Enhanced spectral resolution for correlated spectroscopic imaging using inner-product and covariance transform: a pilot analysis of metabolites and lipids in breast cancer in vivo.

Acquisition duration of correlated spectroscopy in vivo can be longer due to a large number of t1 increments along the indirect (F1) dimension. Limited number of t1 increments on the other hand leads to poor spectral resolution along F1. Covariance transformation (CT) instead of Fourier transform along t1 is an alternative way of increasing the resolution of the 2D COSY spectrum. Prospectively undersampled five-dimensional echo-planar correlated spectroscopic imaging (EP-COSI) data from ten malignant patients and ten healthy women were acquired and reconstructed using compressed sensing. The COSY spectrum at each voxel location was then generated using FFT, CT and a variant of CT called Inner Product (IP). Metabolite and lipid ratios were computed with respect to water from unsuppressed one-dimensional spectrum. The effects of t1-ridging artifacts commonly seen with FFT were not observed with CT/IP. Statistically significant differences were observed in the fat cross peaks measured with CT/IP/FFT. Spectral resolution was increased ~ 8.5 times (~ 19.53 Hz in FFT, ~ 2.32 Hz in CT/IP) without affecting the spectral width along F1 was possible with CT/IP. CT and IP enabled substantially increased F1 resolution effectively with significant gain in scan time and reliable measure of unsaturation index as a biomarker for malignant breast cancer.

Ensemble Learning for Breast Cancer Lesion Classification: A Pilot Validation Using Correlated Spectroscopic Imaging and Diffusion-Weighted Imaging.

The main objective of this work was to evaluate the application of individual and ensemble machine learning models to classify malignant and benign breast masses using features from two-dimensional (2D) correlated spectroscopy spectra extracted from five-dimensional echo-planar correlated spectroscopic imaging (5D EP-COSI) and diffusion-weighted imaging (DWI). Twenty-four different metabolite and lipid ratios with respect to diagonal fat peaks (1.4 ppm, 5.4 ppm) from 2D spectra, and water and fat peaks (4.7 ppm, 1.4 ppm) from one-dimensional non-water-suppressed (NWS) spectra were used as the features. Additionally, water fraction, fat fraction and water-to-fat ratios from NWS spectra and apparent diffusion coefficients (ADC) from DWI were included. The nine most important features were identified using recursive feature elimination, sequential forward selection and correlation analysis. XGBoost (AUC: 93.0%, Accuracy: 85.7%, F1-score: 88.9%, Precision: 88.2%, Sensitivity: 90.4%, Specificity: 84.6%) and GradientBoost (AUC: 94.3%, Accuracy: 89.3%, F1-score: 90.7%, Precision: 87.9%, Sensitivity: 94.2%, Specificity: 83.4%) were the best-performing models. Conventional biomarkers like choline, myo-Inositol, and glycine were statistically significant predictors. Key features contributing to the classification were ADC, 2D diagonal peaks at 0.9 ppm, 2.1 ppm, 3.5 ppm, and 5.4 ppm, cross peaks between 1.4 and 0.9 ppm, 4.3 and 4.1 ppm, 2.3 and 1.6 ppm, and the triglyceryl-fat cross peak. The results highlight the contribution of the 2D spectral peaks to the model, and they demonstrate the potential of 5D EP-COSI for early breast cancer detection.

Alterations of gray and white matter volumes and cortical thickness in treated HIV-positive patients.

Brain structural changes in HIV identified by voxel-based morphometry (VBM) alone could arise from a variety of causes that are difficult to distinguish without further information, such as cortical thickness (CT), gyrification index (GI) or sulcal depth (SD). Hence, our goal was to assess these additional metrics in HIV using high-resolution 3D T1-weighted images and investigate if surface-based morphometric (SBM) analysis would reveal significant changes in the gray matter (GM) and white matter (WM) volumes combined with alterations in cortical thickness (CT), gyrification index (GI), sulcal depth (SD). T1-w magnetization-prepared-rapid-acquisition gradient-echo (MP-RAGE) scans were acquired in 27 HIV-infected individuals on antiretroviral therapy (ART) and 15 HIV-uninfected healthy controls using a 3T MRI scanner equipped with a 16-channel head "receive" and a quadrature body "transmit" coil. Voxel-based and surface-based morphometric analyses were performed using the MATLAB based SPM Computational Anatomy Toolbox (CAT12.7(1700)). HIV-infected patients showed significantly altered GM and WM volumes, CT, GI, and SD, in multiple brain regions. This study showed the association of altered GM and WM volumes in local brain regions with the changes in region-wise CT, GI and SD measures of HIV-infected patients, especially in the parahippocampal and middle frontal regions as compared to uninfected healthy controls. The outcome of this study suggests that the findings of VBM may not necessarily indicate the volumetric shrinkage or increase alone, but might also be due to altered CT, GI, or SD. Correlation analysis showed a significantly accelerated gray matter loss with age in HIV-infected individuals compared to uninfected healthy controls.

Dictionary learning compressed sensing reconstruction: pilot validation of accelerated echo planar J-resolved spectroscopic imaging in prostate cancer.

OBJECTIVES: This study aimed at developing dictionary learning (DL) based compressed sensing (CS) reconstruction for randomly undersampled five-dimensional (5D) MR Spectroscopic Imaging (3D spatial + 2D spectral) data acquired in prostate cancer patients and healthy controls, and test its feasibility at 8x and 12x undersampling factors. MATERIALS AND METHODS: Prospectively undersampled 5D echo-planar J-resolved spectroscopic imaging (EP-JRESI) data were acquired in nine prostate cancer (PCa) patients and three healthy males. The 5D EP-JRESI data were reconstructed using DL and compared with gradient sparsity-based Total Variation (TV) and Perona-Malik (PM) methods. A hybrid reconstruction technique, Dictionary Learning-Total Variation (DLTV), was also designed to further improve the quality of reconstructed spectra. RESULTS: The CS reconstruction of prospectively undersampled (8x and 12x) 5D EP-JRESI data acquired in prostate cancer and healthy subjects were performed using DL, DLTV, TV and PM. It is evident that the hybrid DLTV method can unambiguously resolve 2D J-resolved peaks including myo-inositol, citrate, creatine, spermine and choline. CONCLUSION: Improved reconstruction of the accelerated 5D EP-JRESI data was observed using the hybrid DLTV. Accelerated acquisition of in vivo 5D data with as low as 8.33% samples (12x) corresponds to a total scan time of 14 min as opposed to a fully sampled scan that needs a total duration of 2.4 h (TR = 1.2 s, 32 [Formula: see text]×16 [Formula: see text]×8 [Formula: see text], 512 [Formula: see text] and 64 [Formula: see text]).

Correlated MR spectroscopic imaging of breast cancer to investigate metabolites and lipids: acceleration and compressed sensing reconstruction.

Objectives: The main objective of this work was to detect novel biomarkers in breast cancer by spreading the MR spectra over two dimensions in multiple spatial locations using an accelerated 5D EP-COSI technology. Methods: The 5D EP-COSI data were non-uniformly undersampled with an acceleration factor of 8 and reconstructed using group sparsity-based compressed sensing reconstruction. Different metabolite and lipid ratios were then quantified and statistically analyzed for significance. Linear discriminant models based on the quantified metabolite and lipid ratios were generated. Spectroscopic images of the quantified metabolite and lipid ratios were also reconstructed. Results: The 2D COSY spectra generated using the 5D EP-COSI technique showed differences among healthy, benign, and malignant tissues in terms of their mean values of metabolite and lipid ratios, especially the ratios of potential novel biomarkers based on unsaturated fatty acids, myo-inositol, and glycine. It is further shown the potential of choline and unsaturated lipid ratio maps, generated from the quantified COSY signals across multiple locations in the breast, to serve as complementary markers of malignancy that can be added to the multiparametric MR protocol. Discriminant models using metabolite and lipid ratios were found to be statistically significant for classifying benign and malignant tumor from healthy tissues. Conclusions: Accelerated 5D EP-COSI technique demonstrates the potential to detect novel biomarkers such as glycine, myo-inositol, and unsaturated fatty acids in addition to commonly reported choline in breast cancer, and facilitates metabolite and lipid ratio maps which have the potential to play a significant role in breast cancer detection. Advances in knowledge: This study presents the first evaluation of a multidimensional MR spectroscopic imaging technique for the detection of potentially novel biomarkers based on glycine, myo-inositol, and unsaturated fatty acids, in addition to commonly reported choline. Spatial mapping of choline and unsaturated fatty acid ratios with respect to water in malignant and benign breast masses are also shown. These metabolic characteristics may serve as additional biomarkers for improving the diagnostic and therapeutic evaluation of breast cancer.

Compressed sensing MRI using an interpolation-free nonlinear diffusion model.

PURPOSE: Constraints in extended neighborhood system demand the use of a large number of interpolations in directionality-guided compressed-sensing nonlinear diffusion MR image reconstruction technique. This limits its practical application in terms of computational complexity. The proposed method aims at multifold improvement in its runtime without compromising the image quality. THEORY AND METHODS: Conventional approach to extended neighborhood computation requires 108 linear interpolations per pixel for 10 sets of neighborhoods. We propose a neighborhood stretching technique that systematically extends the location of neighboring pixels such that 66% to 100% fewer interpolations are required to compute the gradients along multiple directions. A spatial frequency-based deviation measure is then used to choose the most reliable edges from the set of images generated by diffusion along different directions. RESULTS: The semi-interpolated and interpolation-free diffusion techniques proposed in this paper are compared with the fully interpolated diffusion-based reconstruction by reconstruing multiple multichannel in vivo datasets, undersampled using different sampling patterns at various sampling rates. Results indicate a two- to fivefold increase in reconstruction speed with a potential to generate 1 to 2 dB improvement in peak SNR measure. CONCLUSION: The proposed method outperforms the state-of-the-art fully interpolated diffusion model and generates high-quality reconstructions for different sampling patterns and acceleration factors with a two- to fivefold increment in reconstruction speed. This makes it the most suitable candidate for edge-preserving penalties used in the compressed sensing MRI reconstruction methods.

Directionality guided non linear diffusion compressed sensing MR image reconstruction.

PURPOSE: Address the shortcomings of edge-preserving filters to preserve the complex nature of edges, by adapting the direction of diffusion to the local variations in intensity function on a subpixel level, thereby achieving a reconstruction accuracy superior to that of data-driven learning-based approaches. THEORY AND METHODS: Rate of diffusion for edges is found to vary in accordance with their gradient direction. Therefore, the edge preservation is strongly dependent on the direction in which the gradient is computed. Since the directionality of edges varies at different regions of the image, the proposed technique computes the gradients in all possible angular directions and uses a spatial-frequency-based deviation measure to choose the most reliable edges from the images diffused along different directions. RESULTS: The proposed method is compared with the state-of-the-art data-driven learning-based techniques of block matching and 3D filtering (BM3D), patch-based nonlocal operator (PANO), and dictionary learning MRI (DLMRI). Best results are obtained when directionality of edges is estimated from a prior optimized k-space and shows an improvement in peak signal-to-noise ratio (PSNR) measures by a factor of 2.36 dB, 1.92 dB, and 1.59 dB over BM3D, PANO, and dictionary learning MRI, respectively. CONCLUSION: The proposed technique prevents the emphasis of false edges and better captures the structural details by a locally varying directionality-guided diffusion to make the error lower than that of the state-of-the-art reconstruction techniques. In addition, a highly parallelizable form of the proposed model promises a significant gain in the reconstruction speed for practical implementations.

A mixed-order nonlinear diffusion compressed sensing MR image reconstruction.

PURPOSE: Avoid formation of staircase artifacts in nonlinear diffusion-based MR image reconstruction without compromising computational speed. METHODS: Whereas second-order diffusion encourages the evolution of pixel neighborhood with uniform intensities, fourth-order diffusion considers smooth region to be not necessarily a uniform intensity region but also a planar region. Therefore, a controlled application of fourth-order diffusivity function is used to encourage second-order diffusion to reconstruct the smooth regions of the image as a plane rather than a group of blocks, while not being strong enough to introduce the undesirable speckle effect. RESULTS: Proposed method is compared with second- and fourth-order nonlinear diffusion reconstruction, total variation (TV), total generalized variation, and higher degree TV using in vivo data sets for different undersampling levels with application to dictionary learning-based reconstruction. It is observed that the proposed technique preserves sharp boundaries in the image while preventing the formation of staircase artifacts in the regions of smoothly varying pixel intensities. It also shows reduced error measures compared with second-order nonlinear diffusion reconstruction or TV and converges faster than TV-based methods. CONCLUSION: Because nonlinear diffusion is known to be an effective alternative to TV for edge-preserving reconstruction, the crucial aspect of staircase artifact removal is addressed. Reconstruction is found to be stable for the experimentally determined range of fourth-order regularization parameter, and therefore not does not introduce a parameter search. Hence, the computational simplicity of second-order diffusion is retained.

Multichannel compressed sensing MR image reconstruction using statistically optimized nonlinear diffusion.

PURPOSE: Eliminate the need for parametric tuning in total variation (TV) based multichannel compressed-sensing image reconstruction using statistically optimized nonlinear diffusion without compromising image quality. THEORY AND METHODS: Nonlinear diffusion controls the denoising process using a contrast parameter that separates the gradients corresponding to noise and true edges in the image. This parameter is statistically estimated from the variance of combined image gradient to yield minimum steady-state reconstruction error. In addition, it uses acquired k-space data to bias the diffusion process toward an optimal solution. RESULTS: The proposed method is compared with TV using a set of noisy spine and brain data sets for three, four, and five-fold undersampling. It is observed that the choice of regularization parameter (step size) of TV-based methods requires prior tuning based on an extensive search procedure. In contrast, statistical estimation of contrast parameter removes this need for tuning by adapting to the changes in data sets and undersampling levels. CONCLUSIONS: Although an a-priori tuned TV-based reconstruction can provide a comparable image quality to that of controlled nonlinear diffusion, there are practical limitations with regard to its time complexity for ad-hoc applications to multicoil compressed-sensing reconstruction. Magn Reson Med 78:754-762, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

Improved image reconstruction of low-resolution multichannel phase contrast angiography.

In low-resolution phase contrast magnetic resonance angiography, the maximum intensity projected channel images will be blurred with consequent loss of vascular details. The channel images are enhanced using a stabilized deblurring filter, applied to each channel prior to combining the individual channel images. The stabilized deblurring is obtained by the addition of a nonlocal regularization term to the reverse heat equation, referred to as nonlocally stabilized reverse diffusion filter. Unlike reverse diffusion filter, which is highly unstable and blows up noise, nonlocal stabilization enhances intensity projected parallel images uniformly. Application to multichannel vessel enhancement is illustrated using both volunteer data and simulated multichannel angiograms. Robustness of the filter applied to volunteer datasets is shown using statistically validated improvement in flow quantification. Improved performance in terms of preserving vascular structures and phased array reconstruction in both simulated and real data is demonstrated using structureness measure and contrast ratio.