Phase-correcting non-local means filtering for diffusion-weighted imaging of the spinal cord

MP-PCA denoising of fMRI time-series data can lead to artificial activation "spreading"

MP-PCA denoising has become the method of choice for denoising MRI data since it provides an objective threshold to separate the signal components from unwanted thermal noise components. In rodents, thermal noise in the coils is an important source of noise that can reduce the accuracy of activation mapping in fMRI. Further confounding this problem, vendor data often contains zero-filling and other post-processing steps that may violate MP-PCA assumptions. Here, we develop an approach to denoise vendor data and assess activation "spreading" caused by MP-PCA denoising in rodent task-based fMRI data. Data was obtained from N = 3 mice using conventional multislice and ultrafast acquisitions (1 s and 50 ms temporal resolution, respectively), during visual stimulation. MP-PCA denoising produced SNR gains of 64% and 39% and Fourier Spectral Amplitude (FSA) increases in BOLD maps of 9% and 7% for multislice and ultrafast data, respectively, when using a small [2 2] denoising window. Larger windows provided higher SNR and FSA gains with increased spatial extent of activation that may or may not represent real activation. Simulations showed that MP-PCA denoising can incur activation "spreading" with increased false positive rate and smoother functional maps due to local "bleeding" of principal components, and that the optimal denoising window for improved specificity of functional mapping, based on Dice score calculations, depends on the data's tSNR and functional CNR. This "spreading" effect applies also to another recently proposed low-rank denoising method (NORDIC), although to a lesser degree. Our results bode well for enhancing spatial and/or temporal resolution in future fMRI work, while taking into account the sensitivity/specificity trade-offs of low-rank denoising methods.

Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising

Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout in vivo (i.e. with matched spatial encoding parameters across a range of imaging contrasts). We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via multi-contrast image denoising methods. As a proof-of-concept, here we provide a demonstration with one such method, known as Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a singular value (SV) decomposition truncation approach that relies on redundant acquisitions, i.e. such that the number of measurements is large compared to the number of components that are maintained in the truncated SV decomposition. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient MP-PCA denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 17% for mean kurtosis, 8% for bound pool fraction (myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from multi-contrast denoising methods such as MP-PCA.

Fast submillimeter diffusion MRI using gSlider-SMS and SNR-enhancing joint reconstruction

PURPOSE

We evaluate a new approach for achieving diffusion MRI data with high spatial resolution, large volume coverage, and fast acquisition speed.

THEORY AND METHODS

A recent method called gSlider-SMS enables whole-brain submillimeter diffusion MRI with high signal-to-noise ratio (SNR) efficiency. However, despite the efficient acquisition, the resulting images can still suffer from low SNR due to the small size of the imaging voxels. This work proposes to mitigate the SNR problem by combining gSlider-SMS with a regularized SNR-enhancing reconstruction approach.

RESULTS

Illustrative results show that, from gSlider-SMS data acquired over a span of only 25 minutes on a 3T scanner, the proposed method is able to produce 71 MRI images (64 diffusion encoding orientations with b = 1500 s/ mm 2 , and 7 images without diffusion weighting) of the entire in vivo human brain with nominal 0.66 mm spatial resolution. Using data acquired from 75 minutes of acquisition as a gold standard reference, we demonstrate that the proposed SNR-enhancement procedure leads to substantial improvements in estimated diffusion parameters compared to conventional gSlider reconstruction. Results also demonstrate that the proposed method has advantages relative to denoising methods based on low-rank matrix modeling. A theoretical analysis of the trade-off between spatial resolution and SNR suggests that the proposed approach has high efficiency.

CONCLUSIONS

The combination of gSlider-SMS with advanced regularized reconstruction enables high-resolution quantitative diffusion MRI from a relatively fast acquisition.

Modulating Diffusion-Weighted Magnetic Resonance Imaging for Screening in Oncologic Tertiary Prevention: A Prospective Ex Vivo and In Vivo Study

INTRODUCTION

Diffusion-weighted imaging (DWI) is an important part of oncological magnetic resonance imaging (MRI) examinations, especially for tertiary cancer prevention in terms of early detection of recurrent disease. However, abdominal studies can be challenged by motion artifacts, poor signal-to-noise ratios, and visibility of retroperitoneal structures, which necessitates sequence optimization depending on the investigated region. This study aims at prospectively evaluating an adapted DWI sequence ex vivo and in vivo in oncologic patients undergoing abdominal MRI.

METHODS

This institutional review board-approved, prospective study included phantom measurements, volunteer examinations, and oncologic patient examinations of the abdomen. Fifty-seven MRI examinations in 54 patients (mean age, 58 years; range, 21-90 years) were included into the analysis. The MRI examination were performed at a 1.5 T MRI scanner (MAGNETOM Aera; Siemens Healthcare, Erlangen, Germany) and included both a standard EPI-DWI (s-DWI; b = 50, 900 s/mm) and an adapted DWI (opt-DWI; EPI-DWI with b = 0, 50, 900, 1500 s/mm, acquisition with higher spatial resolution and optimized processing for the abdomen including motion correction, adaptive image combination, and background suppression). For b = 900 s/mm, the ratio of signal intensity in the normal tissue and the standard deviation of the noise in the surrounding air was quantitatively calculated; image quality and tissue differentiation parameters were rated by 2 independent, blinded readers using a 5-point Likert scale. Statistics included Wilcoxon signed-rank test and kappa statistic (P < 0.05/0.0125 after Bonferroni correction).

RESULTS

The DWI phantom demonstrated an optimized contour sharpness and inlay differentiation for opt-DWI. The apparent ratio of normal tissue signal/standard deviation of background noise at b = 900 s/mm of the right/left hemiabdomen was significantly increased in opt-DWI (mean, 71.9 ± 23.5/86.0 ± 43.3) versus s-DWI (mean, 51.4 ± 15.4/63.4 ± 36.5; P < 0.001). Image quality parameters (contour sharpness and tissue differentiation of upper abdominal and retroperitoneal structures) were significantly increased in opt-DWI versus s-DWI (P < 0.001). Interreader reliability test showed good agreement (kappa = 0.768; P < 0.001).

DISCUSSION

This study prospectively evaluated the potential of adapted DWI for screening in tertiary prevention of oncologic patients. An optimized DWI protocol with advanced processing achieved improved image quality in quantitative and qualitative analyses. Oncological optimization of DWI should be performed before its application in cancer patients to improve both screening and follow-up examinations, to better unleash the diagnostic potential of DWI.