Luca Marinelli

Accelerated diffusion spectrum imaging in the human brain using compressed sensing

We developed a novel method to accelerate diffusion spectrum imaging using compressed sensing. The method can be applied to either reduce acquisition time of diffusion spectrum imaging acquisition without losing critical information or to improve the resolution in diffusion space without increasing scan time. Unlike parallel imaging, compressed sensing can be applied to reconstruct a sub‐Nyquist sampled dataset in domains other than the spatial one. Simulations of fiber crossings in 2D and 3D were performed to systematically evaluate the effect of compressed sensing reconstruction with different types of undersampling patterns (random, gaussian, Poisson disk) and different acceleration factors on radial and axial diffusion information. Experiments in brains of healthy volunteers were performed, where diffusion space was undersampled with different sampling patterns and reconstructed using compressed sensing. Essential information on diffusion properties, such as orientation distribution function, diffusion coefficient, and kurtosis is preserved up to an acceleration factor of R = 4. Magn Reson Med, 2011.

128-channel body MRI with a flexible high-density receiver-coil array.

To determine whether the promise of high‐density many‐coil MRI receiver arrays for enabling highly accelerated parallel imaging can be realized in practice.

Accelerated MR imaging using compressive sensing with no free parameters

We describe and evaluate a robust method for compressive sensing MRI reconstruction using an iterative soft thresholding framework that is data‐driven, so that no tuning of free parameters is required. The approach described here combines a Nesterov type optimal gradient scheme for iterative update along with standard wavelet‐based adaptive denoising methods, resulting in a leaner implementation compared with the nonlinear conjugate gradient method. Tests with T2 weighted brain data and vascular 3D phase contrast data show that the image quality of reconstructions is comparable with those from an empirically tuned nonlinear conjugate gradient approach. Statistical analysis of image quality scores for multiple datasets indicates that the iterative soft thresholding approach as presented here may improve the robustness of the reconstruction and the image quality, when compared with nonlinear conjugate gradient that requires manual tuning for each dataset. A data‐driven approach as illustrated in this article should improve future clinical applicability of compressive sensing image reconstruction. Magn Reson Med, 2012.

Local planar gradients with order-of-magnitude strength and speed advantage†

A three‐axis uniplanar gradient coil was designed and built to provide order‐of‐magnitude increases in gradient strength of up to 500 mT/m on the x‐ and y‐axes, and 1000 mT/m for the z‐axis at 640 A input over a limited FOV (∼16 cm) for superficial regions, compared to conventional gradient coils, with significant gradient strengths extending deeper into the body. The gradient set is practically accommodated in the bore of a conventional whole‐body, cylindrical‐geometry MRI scanner, and operated using standard gradient supplies. The design was optimized for gradient linearity over a restricted volume while accounting for the practical problems of torque and heating. Tests at 320 A demonstrated up to 420‐mT/m gradients near the surface at efficiencies of up to 1.4 mT/m/A. A new true 2D gradient‐nonlinearity correction algorithm was developed to rectify gradient nonlinearities and considerably expand the imageable volumes. The gradient system and correction algorithm were implemented in a standard 1.5T scanner and demonstrated by high‐resolution imaging of phantoms and humans. Magn Reson Med 58:134–143, 2007.

Improved correction for gradient nonlinearity effects in diffusion-weighted imaging

To provide an improved correction for gradient nonlinearity (GN) effects in diffusion‐weighted imaging (DWI). These effects produce spatially varying apparent diffusion coefficient (ADC), a result that will be significant in large field‐of‐view imaging, and may be confounded by distortion and concomitant fields related to the DWI acquisition.

Gradient nonlinearity correction to improve apparent diffusion coefficient accuracy and standardization in the american college of radiology imaging network 6698 breast cancer trial

To evaluate a gradient nonlinearity correction (GNC) program for quantitative apparent diffusion coefficient (ADC) measurements on phantom and human subject diffusion‐weighted (DW) magnetic resonance imaging (MRI) scans in a multicenter breast cancer treatment response study

Modified cine inversion recovery pulse sequence for the quantification of myocardial T1 and gadolinium partition coefficient

To optimize and validate a modified cine inversion recovery sequence (MCine‐IR) for myocardial T1 quantification and gadolinium partition coefficient (λGd) estimation at 1.5 T.

Multi‐directional anisotropy from diffusion orientation distribution functions

To evaluate a model‐independent, multi‐directional anisotropy (MDA) metric that is analytically and experimentally equivalent to fractional anisotropy (FA) in single‐direction diffusivity, but potentially superior to FA in its sensitivity to the underlying anisotropy of multi‐directional diffusivity.

Recursive feature elimination for biomarker discovery in resting-state functional connectivity

Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate analysis suffers from the problem of multiple comparisons. Here, we adopt an alternative data-driven method for identifying population differences in functional connectivity. We propose a machine-learning approach to down-select functional connectivity features associated with symptom severity in mild traumatic brain injury (mTBI). Using this approach, we identified functional regions with altered connectivity in mTBI. including the executive control, visual and precuneus networks. We compared functional connections at multiple resolutions to determine which scale would be more sensitive to changes related to patient recovery. These modular network-level features can be used as diagnostic tools for predicting disease severity and recovery profiles.

Joint Super-Resolution Using Only One Anisotropic Low-Resolution Image per q-Space Coordinate

Recently, super-resolution methods for diffusion MRI capable of retrieving high-resolution diffusion-weighted images were proposed, yielding a resolution beyond the scanner hardware limitations. These techniques rely on acquiring either one isotropic or several anisotropic low-resolution versions of each diffusion-weighted image. In the present work, a variational formulation of joint super-resolution of all diffusion-weighted images is presented which takes advantage of interrelations between similar diffusion-weighted images. These interrelations allow to use only one anisotropic low-resolution version of each diffusion-weighted image and to retrieve its missing high-frequency components from other images which have a similar q-space coordinate but a different resolution-anisotropy orientation. An acquisition scheme that entails complementary resolution-anisotropy among neighboring q-space points is introduced. High-resolution images are recovered at reduced scan time requirements compared to state-of-the-art anisotropic super-resolution methods. The introduced principles of joint super-resolution thus have the potential to further improve the performance of super-resolution methods.