured by extensive phase encoding and the related acquisition times.

METHODS

To address these limitations, we here propose an acquisition and reconstruction scheme that offers both implicit dataset denoising and acquisition acceleration. Specifically, a slice selective high-resolution FID-MRSI sequence was implemented. Spectroscopic datasets were processed to remove fat contamination, and then reconstructed using a total generalized variation (TGV) regularized low-rank model. We further measured reconstruction performance for random undersampled data to assess feasibility of a compressed-sensing SENSE acceleration scheme. Performance of the lipid suppression was assessed using an ad hoc phantom, while that of the low-rank TGV reconstruction model was benchmarked using simulated MRSI data. To assess real-world performance, 2D FID-MRSI acquisitions of the brain in healthy volunteers were reconstructed using the proposed framework.

RESULTS

Results from the phantom and simulated data demonstrate that skull lipid contamination is effectively removed and that data reconstruction quality is improved with the low-rank TGV model. Also, we demonstrated that the presented acquisition and reconstruction methods are compatible with a compressed-sensing SENSE acceleration scheme.

CONCLUSIONS

An original reconstruction pipeline for 2D 1 H-FID-MRSI datasets was presented that places high-resolution metabolite mapping on 3T MR scanners within clinically feasible limits.

Quantitative susceptibility mapping (QSM) with an extended physical model for MRI frequency contrast in the brain: a proof-of-concept of quantitative susceptibility and residual (QUASAR) mapping

Quantitative susceptibility mapping (QSM) aims to calculate the tissue's magnetic susceptibility distribution from its perturbing effect on the MRI static main magnetic field. The method is increasingly being applied to study iron and myelin in clinical and preclinical settings. However, recent experimental and theoretical findings have challenged the fundamental theoretical assumptions that form the basis of current numerical implementations of QSM algorithms. The present work introduces a new class of susceptibility mapping algorithms, termed quantitative susceptibility and residual mapping (QUASAR), which takes into account frequency contributions not related to the spatial variation of bulk magnetic susceptibility in the Lorentz sphere model. We present a simple proof-of-concept QUASAR algorithm that, unlike most of the QSM algorithms currently used widely, results in an improved anatomical accuracy of the susceptibility distribution without any a priori assumptions about the susceptibility distribution during the field-to-source inversion. The algorithm was evaluated both in silico and in vivo in the preclinical setting. Our preliminary application of QUASAR in rodents provides the first in vivo evidence that the susceptibility-field model traditionally used in the QSM field cannot fully explain the frequency contrast in brain tissues. Only when an additional local frequency contribution is added to the physical model can the frequency contrast in the brain be related properly to the underlying anatomy.

Over-discretized SENSE reconstruction and B0 correction for accelerated non-lipid-suppressed 1 H FID MRSI of the human brain at 9.4 T

The aim of this work was to use post-processing methods to improve the data quality of metabolite maps acquired on the human brain at 9.4 T with accelerated acquisition schemes. This was accomplished by combining an improved sensitivity encoding (SENSE) reconstruction with a B₀ correction of spatially over-discretized magnetic resonance spectroscopic imaging (MRSI) data. Since MRSI scans suffer from long scan duration, investigating different acceleration techniques has recently been the focus of several studies. Due to strong B₀ inhomogeneity and strict specific absorption rate (SAR) limitations at ultra-high fields, the use of a low-SAR sequence combined with an acceleration technique that is compatible with dynamic B₀ shim updating is preferable. Hence, in this study, a non-lipid-suppressed ultra-short T_E and T_R¹ H free induction decay MRSI sequence is combined with an in-plane SENSE acceleration technique to obtain high-resolution metabolite maps in a clinically feasible scan time. One of the major issues in applying parallel imaging techniques to non-lipid-suppressed MRSI is the presence of strong lipid aliasing artifacts, which if not thoroughly resolved will hinder the accurate quantification of the metabolites of interest. To achieve a more robust reconstruction, an over-discretized SENSE reconstruction (with direct control over the shape of the spatial response function) was combined with an over-discretized B₀ correction. This method is compared with conventional SENSE reconstruction for seven acceleration schemes on four healthy volunteers. The over-discretized method consistently outperformed conventional SENSE, resulting in an average of 23 ± 1.2% higher signal-to-noise ratio and 8 ± 2.9% less error in the fitting of the N-acetylaspartate signal over a whole brain slice. The highest achievable acceleration factor with the proposed technique was determined to be 4. Finally, using the over-discretized method, high-resolution (97 μL nominal voxel size) metabolite maps can be acquired in 3.75 min at 9.4 T. This enables the acquisition of high-resolution metabolite maps with more spatial coverage at ultra-high fields.

A Bi-Directional LSTM-CNN Model with Attention for Aspect-Level Text Classification

The prevalence that people share their opinions on the products and services in their daily lives on the Internet has generated a large quantity of comment data, which contain great business value. As for comment sentences, they often contain several comment aspects and the sentiment on these aspects are different, which makes it meaningless to give an overall sentiment polarity of the sentence. In this paper, we introduce Attention-based Aspect-level Recurrent Convolutional Neural Network (AARCNN) to analyze the remarks at aspect-level. The model integrates attention mechanism and target information analysis, which enables the model to concentrate on the important parts of the sentence and to make full use of the target information. The model uses bidirectional LSTM (Bi-LSTM) to build the memory of the sentence, and then CNN is applied to extracting attention from memory to get the attentive sentence representation. The model uses aspect embedding to analyze the target information of the representation and finally the model outputs the sentiment polarity through a softmax layer. The model was tested on multi-language datasets, and demonstrated that it has better performance than conventional deep learning methods.

On the sensitivity of quantitative susceptibility mapping for measuring trabecular bone density

Purpose

To develop a methodological framework to simultaneously measure R2* and magnetic susceptibility in trabecularized yellow bone marrow and to investigate the sensitivity of Quantitative Susceptibility Mapping (QSM) for measuring trabecular bone density using a non‐UTE multi‐gradient echo sequence.

Methods

The ankle of 16 healthy volunteers and two patients was scanned using a time‐interleaved multi‐gradient‐echo (TIMGRE) sequence. After field mapping based on water–fat separation methods and background field removal based on the Laplacian boundary value method, three different QSM dipole inversion schemes were implemented. Mean susceptibility values in regions of different trabecular bone density in the calcaneus were compared to the corresponding values in the R2* maps, bone volume to total volume ratios (BV/TV) estimated from high resolution imaging (in 14 subjects), and CT attenuation (in two subjects). In addition, numerical simulations were performed in a simplified trabecular bone model of randomly positioned spherical bone inclusions to verify and compare the scaling of R2* and susceptibility with BV/TV.

Results

Differences in calcaneus trabecularization were well depicted in susceptibility maps, in good agreement with high‐resolution MR and CT images. Simulations and in vivo scans showed a linear relationship of measured susceptibility with BV/TV and R2*. The ankle in vivo results showed a strong linear correlation between susceptibility and R2* (R² = 0.88, p < 0.001) with a slope and intercept of −0.004 and 0.2 ppm, respectively.

Conclusions

A method for multi‐paramteric mapping, including R2*‐mapping and QSM was developed for measuring trabecularized yellow bone marrow, showing good sensitivity of QSM for measuring trabecular bone density.

Density-weighted concentric circle trajectories for high resolution brain magnetic resonance spectroscopic imaging at 7T

PURPOSE

Full-slice magnetic resonance spectroscopic imaging at ≥7 T is especially vulnerable to lipid contaminations arising from regions close to the skull. This contamination can be mitigated by improving the point spread function via higher spatial resolution sampling and k-space filtering, but this prolongs scan times and reduces the signal-to-noise ratio (SNR) efficiency. Currently applied parallel imaging methods accelerate magnetic resonance spectroscopic imaging scans at 7T, but increase lipid artifacts and lower SNR-efficiency further. In this study, we propose an SNR-efficient spatial-spectral sampling scheme using concentric circle echo planar trajectories (CONCEPT), which was adapted to intrinsically acquire a Hamming-weighted k-space, thus termed density-weighted-CONCEPT. This minimizes voxel bleeding, while preserving an optimal SNR.

THEORY AND METHODS

Trajectories were theoretically derived and verified in phantoms as well as in the human brain via measurements of five volunteers (single-slice, field-of-view 220 × 220 mm2 , matrix 64 × 64, scan time 6 min) with free induction decay magnetic resonance spectroscopic imaging. Density-weighted-CONCEPT was compared to (a) the originally proposed CONCEPT with equidistant circles (here termed e-CONCEPT), (b) elliptical phase-encoding, and (c) 5-fold Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration accelerated elliptical phase-encoding.

RESULTS

By intrinsically sampling a Hamming-weighted k-space, density-weighted-CONCEPT removed Gibbs-ringing artifacts and had in vivo +9.5%, +24.4%, and +39.7% higher SNR than e-CONCEPT, elliptical phase-encoding, and the Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration accelerated elliptical phase-encoding (all P < 0.05), respectively, which lead to improved metabolic maps.

CONCLUSION

Density-weighted-CONCEPT provides clinically attractive full-slice high-resolution magnetic resonance spectroscopic imaging with optimal SNR at 7T. Magn Reson Med 79:2874-2885, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Metabolite-cycled density-weighted concentric rings k-space trajectory (DW-CRT) enables high-resolution 1 H magnetic resonance spectroscopic imaging at 3-Tesla

Magnetic resonance spectroscopic imaging (MRSI) is a promising technique in both experimental and clinical settings. However, to date, MRSI has been hampered by prohibitively long acquisition times and artifacts caused by subject motion and hardware-related frequency drift. In the present study, we demonstrate that density weighted concentric ring trajectory (DW-CRT) k-space sampling in combination with semi-LASER excitation and metabolite-cycling enables high-resolution MRSI data to be rapidly acquired at 3 Tesla. Single-slice full-intensity MRSI data (short echo time (TE) semi-LASER TE = 32 ms) were acquired from 6 healthy volunteers with an in-plane resolution of 5 × 5 mm in 13 min 30 sec using this approach. Using LCModel analysis, we found that the acquired spectra allowed for the mapping of total N-acetylaspartate (median Cramer-Rao Lower Bound [CRLB] = 3%), glutamate+glutamine (8%), and glutathione (13%). In addition, we demonstrate potential clinical utility of this technique by optimizing the TE to detect 2-hydroxyglutarate (long TE semi-LASER, TE = 110 ms), to produce relevant high-resolution metabolite maps of grade III IDH-mutant oligodendroglioma in a single patient. This study demonstrates the potential utility of MRSI in the clinical setting at 3 Tesla.

Morphology-adaptive total variation for the reconstruction of quantitative susceptibility map from the magnetic resonance imaging phase

Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging technique that quantifies the magnetic susceptibility distribution within biological tissues. QSM calculates the underlying magnetic susceptibility by deconvolving the tissue magnetic field map with a unit dipole kernel. However, this deconvolution problem is ill-posed. The morphology enabled dipole inversion (MEDI) introduces total variation (TV) to regularize the susceptibility reconstruction. However, MEDI results still contain artifacts near tissue boundaries because MEDI only imposes TV constraint on voxels inside smooth regions. We introduce a Morphology-Adaptive TV (MATV) for improving TV-regularized QSM. The MATV method first classifies imaging target into smooth and nonsmooth regions by thresholding magnitude gradients. In the dipole inversion for QSM, the TV regularization weights are a monotonically decreasing function of magnitude gradients. Thus, voxels inside smooth regions are assigned with larger weights than those in nonsmooth regions. Using phantom and in vivo datasets, we compared the performance of MATV with that of MEDI. MATV results had better visual quality than MEDI results, especially near tissue boundaries. Preliminary brain imaging results illustrated that MATV has potential to improve the reconstruction of regions near tissue boundaries.

Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge

PURPOSE

The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data faithfully.

METHODS

Gradient-echo images of a healthy volunteer acquired at 3T in a single orientation with 1.06 mm isotropic resolution. A reference susceptibility map was provided, which was computed using the susceptibility tensor imaging algorithm on data acquired at 12 head orientations. Susceptibility maps calculated from the single orientation data were compared against the reference susceptibility map. Deviations were quantified using the following metrics: root mean squared error (RMSE), structure similarity index (SSIM), high-frequency error norm (HFEN), and the error in selected white and gray matter regions.

RESULTS

Twenty-seven submissions were evaluated. Most of the best scoring approaches estimated the spatial frequency content in the ill-conditioned domain of the dipole kernel using compressed sensing strategies. The top 10 maps in each category had similar error metrics but substantially different visual appearance.

CONCLUSION

Because QSM algorithms were optimized to minimize error metrics, the resulting susceptibility maps suffered from over-smoothing and conspicuity loss in fine features such as vessels. As such, the challenge highlighted the need for better numerical image quality criteria. Magn Reson Med 79:1661-1673, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Density-weighted concentric rings k-space trajectory for 1 H magnetic resonance spectroscopic imaging at 7 T

It has been shown that density-weighted (DW) k-space sampling with spiral and conventional phase encoding trajectories reduces spatial side lobes in magnetic resonance spectroscopic imaging (MRSI). In this study, we propose a new concentric ring trajectory (CRT) for DW-MRSI that samples k-space with a density that is proportional to a spatial, isotropic Hanning window. The properties of two different DW-CRTs were compared against a radially equidistant (RE) CRT and an echo-planar spectroscopic imaging (EPSI) trajectory in simulations, phantoms and in vivo experiments. These experiments, conducted at 7 T with a fixed nominal voxel size and matched acquisition times, revealed that the two DW-CRT designs improved the shape of the spatial response function by suppressing side lobes, also resulting in improved signal-to-noise ratio (SNR). High-quality spectra were acquired for all trajectories from a specific region of interest in the motor cortex with an in-plane resolution of 7.5 × 7.5 mm2 in 8 min 3 s. Due to hardware limitations, high-spatial-resolution spectra with an in-plane resolution of 5 × 5 mm2 and an acquisition time of 12 min 48 s were acquired only for the RE and one of the DW-CRT trajectories and not for EPSI. For all phantom and in vivo experiments, DW-CRTs resulted in the highest SNR. The achieved in vivo spectral quality of the DW-CRT method allowed for reliable metabolic mapping of eight metabolites including N-acetylaspartylglutamate, γ-aminobutyric acid and glutathione with Cramér-Rao lower bounds below 50%, using an LCModel analysis. Finally, high-quality metabolic mapping of a whole brain slice using DW-CRT was achieved with a high in-plane resolution of 5 × 5 mm2 in a healthy subject. These findings demonstrate that our DW-CRT MRSI technique can perform robustly on MRI systems and within a clinically feasible acquisition time.

Fast 3D rosette spectroscopic imaging of neocortical abnormalities at 3 T: Assessment of spectral quality

PURPOSE

To use a fast 3D rosette spectroscopic imaging acquisition to quantitatively evaluate how spectral quality influences detection of the endogenous variation of gray and white matter metabolite differences in controls, and demonstrate how rosette spectroscopic imaging can detect metabolic dysfunction in patients with neocortical abnormalities.

METHODS

Data were acquired on a 3T MR scanner and 32-channel head coil, with rosette spectroscopic imaging covering a 4-cm slab of fronto-parietal-temporal lobes. The influence of acquisition parameters and filtering on spectral quality and sensitivity to tissue composition was assessed by LCModel analysis, the Cramer-Rao lower bound, and the standard errors from regression analyses. The optimized protocol was used to generate normative white and gray matter regressions and evaluate three patients with neocortical abnormalities.

RESULTS

As a measure of the sensitivity to detect abnormalities, the standard errors of regression for Cr/NAA and Ch/NAA were significantly correlated with the Cramer-Rao lower bound values (R = 0.89 and 0.92, respectively, both with P < 0.001). The rosette acquisition with a duration of 9.6 min, produces a mean Cramer-Rao lower bound (%) over the entire slab of 4.6 ± 2.6 and 5.8 ± 2.3 for NAA and Cr, respectively. This enables a Cr/NAA standard error of 0.08 (i.e., detection sensitivity of 25% for a 50/50 mixed gray and white matter voxel). In healthy controls, the regression of Cr/NAA versus fraction gray matter in the cingulate differs from frontal and parietal regions.

CONCLUSIONS

Fast rosette spectroscopic imaging acquisitions with regression analyses are able to identify metabolic differences across 4-cm slabs of the brain centrally and over the cortical periphery with high efficiency, generating results that are consistent with clinical findings. Magn Reson Med 79:2470-2480, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Non-water-suppressed short-echo-time magnetic resonance spectroscopic imaging using a concentric ring k-space trajectory

Water-suppressed MRS acquisition techniques have been the standard MRS approach used in research and for clinical scanning to date. The acquisition of a non-water-suppressed MRS spectrum is used for artefact correction, reconstruction of phased-array coil data and metabolite quantification. Here, a two-scan metabolite-cycling magnetic resonance spectroscopic imaging (MRSI) scheme that does not use water suppression is demonstrated and evaluated. Specifically, the feasibility of acquiring and quantifying short-echo (TE  = 14 ms), two-dimensional stimulated echo acquisition mode (STEAM) MRSI spectra in the motor cortex is demonstrated on a 3 T MRI system. The increase in measurement time from the metabolite-cycling is counterbalanced by a time-efficient concentric ring k-space trajectory. To validate the technique, water-suppressed MRSI acquisitions were also performed for comparison. The proposed non-water-suppressed metabolite-cycling MRSI technique was tested for detection and correction of resonance frequency drifts due to subject motion and/or hardware instability, and the feasibility of high-resolution metabolic mapping over a whole brain slice was assessed. Our results show that the metabolite spectra and estimated concentrations are in agreement between non-water-suppressed and water-suppressed techniques. The achieved spectral quality, signal-to-noise ratio (SNR) > 20 and linewidth <7 Hz allowed reliable metabolic mapping of five major brain metabolites in the motor cortex with an in-plane resolution of 10 × 10 mm2 in 8 min and with a Cramér-Rao lower bound of less than 20% using LCModel analysis. In addition, the high SNR of the water peak of the non-water-suppressed technique enabled voxel-wise single-scan frequency, phase and eddy current correction. These findings demonstrate that our non-water-suppressed metabolite-cycling MRSI technique can perform robustly on 3 T MRI systems and within a clinically feasible acquisition time.

Overview of quantitative susceptibility mapping

Magnetic susceptibility describes the magnetizability of a material to an applied magnetic field and represents an important parameter in the field of MRI. With the recently introduced method of quantitative susceptibility mapping (QSM) and its conceptual extension to susceptibility tensor imaging (STI), the non-invasive assessment of this important physical quantity has become possible with MRI. Both methods solve the ill-posed inverse problem to determine the magnetic susceptibility from local magnetic fields. Whilst QSM allows the extraction of the spatial distribution of the bulk magnetic susceptibility from a single measurement, STI enables the quantification of magnetic susceptibility anisotropy, but requires multiple measurements with different orientations of the object relative to the main static magnetic field. In this review, we briefly recapitulate the fundamental theoretical foundation of QSM and STI, as well as computational strategies for the characterization of magnetic susceptibility with MRI phase data. In the second part, we provide an overview of current methodological and clinical applications of QSM with a focus on brain imaging. Copyright © 2016 John Wiley & Sons, Ltd.

Single-step quantitative susceptibility mapping with variational penalties

Quantitative susceptibility mapping (QSM) estimates the underlying tissue magnetic susceptibility from the gradient echo (GRE) phase signal through background phase removal and dipole inversion steps. Each of these steps typically requires the solution of an ill-posed inverse problem and thus necessitates additional regularization. Recently developed single-step QSM algorithms directly relate the unprocessed GRE phase to the unknown susceptibility distribution, thereby requiring the solution of a single inverse problem. In this work, we show that such a holistic approach provides susceptibility estimation with artifact mitigation and develop efficient algorithms that involve simple analytical solutions for all of the optimization steps. Our methods employ total variation (TV) and total generalized variation (TGV) to jointly perform the background removal and dipole inversion in a single step. Using multiple spherical mean value (SMV) kernels of varying radii permits high-fidelity background removal whilst retaining the phase information in the cortex. Using numerical simulations, we demonstrate that the proposed single-step methods reduce the reconstruction error by up to 66% relative to the multi-step methods that involve SMV background filtering with the same number of SMV kernels, followed by TV- or TGV-regularized dipole inversion. In vivo single-step experiments demonstrate a dramatic reduction in dipole streaking artifacts and improved homogeneity of image contrast. These acquisitions employ the rapid three-dimensional echo planar imaging (3D EPI) and Wave-CAIPI (controlled aliasing in parallel imaging) trajectories for signal-to-noise ratio-efficient whole-brain imaging. Herein, we also demonstrate the multi-echo capability of the Wave-CAIPI sequence for the first time, and introduce an automated, phase-sensitive coil sensitivity estimation scheme based on a 4-s calibration acquisition. Copyright © 2016 John Wiley & Sons, Ltd.

Improved adaptive reconstruction of multichannel MR images

PURPOSE

To improve adaptive reconstruction of multichannel MR images by simultaneously removing nonsmooth phase and signal-loss imaging artifacts.

METHODS

The improved adaptive reconstruction consists of three steps: (1) modified multichannel images are first derived by dividing raw multichannel images by a reference image (i.e., a normalized single-channel image); (2) the modified multichannel images are smoothed by a low-pass filter; (3) adaptive spatial matched filters determined from the smoothed multichannel images are utilized to obtain multichannel combined images. Numerical simulations, as well as MRI experiments, on phantoms and human subjects are performed to evaluate and compare the effectiveness of this improved adaptive reconstruction approach against traditional coil combination methods.

RESULTS

Both simulation and MRI experimental results demonstrated that the proposed improved adaptive reconstruction method is able to obtain combined images with reduced nonsmooth phase and signal-loss imaging artifacts.

CONCLUSIONS

A novel multichannel image reconstruction method is developed that produces high quality multichannel combined images.

Rapid reconstruction of quantitative susceptibility mapping via improved ℓ0 norm approximation

Quantitative susceptibility mapping (QSM) reconstruction is a well-known ill-posed problem. Various regularization techniques have been proposed for solving this problem. In this paper, a rapid method is proposed that uses ℓ₀ norm minimization in a gradient domain. Because ℓ₀ minimization is an NP-hard problem, a special alternating optimization strategy is employed to simplify the reconstruction algorithm. The proposed algorithm uses only simple point-wise multiplications and thresholding operations, and significantly speeds up the calculation. Both numerical simulations and in vivo experiments demonstrate that the proposed method can reconstruct susceptibility fast and accurately. Because morphology information weighted methods have achieved considerable success in QSM, we performed a quantitative comparison with some typical weighted methods, such as MEDI (morphology enabled dipole inversion), iLSQR (improved least squares algorithm), and wℓ₁ (weighted ℓ₁ norm minimization). The reconstructed results show that the proposed method can provide accurate results with a satisfactory speed.

MRI tools for assessment of microstructure and nephron function of the kidney

MRI can provide excellent detail of renal structure and function. Recently, novel MR contrast mechanisms and imaging tools have been developed to evaluate microscopic kidney structures including the tubules and glomeruli. Quantitative MRI can assess local tubular function and is able to determine the concentrating mechanism of the kidney noninvasively in real time. Measuring single nephron function is now a near possibility. In parallel to advancing imaging techniques for kidney microstructure is a need to carefully understand the relationship between the local source of MRI contrast and the underlying physiological change. The development of these imaging markers can impact the accurate diagnosis and treatment of kidney disease. This study reviews the novel tools to examine kidney microstructure and local function and demonstrates the application of these methods in renal pathophysiology.

Quantitative Susceptibility Mapping Using Structural Feature Based Collaborative Reconstruction (SFCR) in the Human Brain

The reconstruction of MR quantitative susceptibility mapping (QSM) from local phase measurements is an ill posed inverse problem and different regularization strategies incorporating a priori information extracted from magnitude and phase images have been proposed. However, the anatomy observed in magnitude and phase images does not always coincide spatially with that in susceptibility maps, which could give erroneous estimation in the reconstructed susceptibility map. In this paper, we develop a structural feature based collaborative reconstruction (SFCR) method for QSM including both magnitude and susceptibility based information. The SFCR algorithm is composed of two consecutive steps corresponding to complementary reconstruction models, each with a structural feature based l 1 norm constraint and a voxel fidelity based l 2 norm constraint, which allows both the structure edges and tiny features to be recovered, whereas the noise and artifacts could be reduced. In the M-step, the initial susceptibility map is reconstructed by employing a k -space based compressed sensing model incorporating magnitude prior. In the S-step, the susceptibility map is fitted in spatial domain using weighted constraints derived from the initial susceptibility map from the M-step. Simulations and in vivo human experiments at 7T MRI show that the SFCR method provides high quality susceptibility maps with improved RMSE and MSSIM. Finally, the susceptibility values of deep gray matter are analyzed in multiple head positions, with the supine position most approximate to the gold standard COSMOS result.

Iron in Multiple Sclerosis and Its Noninvasive Imaging with Quantitative Susceptibility Mapping

Iron is considered to play a key role in the development and progression of Multiple Sclerosis (MS). In particular, iron that accumulates in myeloid cells after the blood-brain barrier (BBB) seals may contribute to chronic inflammation, oxidative stress and eventually neurodegeneration. Magnetic resonance imaging (MRI) is a well-established tool for the non-invasive study of MS. In recent years, an advanced MRI method, quantitative susceptibility mapping (QSM), has made it possible to study brain iron through in vivo imaging. Moreover, immunohistochemical investigations have helped defining the lesional and cellular distribution of iron in MS brain tissue. Imaging studies in MS patients and of brain tissue combined with histological studies have provided important insights into the role of iron in inflammation and neurodegeneration in MS.

Iron in Multiple Sclerosis and Its Noninvasive Imaging with Quantitative Susceptibility Mapping

Iron is considered to play a key role in the development and progression of Multiple Sclerosis (MS). In particular, iron that accumulates in myeloid cells after the blood-brain barrier (BBB) seals may contribute to chronic inflammation, oxidative stress and eventually neurodegeneration. Magnetic resonance imaging (MRI) is a well-established tool for the non-invasive study of MS. In recent years, an advanced MRI method, quantitative susceptibility mapping (QSM), has made it possible to study brain iron through in vivo imaging. Moreover, immunohistochemical investigations have helped defining the lesional and cellular distribution of iron in MS brain tissue. Imaging studies in MS patients and of brain tissue combined with histological studies have provided important insights into the role of iron in inflammation and neurodegeneration in MS.

Foundations of MRI phase imaging and processing for Quantitative Susceptibility Mapping (QSM)

Quantitative Susceptibility Mapping (QSM) is a novel MRI based technique that relies on estimates of the magnetic field distribution in the tissue under examination. Several sophisticated data processing steps are required to extract the magnetic field distribution from raw MRI phase measurements. The objective of this review article is to provide a general overview and to discuss several underlying assumptions and limitations of the pre-processing steps that need to be applied to MRI phase data before the final field-to-source inversion can be performed. Beginning with the fundamental relation between MRI signal and tissue magnetic susceptibility this review covers the reconstruction of magnetic field maps from multi-channel phase images, background field correction, and provides an overview of state of the art QSM solution strategies.

Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker

In MRI, the main magnetic field polarizes the electron cloud of a molecule, generating a chemical shift for observer protons within the molecule and a magnetic susceptibility inhomogeneity field for observer protons outside the molecule. The number of water protons surrounding a molecule for detecting its magnetic susceptibility is vastly greater than the number of protons within the molecule for detecting its chemical shift. However, the study of tissue magnetic susceptibility has been hindered by poor molecular specificities of hitherto used methods based on MRI signal phase and T2* contrast, which depend convolutedly on surrounding susceptibility sources. Deconvolution of the MRI signal phase can determine tissue susceptibility but is challenged by the lack of MRI signal in the background and by the zeroes in the dipole kernel. Recently, physically meaningful regularizations, including the Bayesian approach, have been developed to enable accurate quantitative susceptibility mapping (QSM) for studying iron distribution, metabolic oxygen consumption, blood degradation, calcification, demyelination, and other pathophysiological susceptibility changes, as well as contrast agent biodistribution in MRI. This paper attempts to summarize the basic physical concepts and essential algorithmic steps in QSM, to describe clinical and technical issues under active development, and to provide references, codes, and testing data for readers interested in QSM.

Quantitative susceptibility mapping of human brain at 3T: a multisite reproducibility study

BACKGROUND AND PURPOSE

Quantitative susceptibility mapping of the human brain has demonstrated strong potential in examining iron deposition, which may help in investigating possible brain pathology. This study assesses the reproducibility of quantitative susceptibility mapping across different imaging sites.

MATERIALS AND METHODS

In this study, the susceptibility values of 5 regions of interest in the human brain were measured on 9 healthy subjects following calibration by using phantom experiments. Each of the subjects was imaged 5 times on 1 scanner with the same procedure repeated on 3 different 3T systems so that both within-site and cross-site quantitative susceptibility mapping precision levels could be assessed. Two quantitative susceptibility mapping algorithms, similar in principle, one by using iterative regularization (iterative quantitative susceptibility mapping) and the other with analytic optimal solutions (deterministic quantitative susceptibility mapping), were implemented, and their performances were compared.

RESULTS

Results show that while deterministic quantitative susceptibility mapping had nearly 700 times faster computation speed, residual streaking artifacts seem to be more prominent compared with iterative quantitative susceptibility mapping. With quantitative susceptibility mapping, the putamen, globus pallidus, and caudate nucleus showed smaller imprecision on the order of 0.005 ppm, whereas the red nucleus and substantia nigra, closer to the skull base, had a somewhat larger imprecision of approximately 0.01 ppm. Cross-site errors were not significantly larger than within-site errors. Possible sources of estimation errors are discussed.

CONCLUSIONS

The reproducibility of quantitative susceptibility mapping in the human brain in vivo is regionally dependent, and the precision levels achieved with quantitative susceptibility mapping should allow longitudinal and multisite studies such as aging-related changes in brain tissue magnetic susceptibility.

Wave-CAIPI for highly accelerated 3D imaging

PURPOSE

To introduce the wave-CAIPI (controlled aliasing in parallel imaging) acquisition and reconstruction technique for highly accelerated 3D imaging with negligible g-factor and artifact penalties.

METHODS

The wave-CAIPI 3D acquisition involves playing sinusoidal gy and gz gradients during the readout of each kx encoding line while modifying the 3D phase encoding strategy to incur interslice shifts as in 2D-CAIPI acquisitions. The resulting acquisition spreads the aliasing evenly in all spatial directions, thereby taking full advantage of 3D coil sensitivity distribution. By expressing the voxel spreading effect as a convolution in image space, an efficient reconstruction scheme that does not require data gridding is proposed. Rapid acquisition and high-quality image reconstruction with wave-CAIPI is demonstrated for high-resolution magnitude and phase imaging and quantitative susceptibility mapping.

RESULTS

Wave-CAIPI enables full-brain gradient echo acquisition at 1 mm isotropic voxel size and R = 3 × 3 acceleration with maximum g-factors of 1.08 at 3T and 1.05 at 7T. Relative to the other advanced Cartesian encoding strategies (2D-CAIPI and bunched phase encoding) wave-CAIPI yields up to two-fold reduction in maximum g-factor for nine-fold acceleration at both field strengths.

CONCLUSION

Wave-CAIPI allows highly accelerated 3D acquisitions with low artifact and negligible g-factor penalties, and may facilitate clinical application of high-resolution volumetric imaging.

Quantification of cerebral perfusion using dynamic quantitative susceptibility mapping

PURPOSE

The purpose of this study is to develop a dynamic quantitative susceptibility mapping (QSM) technique with sufficient temporal resolution to map contrast agent concentration in cerebral perfusion imaging.

METHODS

The dynamic QSM used a multiecho three-dimensional (3D) spoiled gradient echo golden angle interleaved spiral sequence during contrast bolus injection. Four-dimensional (4D) space-time resolved magnetic field reconstruction was performed using the temporal resolution acceleration with constrained evolution reconstruction method. Deconvolution of the gadolinium-induced field was performed at each time point with the morphology enabled dipole inversion method to generate a 4D gadolinium concentration map, from which three-dimensional spatial distributions of cerebral blood volume and cerebral blood flow were computed.

RESULTS

Initial in vivo brain imaging demonstrated the feasibility of using dynamic QSM for generating quantitative 4D contrast agent maps and imaging three-dimensional perfusion. The cerebral blood flow obtained with dynamic QSM agreed with that obtained using arterial spin labeling.

CONCLUSION

Dynamic QSM can be used to perform 4D mapping of contrast agent concentration in contrast-enhanced magnetic resonance imaging. The perfusion parameters derived from this 4D contrast agent concentration map were in good agreement with those obtained using arterial spin labeling.