Center for Imaging Research

Advanced Neuroimaging

Grant Funded Projects

Recent and Current Grant-Funded Projects

NIH R21: A Complete Metal Artifact Reduced MRI Exam for Neuroimaging (National Institute for Biomedical Imaging and Bioengineering)
(Project Leads: Kevin Koch, PhD and Andrew Nencka, PhD)
This NIH-funded project will bring advanced MRI capabilities to patients with cranial metallic implants who need diagnostic neurological imaging. Compared to the current standard of care methods using computed tomography imaging methods, patients with ventricular shunts, aneurysm coils, arterial flow diverters, dental appliances, cochlear implants, and other metallic implants in their heads, metal artifact reduced MRI offers no risks of ionizing radiation and improved contrast within brain tissues of interest. The neuroimaging-focused translation and development of novel MRI principles performed in this project will offer transformative diagnostic improvements in patients with neurological disorders and implants in or near their skulls.

DOD: Project Head to Head
(Project Lead: Michael McCrea, PhD, MCW Neurosurgery)
As a co-investigator on Dr. McCrea’s Head to Head project of sports concussion in high school and collegiate football athletes, Dr. Koch has led brain magnetism analyses of post-concussion subjects. This work has led to two published manuscripts demonstrating acute changes in brain magnetism after concussion in these athletes. In his most recent work, he has demonstrated that these measurements track with the duration in which athletes are symptomatic after their injury.

DOD/NCAA: CARE Consortium Advanced Research Core
(Project Lead: Michael McCrea, PhD, MCW Neurosurgery)
Dr. Koch is the co-chair of the Neuroimaging Sub-Core of the CARE consortium ARC. In this role, he has worked to deploy advanced MRI protocols across 4 data collection sites, spanning both GE Healthcare and Siemens Healthcare MRI scanners. Data has been collected within this protocol on post-concussive NCAA athletes since 2014 and is ongoing. Along with his administrative oversight of the sub-core, Dr. Koch is leading the tissue magnetism analysis of data collected in this protocol.

GE Healthcare: Connectome Technology Hub
(Project Leads: Kevin Koch, PhD and Andrew Nencka, PhD)
Our technical team performs the development and beta testing of new software implementations of human connectome protocol software prototypes on the GE Healthcare MRI platform. We disseminate the tested packages, support their usage, and seek to develop new advances that can be deployed in these packages. In addition, we are working to establish mechanisms whereby advanced neuroimaging tools can be more readily utilized in clinical practice. This work is particularly focused on advanced clinical usage of diffusion and tissue magnetism MRI-based quantitative tools.


Recent and current publications.
Acute Post-Concussive Assessments of Brain Tissue Magnetism using Magnetic Resonance Imaging
Recent studies have demonstrated the promising capabilities of MRI-based quantitative susceptibility maps (QSM) in producing biomarkers of brain injury. The present study aims to further explore acute QSM changes in athletes after sports concussion and investigate prognostication capabilities of QSM-derived imaging metrics. QSM were derived from neurological MRI data acquired on a cohort (n=78) of concussed male American football athletes within 48 hours of injury. MRI-derived QSM values in subcortical gray and white matter compartments after concussion showed differences relative to a matched uninjured control group (white matter: z = 3.04, p = 0.002, subcortical gray matter: z = −2.07, p = 0.04). Subcortical gray matter QSM MRI measurements also correlated strongly with duration of symptoms (ρ=-0.46, p = 0.002) within a sub-cohort of subjects that had symptom durations for at least 1 week (n=39). The acute QSM MRI metrics showed promising prognostication capabilities, with subcortical gray matter compartment QSM values yielding a mean classification area under the curve (AUC) performance of 0.78 when predicting symptoms of more than two weeks in duration. The results of the study reproduce previous acute post-concussion group QSM findings and provide promising initial prognostication capabilities of acute QSM measurements in a post-concussion setting. Read more of Dr. Koch’s article “Acute Post-Concussive Assessments of Brain Tissue Magnetism using Magnetic Resonance Imaging“ from the Journal of Neurotrauma.
Model-Based Learning for Quantitative Susceptibility Mapping
Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that estimates magnetic susceptibility of tissue from Larmor frequency offset measurements. The generation of QSM requires solving a challenging ill-posed field-to-source inversion problem. Inaccurate field-to-source inversion often causes large susceptibility estimation errors that appear as streaking artifacts in the QSM, especially in massive hemorrhagic regions. Recently, several deep learning (DL) QSM techniques have been proposed and demonstrated impressive performance. Due to the inherent non-existent ground-truth QSM references, these DL techniques used either calculation of susceptibility through multiple orientation sampling (COSMOS) maps or synthetic data for network training. Therefore, they were constrained by the availability and accuracy of COSMOS maps, or suffered from performance drop when the training and testing domains were different. To address these limitations, we present a model-based DL method, denoted as uQSM. Without accessing to QSM labels, uQSM is trained using the well-established physical model. When evaluating on multi-orientation QSM datasets, uQSM achieves higher levels of quantitative accuracy compared to TKD, TV-FANSI, MEDI, and DIP approaches. When qualitatively evaluated on single-orientation datasets, uQSM outperforms other methods and reconstructed high quality QSM. Read more of Dr. Koch’s article “Model-Based Learning for Quantitative Susceptibility Mapping” from the Machine Learning for Medical Image Reconstruction.
Weakly-Supervised Learning for Single-Step Quantitative Susceptibility Mapping
Quantitative susceptibility mapping (QSM) utilizes MRI phase information to estimate tissue magnetic susceptibility. The generation of QSM requires solving ill-posed background field removal (BFR) and field-to-source inversion problems. Because current QSM techniques struggle to generate reliable QSM in clinical contexts, QSM clinical translation is greatly hindered. Recently, deep learning (DL) approaches for QSM reconstruction have shown impressive performance. Due to inherent non-existent ground-truth, these DL techniques use either calculation of susceptibility through multiple orientation sampling (COSMOS) maps or synthetic data for network training, which are constrained by the availability and accuracy of COSMOS maps or domain shift when training data and testing data have different domains. To address these limitations, we propose a weakly-supervised single-step QSM reconstruction method, denoted as wTFI, to directly reconstruct QSM from the total field without BFR. wTFI uses the BFR method RESHARP local fields as supervision to perform a multi-task learning of local tissue fields and QSM, and is capable of recovering magnetic susceptibility estimates near the edges of the brain where are eroded in RESHARP and realize whole brain QSM estimation. Quantitative and qualitative evaluation shows that wTFI can generate high-quality local field and susceptibility maps in a variety of neuroimaging contexts. Read more of Dr. Koch’s article “Weakly-Supervised Learning for Single-Step Quantitative Susceptibility Mapping” from the Machine Learning for Medical Image Reconstruction.
Association of Head Impact Exposure with White Matter Macrostructure and Microstructure Metrics
Prior studies have reported white matter abnormalities associated with a history of cumulative concussion and/or repetitive head impacts (RHI) in contact sport athletes. Growing evidence suggests these abnormalities may begin as more subtle changes earlier in life in active younger athletes. We investigated the relationship between prior concussion and contact sport exposure with multi-modal white matter microstructure and macrostructure using magnetic resonance imaging. High school and collegiate athletes (n = 121) completed up to four evaluations involving neuroimaging. Linear mixed-effects models examined associations of years of contact sport exposure (i.e., RHI proxy) and prior concussion across multiple metrics of white matter, including total white matter volume, diffusion tensor imaging (DTI) metrics, diffusion kurtosis imaging (DKI) metrics, and quantitative susceptibility mapping (QSM). A significant inverse association between cumulative years of contact sport exposure and QSM was observed, F(1, 237.77) = 4.67, p = 0.032. Cumulative contact sport exposure was also associated with decreased radial diffusivity, F(1, 114.56) = 5.81, p = 0.018, as well as elevated fractional anisotropy, F(1, 115.32) = 5.40, p = 0.022, and radial kurtosis, F(1, 113.45) = 4.03, p = 0.047. In contrast, macroscopic white matter volume was not significantly associated with cumulative contact sport exposure (p > 0.05). Concussion history was not significantly associated with QSM, DTI, DKI, or white matter volume (all, p > 0.05). Cumulative contact sport exposure is associated with subtle differences in white matter microstructure, but not gross white matter macrostructure, in young active athletes. Longitudinal follow-up is required to assess the progression of these findings to determine their contribution to potential adverse effects later in life. Read more of Dr. Koch’s article “Association of Head Impact Exposure with White Matter Macrostructure and Microstructure Metrics” from the Journal of Neurotrauma.
Split Slice Training Augmentation and Hyperparameter Tuning of RAKI Networks for Simultaneous Multi-Slice Reconstruction
Split-slice augmentation for simultaneous multi-slice RAKI networks positively impacts network performance. Hyperparameter tuning of such reconstruction networks can lead to further improvements in unaliasing performance. Read more of Dr. Koch’s article “Split Slice Training Augmentation and Hyperparameter Tuning of RAKI Networks for Simultaneous Multi-Slice Reconstruction” from
Optimization of hyperparameters for SMS reconstruction
Simultaneous multi-slice (SMS) imaging accelerates MRI data acquisition by exciting multiple image slices with a single radiofrequency pulse. Overlapping slices encoded in acquired signal are separated using a mathematical model, which requires estimation of image reconstruction kernels using calibration data. Several parameters used in SMS reconstruction impact the quality and fidelity of final images. Therefore, finding an optimal set of reconstruction parameters is critical to ensure that accelerated acquisition does not significantly degrade resulting image quality. Read more of Dr. Koch’s article “Optimization of hyperparameters for SMS reconstruction” from Magnetic Resonance Imaging.
Non-locally Encoder-Decoder Convolutional Network for Whole Brain QSM Inversion
Quantitative Susceptibility Mapping (QSM) reconstruction is a challenging inverse problem driven by ill conditioning of its field-to -susceptibility transformation. State-of-art QSM reconstruction methods either suffer from image artifacts or long computation times, which limits QSM clinical translation efforts. To overcome these limitations, a non-locally encoder-decoder gated convolutional neural network is trained to infer whole brain susceptibility map, using the local field and brain mask as the inputs. The performance of the proposed method is evaluated relative to synthetic data, a publicly available challenge dataset, and clinical datasets. The proposed approach can outperform existing methods on quantitative metrics and visual assessment of image sharpness and streaking artifacts. The estimated susceptibility maps can preserve conspicuity of fine features and suppress streaking artifacts. The demonstrated methods have potential value in advancing QSM clinical research and aiding in the translation of QSM to clinical operations. Read more of this article “Non-locally Encoder-Decoder Convolutional Network for Whole Brain QSM Inversion” from
Quantitative Susceptibility Mapping after Sports-Related Concussion
Quantitative susceptibility mapping using MR imaging can assess changes in brain tissue structure and composition. This report presents preliminary results demonstrating changes in tissue magnetic susceptibility after sports-related concussion. Read more of Dr. Koch’s article “Quantitative Susceptibility Mapping after Sports-Related Concussion” from American Journal of Neuroradiology.
Stability of MRI metrics in the advanced research core of the NCAA-DoD concussion assessment, research and education (CARE) consortium
The NCAA-DoD Concussion Assessment, Research, and Education (CARE) consortium is performing a large-scale, comprehensive study of sport related concussions in college student-athletes and military service academy cadets. The CARE “Advanced Research Core” (ARC), is focused on executing a cutting-edge investigative protocol on a subset of the overall CARE athlete population. Here, we present the details of the CARE ARC MRI acquisition and processing protocol along with preliminary analyzes of within-subject, between-site, and between-subject stability across a variety of MRI biomarkers. Two experimental datasets were utilized for this analysis. First, two “human phantom” subjects were imaged multiple times at each of the four CARE ARC imaging sites, which utilize equipment from two imaging vendors. Additionally, a control cohort of healthy athletes participating in non-contact sports were enrolled in the study at each CARE ARC site and imaged at four time points. Multiple morphological image contrasts were acquired in each MRI exam; along with quantitative diffusion, functional, perfusion, and relaxometry imaging metrics. As expected, the imaging markers were found to have varying levels of stability throughout the brain. Importantly, between-subject variance was generally found to be greater than within-subject and between-site variance. These results lend support to the expectation that cross-site and cross-vendor advanced quantitative MRI metrics can be utilized to improve analytic power in assessing sensitive neurological variations; such as those effects hypothesized to occur in sports-related-concussion. This stability analysis provides a crucial foundation for further work utilizing this expansive dataset, which will ultimately be freely available through the Federal Interagency Traumatic Brain Injury Research Informatics System. Read more of Dr. Koch’s article from Brain Imaging and Behavior.