Berkin Bilgic

MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping.

Quantifying tissue iron concentration in vivo is instrumental for understanding the role of iron in physiology and in neurological diseases associated with abnormal iron distribution. Herein, we use recently-developed Quantitative Susceptibility Mapping (QSM) methodology to estimate the tissue magnetic susceptibility based on MRI signal phase. To investigate the effect of different regularization choices, we implement and compare ℓ1 and ℓ2 norm regularized QSM algorithms. These regularized approaches solve for the underlying magnetic susceptibility distribution, a sensitive measure of the tissue iron concentration, that gives rise to the observed signal phase. Regularized QSM methodology also involves a pre-processing step that removes, by dipole fitting, unwanted background phase effects due to bulk susceptibility variations between air and tissue and requires data acquisition only at a single field strength. For validation, performances of the two QSM methods were measured against published estimates of regional brain iron from postmortem and in vivo data. The in vivo comparison was based on data previously acquired using Field-Dependent Relaxation Rate Increase (FDRI), an estimate of MRI relaxivity enhancement due to increased main magnetic field strength, requiring data acquired at two different field strengths. The QSM analysis was based on susceptibility-weighted images acquired at 1.5 T, whereas FDRI analysis used Multi-Shot Echo-Planar Spin Echo images collected at 1.5 T and 3.0 T. Both datasets were collected in the same healthy young and elderly adults. The in vivo estimates of regional iron concentration comported well with published postmortem measurements; both QSM approaches yielded the same rank ordering of iron concentration by brain structure, with the lowest in white matter and the highest in globus pallidus. Further validation was provided by comparison of the in vivo measurements, ℓ1-regularized QSM versus FDRI and ℓ2-regularized QSM versus FDRI, which again yielded perfect rank ordering of iron by brain structure. The final means of validation was to assess how well each in vivo method detected known age-related differences in regional iron concentrations measured in the same young and elderly healthy adults. Both QSM methods and FDRI were consistent in identifying higher iron concentrations in striatal and brain stem ROIs (i.e., caudate nucleus, putamen, globus pallidus, red nucleus, and substantia nigra) in the older than in the young group. The two QSM methods appeared more sensitive in detecting age differences in brain stem structures as they revealed differences of much higher statistical significance between the young and elderly groups than did FDRI. However, QSM values are influenced by factors such as the myelin content, whereas FDRI is a more specific indicator of iron content. Hence, FDRI demonstrated higher specificity to iron yet yielded noisier data despite longer scan times and lower spatial resolution than QSM. The robustness, practicality, and demonstrated ability of predicting the change in iron deposition in adult aging suggest that regularized QSM algorithms using single-field-strength data are possible alternatives to tissue iron estimation requiring two field strengths.

Fast quantitative susceptibility mapping with L1-regularization and automatic parameter selection.

To enable fast reconstruction of quantitative susceptibility maps with total variation penalty and automatic regularization parameter selection.

Quantitative oxygenation venography from MRI phase

To demonstrate acquisition and processing methods for quantitative oxygenation venograms that map in vivo oxygen saturation (SvO2) along cerebral venous vasculature.

Multi-contrast Reconstruction With Bayesian Compressed Sensing

Clinical imaging with structural MRI routinely relies on multiple acquisitions of the same region of interest under several different contrast preparations. This work presents a reconstruction algorithm based on Bayesian compressed sensing to jointly reconstruct a set of images from undersampled k‐space data with higher fidelity than when the images are reconstructed either individually or jointly by a previously proposed algorithm, M‐FOCUSS. The joint inference problem is formulated in a hierarchical Bayesian setting, wherein solving each of the inverse problems corresponds to finding the parameters (here, image gradient coefficients) associated with each of the images. The variance of image gradients across contrasts for a single volumetric spatial position is a single hyperparameter. All of the images from the same anatomical region, but with different contrast properties, contribute to the estimation of the hyperparameters, and once they are found, the k‐space data belonging to each image are used independently to infer the image gradients. Thus, commonality of image spatial structure across contrasts is exploited without the problematic assumption of correlation across contrasts. Examples demonstrate improved reconstruction quality (up to a factor of 4 in root‐mean‐square error) compared with previous compressed sensing algorithms and show the benefit of joint inversion under a hierarchical Bayesian model. Magn Reson Med, 2011.

Fast quantitative susceptibility mapping using 3D EPI and total generalized variation

Quantitative susceptibility mapping (QSM) allows new insights into tissue composition and organization by assessing its magnetic property. Previous QSM studies have already demonstrated that magnetic susceptibility is highly sensitive to myelin density and fiber orientation as well as to para- and diamagnetic trace elements. Image resolution in QSM with current approaches is limited by the long acquisition time of 3D scans and the need for high signal to noise ratio (SNR) to solve the dipole inversion problem. We here propose a new total-generalized-variation (TGV) based method for QSM reconstruction, which incorporates individual steps of phase unwrapping, background field removal and dipole inversion in a single iteration, thus yielding a robust solution to the reconstruction problem. This approach has beneficial characteristics for low SNR data, allowing for phase data to be rapidly acquired with a 3D echo planar imaging (EPI) sequence. The proposed method was evaluated with a numerical phantom and in vivo at 3 and 7 T. Compared to total variation (TV), TGV-QSM enforced higher order smoothness which yielded solutions closer to the ground truth and prevented stair-casing artifacts. The acquisition time for images with 1mm isotropic resolution and whole brain coverage was 10s on a clinical 3 Tesla scanner. In conclusion, 3D EPI acquisition combined with single-step TGV reconstruction yields reliable QSM images of the entire brain with 1mm isotropic resolution in seconds. The short acquisition time combined with the robust reconstruction may enable new QSM applications in less compliant populations, clinical susceptibility tensor imaging, and functional resting state examinations.

Wave‐CAIPI for highly accelerated 3D imaging

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.

Efficient integral image computation on the GPU

We present an integral image algorithm that can run in real-time on a Graphics Processing Unit (GPU). Our system exploits the parallelisms in computation via the NIVIDA CUDA programming model, which is a software platform for solving non-graphics problems in a massively parallel high-performance fashion. This implementation makes use of the work-efficient scan algorithm that is explicated elsewhere. Treating the rows and the columns of the target image as independent input arrays for the scan algorithm, our method manages to expose a second level of parallelism in the problem. We compare the performance of the parallel approach running on the GPU with the sequential CPU implementation across a range of image sizes and report a speed up by a factor of 8 for a 4 megapixel input. We further investigate the impact of using packed vector type data on the performance, as well as the effect of double precision arithmetic on the GPU.

Accelerated diffusion spectrum imaging with compressed sensing using adaptive dictionaries

Diffusion spectrum imaging offers detailed information on complex distributions of intravoxel fiber orientations at the expense of extremely long imaging times (∼1 h). Recent work by Menzel et al. demonstrated successful recovery of diffusion probability density functions from sub‐Nyquist sampled q‐space by imposing sparsity constraints on the probability density functions under wavelet and total variation transforms. As the performance of compressed sensing reconstruction depends strongly on the level of sparsity in the selected transform space, a dictionary specifically tailored for diffusion probability density functions can yield higher fidelity results. To our knowledge, this work is the first application of adaptive dictionaries in diffusion spectrum imaging, whereby we reduce the scan time of whole brain diffusion spectrum imaging acquisition from 50 to 17 min while retaining high image quality. In vivo experiments were conducted with the 3T Connectome MRI. The root‐mean‐square error of the reconstructed “missing” diffusion images were calculated by comparing them to a gold standard dataset (obtained from acquiring 10 averages of diffusion images in these missing directions). The root‐mean‐square error from the proposed reconstruction method is up to two times lower than that of Menzel et al.s method and is actually comparable to that of the fully‐sampled 50 minute scan. Comparison of tractography solutions in 18 major white‐matter pathways also indicated good agreement between the fully‐sampled and 3‐fold accelerated reconstructions. Further, we demonstrate that a dictionary trained using probability density functions from a single slice of a particular subject generalizes well to other slices from the same subject, as well as to slices from other subjects. Magn Reson Med, 2012.

Fast image reconstruction with L2‐regularization

We introduce L2‐regularized reconstruction algorithms with closed‐form solutions that achieve dramatic computational speed‐up relative to state of the art L1‐ and L2‐based iterative algorithms while maintaining similar image quality for various applications in MRI reconstruction.

RARE/turbo spin echo imaging with simultaneous multislice Wave-CAIPI

To enable highly accelerated RARE/Turbo Spin Echo (TSE) imaging using Simultaneous MultiSlice (SMS) Wave‐CAIPI acquisition with reduced g‐factor penalty.