Onur Afacan

Functional magnetic resonance imaging in awake animals

Abstract Awake animal imaging is becoming an important tool in behavioral neuroscience and preclinical drug discovery. Non-invasive ultra-high-field, functional magnetic resonance imaging (fMRI) provides a window to the mind, making it possible to image changes in brain activity across distributed, integrated neural circuits with high temporal and spatial resolution. In theory, changes in brain function, anatomy, and chemistry can be recorded in the same animal from early life into old age under stable or changing environmental conditions. This prospective capability of animal imaging to follow changes in brain neurobiology after genetic or environmental insult has great value to the fields of psychiatry and neurology and probably stands as the key advantage of MRI over other methods in the neuroscience toolbox. In addition, awake animal imaging offers the ability to record signal changes across the entire brain in seconds. When combined with the use of 3D segmented, annotated, brain atlases, and computational analysis, it is possible to reconstruct distributed, integrated neural circuits or ‘fingerprints’ of brain activity. These fingerprints can be used to characterize the activity and function of new psychotherapeutics in preclinical development and to study the neurobiology of integrated neural circuits controlling cognition and emotion. In this review, we describe the methods used to image awake animals and the recent advances in the radiofrequency electronics, pulse sequences, and the development of 3D segmented atlases and software for image analysis. Results from pharmacological MRI studies and from studies using provocation paradigms to elicit emotional responses are provided as a small sample of the number of different applications possible with awake animal imaging.

A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth

Longitudinal characterization of early brain growth in-utero has been limited by a number of challenges in fetal imaging, the rapid change in size, shape and volume of the developing brain, and the consequent lack of suitable algorithms for fetal brain image analysis. There is a need for an improved digital brain atlas of the spatiotemporal maturation of the fetal brain extending over the key developmental periods. We have developed an algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age. We applied this new algorithm to construct a spatiotemporal atlas from MRI of 81 normal fetuses scanned between 19 and 39 weeks of gestation and labeled the structures of the developing brain. We evaluated the use of this atlas and additional individual fetal brain MRI atlases for completely automatic multi-atlas segmentation of fetal brain MRI. The atlas is available online as a reference for anatomy and for registration and segmentation, to aid in connectivity analysis, and for groupwise and longitudinal analysis of early brain growth.

Temporal slice registration and robust diffusion-tensor reconstruction for improved fetal brain structural connectivity analysis

&NA; Diffusion weighted magnetic resonance imaging, or DWI, is one of the most promising tools for the analysis of neural microstructure and the structural connectome of the human brain. The application of DWI to map early development of the human connectome in‐utero, however, is challenged by intermittent fetal and maternal motion that disrupts the spatial correspondence of data acquired in the relatively long DWI acquisitions. Fetuses move continuously during DWI scans. Reliable and accurate analysis of the fetal brain structural connectome requires careful compensation of motion effects and robust reconstruction to avoid introducing bias based on the degree of fetal motion. In this paper we introduce a novel robust algorithm to reconstruct in‐vivo diffusion‐tensor MRI (DTI) of the moving fetal brain and show its effect on structural connectivity analysis. The proposed algorithm involves multiple steps of image registration incorporating a dynamic registration‐based motion tracking algorithm to restore the spatial correspondence of DWI data at the slice level and reconstruct DTI of the fetal brain in the standard (atlas) coordinate space. A weighted linear least squares approach is adapted to remove the effect of intra‐slice motion and reconstruct DTI from motion‐corrected data. The proposed algorithm was tested on data obtained from 21 healthy fetuses scanned in‐utero at 22–38 weeks gestation. Significantly higher fractional anisotropy values in fiber‐rich regions, and the analysis of whole‐brain tractography and group structural connectivity, showed the efficacy of the proposed method compared to the analyses based on original data and previously proposed methods. The results of this study show that slice‐level motion correction and robust reconstruction is necessary for reliable in‐vivo structural connectivity analysis of the fetal brain. Connectivity analysis based on graph theoretic measures show high degree of modularity and clustering, and short average characteristic path lengths indicative of small‐worldness property of the fetal brain network. These findings comply with previous findings in newborns and a recent study on fetuses. The proposed algorithm can provide valuable information from DWI of the fetal brain not available in the assessment of the original 2D slices and may be used to more reliably study the developing fetal brain connectome.

Rapid full-brain fMRI with an accelerated multi shot 3D EPI sequence using both UNFOLD and GRAPPA

The desire to understand complex mental processes using functional MRI drives development of imaging techniques that scan the whole human brain at a high spatial and temporal resolution. In this work, an accelerated multishot three‐dimensional echo‐planar imaging sequence is proposed to increase the temporal resolution of these studies. A combination of two modern acceleration techniques, UNFOLD and GRAPPA is used in the secondary phase encoding direction to reduce the scan time effectively. The sequence (repetition time of 1.02 s) was compared with standard two‐dimensional echo‐planar imaging (3 s) and multishot three‐dimensional echo‐planar imaging (3 s) sequences with both block design and event‐related functional MRI paradigms. With the same experimental setup and imaging time, the temporal resolution improvement with our sequence yields similar activation regions in the block design functional MRI paradigm with slightly increased t‐scores. Moreover, additional information on the timing of rapid dynamic changes was extracted from accelerated images for the case of the event related complex mental paradigm. Magn Reson Med, 2012.

Motion-Robust Diffusion-Weighted Brain MRI Reconstruction Through Slice-Level Registration-Based Motion Tracking

This work proposes a novel approach for motion-robust diffusion-weighted (DW) brain MRI reconstruction through tracking temporal head motion using slice-to-volume registration. The slice-level motion is estimated through a filtering approach that allows tracking the head motion during the scan and correcting for out-of-plane inconsistency in the acquired images. Diffusion-sensitized image slices are registered to a base volume sequentially over time in the acquisition order where an outlier-robust Kalman filter, coupled with slice-to-volume registration, estimates head motion parameters. Diffusion gradient directions are corrected for the aligned DWI slices based on the computed rotation parameters and the diffusion tensors are directly estimated from the corrected data at each voxel using weighted linear least squares. The method was evaluated in DWI scans of adult volunteers who deliberately moved during scans as well as clinical DWI of 28 neonates and children with different types of motion. Experimental results showed marked improvements in DWI reconstruction using the proposed method compared to the state-of-the-art DWI analysis based on volume-to-volume registration. This approach can be readily used to retrieve information from motion-corrupted DW imaging data.

Both 3-T dGEMRIC and Acetabular-Femoral T2 Difference May Detect Cartilage Damage at the Chondrolabral Junction

BackgroundIn addition to case reports of gadolinium-related toxicities, there are increasing theoretical concerns about the use of gadolinium for MR imaging. As a result, there is increasing interest in noncontrast imaging techniques for biochemical cartilage assessment. Among them, T2 mapping holds promise because of its simplicity, but its biophysical interpretation has been controversial.Questions/purposesWe sought to determine whether (1) 3-T delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) and T2 mapping are both capable of detecting cartilage damage at the chondrolabral junction in patients with femoroacetabular impingement (FAI); and (2) whether there is a correlation between these two techniques for acetabular and femoral head cartilage assessment.MethodsThirty-one patients with hip-related symptoms resulting from FAI underwent a preoperative 3-T MRI of their hip that included dGEMRIC and T2 mapping (symptomatic group, 16 women, 15 men; mean age, 27 ± 8 years). Ten volunteers with no symptoms according to the WOMAC served as a control (asymptomatic group, seven women, three men; mean age, 28 ± 3 years). After morphologic cartilage assessment, acetabular and femoral head cartilages were graded according to the modified Outerbridge grading criteria. In the midsagittal plane, single-observer analyses of precontrast T1 values (volunteers), the dGEMRIC index (T1Gd, patients), and T2 mapping values (everyone) were compared in acetabular and corresponding femoral head cartilage at the chondrolabral junction of each hip by region-of-interest analysis.ResultsIn the symptomatic group, T1Gd and T2 values were lower in the acetabular cartilage compared with corresponding femoral head cartilage (T1Gd: 515 ± 165 ms versus 650 ± 191 ms, p < 0.001; T2: 39 ± 8 ms versus 46 ± 10 ms, p < 0.001). In contrast, the asymptomatic group demonstrated no differences in T1 and T2 values for the acetabular and femoral cartilages with the numbers available (T1: 861 ± 130 ms versus 860 ± 182 ms, p = 0.98; T2: 43 ± 7 ms versus 42 ± 6 ms, p = 0.73). No correlation with the numbers available was noted between the modified Outerbridge grade and T1, T1Gd, or T2 as well as between T2 and either T1 or T1Gd.ConclusionsWithout the need for contrast media application, T2 mapping may be a viable alternative to dGEMRIC when assessing hip cartilage at the chondrolabral junction. However, acquisition-related phenomena as well as regional variations in the microstructure of hip cartilage necessitate an internal femoral head cartilage control when interpreting these results.Level of EvidenceLevel IV, diagnostic study.

Motion-Robust Reconstruction Based on Simultaneous Multi-slice Registration for Diffusion-Weighted MRI of Moving Subjects

Simultaneous multi-slice (SMS) echo-planar imaging has had a huge impact on the acceleration and routine use of diffusion-weighted MRI (DWI) in neuroimaging studies in particular the human connectome project; but also holds the potential to facilitate DWI of moving subjects, as proposed by the new technique developed in this paper. We present a novel registration-based motion tracking technique that takes advantage of the multi-plane coverage of the anatomy by simultaneously acquired slices to enable robust reconstruction of neural microstructure from SMS DWI of moving subjects. Our technique constitutes three main components: 1) motion tracking and estimation using SMS registration, 2) detection and rejection of intra-slice motion, and 3) robust reconstruction. Quantitative results from 14 volunteer subject experiments and the analysis of motion-corrupted SMS DWI of 6 children indicate robust reconstruction in the presence of continuous motion and the potential to extend the use of SMS DWI in very challenging populations.

Motion Compensated Abdominal Diffusion Weighted MRI by Simultaneous Image Registration and Model Estimation (SIR-ME).

Non-invasive characterization of water molecules mobility variations by quantitative analysis of diffusion-weighted MRI (DW-MRI) signal decay in the abdomen has the potential to serve as a biomarker in gastrointestinal and oncological applications. Accurate and reproducible estimation of the signal decay model parameters is challenging due to the presence of respiratory, cardiac, and peristalsis motion. Independent registration of each b-value image to the b-value=0 s/mm(2) image prior to parameter estimation might be sub-optimal because of the low SNR and contrast difference between images of varying b-value. In this work, we introduce a motion-compensated parameter estimation framework that simultaneously solves image registration and model estimation (SIR-ME) problems by utilizing the interdependence of acquired volumes along the diffusion weighting dimension. We evaluated the improvement in model parameters estimation accuracy using 16 in-vivo DW-MRI data sets of Crohns disease patients by comparing parameter estimates obtained using the SIR-ME model to the parameter estimates obtained by fitting the signal decay model to the acquired DW-MRI images. The proposed SIR-ME model reduced the average root-mean-square error between the observed signal and the fitted model by more than 50%. Moreover, the SIR-ME model estimates discriminate between normal and abnormal bowel loops better than the standard parameter estimates.

T2-Relaxometry for Myelin Water Fraction Extraction Using Wald Distribution and Extended Phase Graph

Quantitative assessment of myelin density in the white matter is an emerging tool for neurodegenerative disease related studies such as multiple sclerosis and Schizophrenia. For the last two decades, T2 relaxometry based on multi-exponential fitting to a single slice multi-echo sequence has been the most common MRI technique for myelin water fraction (MWF) mapping, where the short T2 is associated with myelin water. However, modeling the spectrum of the relaxations as the sum of large number of impulse functions with unknown amplitudes makes the accuracy and robustness of the estimated MWFs questionable. In this paper, we introduce a novel model with small number of parameters to simultaneously characterize transverse relaxation rate spectrum and B1 inhomogeneity at each voxel. We use mixture of three Wald distributions with unknown mixture weights, mean and shape parameters to represent the distribution of the relative amount of water in between myelin sheets, tissue water, and cerebrospinal fluid. The parameters of the model are estimated using the variable projection method and are used to extract the MWF at each voxel. In addition, we use Extended Phase Graph (EPG) method to compensate for the stimulated echoes caused by B1 inhomogeneity. To validate our model, synthetic and real brain experiments were conducted where we have compared our novel algorithm with the non-negative least squares (NNLS) as the state-of-the-art technique in the literature. Our results indicate that we can estimate MWF map with substantially higher accuracy as compared to the NNLS method.

Improved Multi B-Value Diffusion-Weighted MRI of the Body by Simultaneous Model Estimation and Image Reconstruction (SMEIR)

Diffusion-weighted MRI images acquired with multiple b-values have the potential to improve diagnostic accuracy by increasing the conspicuity of lesions and inflammatory activity with background suppression. Unfortunately, the inherently low signal-to-noise ratio (SNR) of DW-MRI reduces enthusiasm for using these images for diagnostic purposes. Moreover, lengthy acquisition times limit our ability to improve the quality of multi b-value DW-MRI images by multiple excitations acquisition and signal averaging at each b-value. To offset these limitations, we propose the Simultaneous Model Estimation and Image Reconstruction (SMEIR) for DW-MRI, which substantially improves the quality of multi b-value DW-MRI images without increasing acquisition times. Our model introduces the physiological signal decay model of DW-MRI as a constraint in the reconstruction of the DW-MRI images. An in-vivo experiment using 6 low-quality DW-MRI datasets of a healthy subject showed that SMEIR reconstruction of low-quality data improved SNR by 55% in the liver and by 41% in the kidney without increasing acquisition times. We also demonstrated the clinical impact of our SMEIR reconstruction by increasing the conspicuity of inflamed bowel regions in DW-MRI of 12 patients with Crohns disease. The contrast-to-noise ratio (CNR) of the inflamed regions in the SMEIR images was higher by 12.6% relative to CNR in the original DW-MRI images.