About

Bruce Damon is the Director of Clinical Imaging Research at the Stephens Family Clinical Research Institute at the University of Illinois Urbana-Champaign. His primary research area is biomedical imaging, with a focus on MRI and physiological systems. He is involved in bioengineering research that encompasses biomedical imaging techniques, particularly MRI, and their applications in understanding physiological systems. His work contributes to the field of bioengineering by advancing imaging methods for detecting and treating health issues, and he is actively engaged in clinical research related to biomedical imaging.

Research topics

• Medicine
• Internal medicine
• Cardiology
• Computer science
• Anatomy

Selected publications

• Study protocol for the Champaign-Urbana population study

  Frontiers in Neuroimaging · 2026-02-10

  articleOpen access

  Superior signal-to-noise ratio, enhanced and novel forms of contrast, and improved spectral resolution made possible by 7 Tesla (7 T) magnetic resonance imaging (MRI) offer great promise for both neuroimaging research and clinical practice. To characterize these gains, it is essential to acquire structural, functional, and biochemical 7 T MRI data from a large sample of adults. The Champaign Urbana Population Study (CUPS) will collect and publish a database of 7 T MRI data, including raw MRI data, from a cohort of up to 200 adults. Here, we describe the study design and provide example images from the initial round of data collection for CUPS.

  PublisherOA PDFDOI

• Pediatric heart transplant patients with cardiac allograft vasculopathy have abnormal regional strain patterns as measured by novel 4D cardiac magnetic resonance strain analysis

  Journal of Cardiovascular Magnetic Resonance · 2026-01-01

  articleOpen access
  PublisherDOI

• Non-invasive detection of local microstructural damage in tendon using Diffusion Tensor MRI

  Acta Biomaterialia · 2026-05-01

  articleOpen access

  Tendon is critical for musculoskeletal function as it transfers forces generated by muscle to bone and stores energy during movement. Impaired mechanical function in tendon limits mobility and results from fatigue-induced damage progression that outpaces the restorative processes maintaining tissue health, a phenomenon we term mechanopathology. Early and non-invasive detection of tendon mechanopathologies is vital to prevent further damage but is lacking in the clinical space. Here, we evaluate the ability of diffusion tensor magnetic resonance imaging (DT-MRI) to detect mechanical fatigue damage in tendon and validate our findings using histologic assessments of collagen fiber microstructure and molecular structure. We found that fatigue-induced changes in DT-MRI metrics of tendon are spatially heterogeneous and correspond to regions with damaged collagen fiber microstructure. While secondary structures of collagen molecules were damaged by fatigue loading, they do not spatially correspond to fatigue-induced changes in DT-MRI metrics. Fatigue-induced changes in DT-MRI metrics can be partially explained by quantitative metrics of post-fatigue collagen fiber microstructure, estimating the limit of detection of DT-MRI metrics to fatigue-induced damage in tendon. Our findings indicate that DT-MRI metrics are sensitive to fatigue-induced local damage in tendon, supporting the use of DT-MRI as a non-invasive tool to detect tendon mechanopathologies and motivating future work toward clinical translation. STATEMENT OF SIGNIFICANCE: Non-invasive imaging techniques capable of detecting fatigue-induced damage in tendon are lacking in the clinical practice. Diffusion Tensor MRI (DT-MRI) is a non-invasive imaging technique that is sensitive to tissue microstructure, but its sensitivity to detect fatigue-induced damage in tendon - and other connective soft tissues - has not been evaluated. Here, we show that DT-MRI can identify local damage to the collagen fiber microstructure of tendon and establish the detection limits of 9.4 tesla DT-MRI metrics to such damage. Collectively, our findings support DT-MRI as a noninvasive tool for detecting fatigue-induced damage in tendon, and potentially other soft connective tissues, warranting further development to facilitate clinical translation.

  PublisherDOI

• The effect of microstructural variations in tendon and ligament on diffusion tensor MRI

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-16 · 1 citations

  articleOpen access

  The fibrous microstructure of tendons and ligaments is an important determinant of their mechanical behavior and integrity. Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that enables the inference of microstructural features within fibrous tissues and has recently been used to characterize the microstructure of dense connective tissues such as tendon and ligament. However, the effect of microstructural variations in tendon and ligament on DTI metrics remains unclear. To address this gap, we simulated diffusion MRI of second harmonic generation (SHG) image-informed square lattice fiber networks to determine which microstructural features have the strongest influence on DTI metrics. Then, we performed a second set of diffusion MRI simulations for randomly dispersed fibers within synthetic tendon volumes to relate DTI metrics to the influential microstructural features, including fiber dispersion. All DTI metrics were insensitive to collagen fiber crimp. Fiber dispersion did not affect mean diffusivity, decreased axial diffusivity, increased radial diffusivity, and decreased fractional anisotropy. These results provide valuable insight into the relationships between DTI metrics and microstructural properties of tendon and ligament, which is particularly relevant for inferring microstructural changes in impaired tissue using DTI. Furthermore, our findings are an important step in the translation of DTI for clinical and computational studies of dense connective tissues such as tendon and ligament.

  PublisherOA PDFDOI

• Correction: Ultrashort-T2* mapping at 7 tesla using an optimized pointwise encoding time reduction with radial acquisition (PETRA) sequence at standard and extended echo times

  PLoS ONE · 2026-03-27

  articleOpen access

  [This corrects the article DOI: 10.1371/journal.pone.0310590.].

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• A Comparison of Skeletal Muscle Diffusion Tensor Imaging Tractography Seeding Methods

  NMR in Biomedicine · 2025-10-16

  articleOpen access1st authorCorresponding

  The internal arrangement of a muscle's fibers with respect to its mechanical line of action (muscle architecture) is a major determinant of muscle function. Muscle architecture can be quantified using diffusion tensor magnetic resonance imaging-based tractography, which propagates streamlines from a set of seed points by integrating vectors that represent the direction of greatest water diffusion (and by inference, the local fiber orientation). Previous work in skeletal muscle has demonstrated that tractography outcomes are sensitive to the method for defining seed points, but this sensitivity has not been fully examined. To do so, we developed a realistic simulated muscle architecture and implemented three methods for tract seeding: seeding along the muscle-aponeurosis boundary with an updated procedure for rounding seed points prior to lookup in the muscle boundary mask and diffusion tensor matrices (APO); voxel-based seeding throughout the muscle volume at a uniform spatial frequency (VXL); and seeding near external and internal muscle boundaries (EDGE). We then implemented these methods in example human datasets. The updated aponeurosis seeding procedures allow more accurate and robust tract propagation from seed points. The voxel-based seeding methods had quantification outcomes that closely matched the updated aponeurosis seeding method. Further, the voxel-based methods can accelerate the overall workflow and may be beneficial in high throughput analysis of multi-muscle datasets. Continued evaluation of these methods in a wider range of muscle architectures is warranted.

  PublisherOA PDFDOI

• High-Resolution Brain Metabolic Imaging at Ultrahigh Field Using Extended Spatiospectral Encoding and Subspace Modeling

  IEEE Transactions on Biomedical Engineering · 2025-05-21 · 1 citations

  articleOpen access

  OBJECTIVE: To develop a high-resolution magnetic resonance (MR) metabolic imaging method for mapping human brain metabolite distributions at ultrahigh field (7T). METHODS: In data acquisition, a free-induction-decay (FID) based MR spectroscopic imaging (MRSI) sequence was implemented. To achieve high spatial resolution, the sequence used fast echo-planar spectroscopic imaging (EPSI) trajectories with echo-spacings larger than the Nyquist sampling interval. Using this sequence, 3D MRSI signals at isotropic nominal resolutions of 3.0 mm and 1.8 mm were acquired within scan times of 4.8 and 14.2 minutes, respectively. In data processing, model-based methods integrating subspace learning, spectral modeling, and generalized series modeling were developed to address key challenges, including spectral ghosting, low signal-to-noise ratio, and spectral aliasing. RESULTS: The proposed acquisition and processing methods successfully generated high-resolution, high-quality metabolite maps of the human brain at 7T. Experimental results from phantom and in vivo scans validated the proposed method and showed its capability to capture detailed brain metabolite distributions. CONCLUSION: This work demonstrates the feasibility of high-resolution brain metabolic imaging at ultrahigh field using MRSI acquisition sequence and model-based processing methods. SIGNIFICANCE: By providing high-resolution spatial mapping of brain metabolites within clinically feasible scan times, the proposed method promises to offer a powerful imaging tool for investigating brain metabolism, which is expected to be useful for various brain imaging applications.

  PublisherDOI

• Improved DTI‐Based Skeletal Muscle Architecture Estimation via Diffusion Weighted Image Denoising and First Eigenvector Map Smoothing

  NMR in Biomedicine · 2025-07-15 · 2 citations

  articleOpen accessSenior authorCorresponding

  Skeletal muscle architecture-the internal arrangement of a muscle's fibers with respect to its line of action-is a major determinant of muscle function. In diffusion tensor imaging (DTI) tractography, the first eigenvector of the diffusion tensor is integrated to create "fiber-tracts" that represent muscle architecture at the spatial scale of several fascicles. However, noise contamination of the images causes erroneous estimates of the first eigenvector that propagate during tractography, causing inaccurate architecture estimates at typical signal-to-noise ratios (SNR's). Although image denoising is commonly used, its effects have not been evaluated in skeletal muscle against known ground truth. Moreover, alternative strategies (such as smoothing of the muscle's first eigenvector field) remain unexplored. Therefore, simulated diffusion tensor images of a model muscle were used to quantify the effect of anisotropic image smoothing, threshold principal component analysis-based image denoising, and first eigenvector field smoothing on the accuracy of DTI-based muscle architecture estimates at different SNR levels. The denoising methods were then implemented in a human dataset. In the simulated dataset, anisotropic image smoothing and first eigenvector field smoothing reduced the deviation of the first eigenvector obtained from the noise-contaminated images from those obtained from the noise-free images and improved the accuracy of the fascicle curvature estimates. In the human dataset, both smoothing methods decreased the fiber-tract curvature estimates compared to the values in the raw images. Both anisotropic image smoothing and smoothing of the first eigenvector field improve the accuracy of DTI tractography-based muscle architecture estimates.

  PublisherOA PDFDOI

• Ultrashort-T2* mapping at 7 tesla using an optimized pointwise encoding time reduction with radial acquisition (PETRA) sequence at standard and extended echo times

  PLoS ONE · 2025-04-17 · 3 citations

  articleOpen access

  Zero echo time (ZTE) sequences capture signal from tissues with extremely short T2* and are useful for qualitative and quantitative imaging of musculoskeletal tissues' ultrashort-T2* components. One such sequence is Pointwise Encoding Time Reduction with Radial Acquisition (PETRA). While this sequence has shown promising results, it has undergone only limited testing at 7 tesla (T). The purpose of this work was to evaluate PETRA at 7T in its standard, commercially available form and with sequence code modifications to allow extended echo times for the purpose of performing ultrashort-T2* mapping. We acquired PETRA images of MnCl2 and collagen phantoms and of the knee in eight participants (5 for optimization and 3 for ultrashort-T2* mapping assessment; 5 male/3 female, 39 ± 11 years old). Images were acquired using a 1-transmit/28-receive-channel knee coil. Artifacts, signal, signal-to-noise ratio (SNR), ultrashort-T2*, the corresponding curve fit quality, and repeatability were assessed. In knee tissues, SNR was higher at TE = 0.07 msec than in a conventional-TE sequence (Dual-Echo Steady State with TE = 2.55 msec), with values of 68-337 for PETRA versus 16-30 for the same regions in the conventional-TE series. Acquisition of series for ultrashort-T2* maps was feasible at 1.50 mm isotropic acquisition resolution and TE ≤ 0.58 msec. Strong linear correlations were observed between relaxation rates (R2*) and MnCl2 concentration, and between signal and collagen concentration. Ultrashort-T2* signal decay curve fit R2 and repeatability were high for phantom and knee ultrashort-T2* <1 msec. PETRA imaging with minimal artifacts, high SNR, and scan time < 11 minutes was achieved at 7T at high (0.34 mm isotropic) resolution at TE = 0.07 msec and lower resolution (1.52 mm isotropic) at echo times ≤ 0.58 msec. Ultrashort-T2* mapping provided acceptable curve-fitting results for substances with sub-millisecond T2*.

  PublisherDOI

• MRI-based 3D Estimation of Skeletal Muscle Architecture and Strain during Contraction

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-07-29 · 2 citations

  preprintOpen accessCorresponding

  Skeletal muscle generates forces that drive the motion of the human body. Three-dimensional (3D) quantification of whole-muscle architecture and strain, and their relationship during contraction is critical to understanding the mechanical function of skeletal muscle in health and disease. This has proven to be challenging, as brightness mode ultrasound is capable of measuring muscle architecture during contraction but cannot capture 3D changes in whole-muscle architecture, while Diffusion Tensor Imaging (DTI)-based tractography can measure 3D whole-muscle architecture but its use during contraction is precluded by long scan times (>5 minutes). In this study, we implement DTI-based tractography with an image registration-based approach, previously validated under passive deformation, to estimate 3D whole-muscle architecture of the tibialis anterior (TA) muscle during moderate intensity contractions (20-40% MVC). Moreover, this approach allows the measurement of whole-muscle strain during contraction, facilitating the evaluation of intramuscular relationships between architecture and strain. Our results show a decrease in the fiber-tract length, an increase in the pennation angle, and an increase in the fiber curvature of the TA during contraction. Intramuscular strain heterogeneity was observed between and within different regions of the muscle, with exploratory analyses suggesting that regional strain heterogeneity could be influenced by muscle architecture. Our results showcase the potential of MRI-based methods to obtain 3D estimates of whole-muscle architecture and strain during contraction, providing a breadth of new data that allows for new avenues of skeletal muscle biomechanical research.

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Recent grants

• NIH Grant R01AR050101

  NIH · $3.1M · 2016

• NIH Grant R01DK105371

  NIH · $2.1M · 2021

• Development and Application of Muscle Diffusion Tensor MRI

  NIH · $2.1M · 2019–2025

• NIH Grant F32AR008614

  NIH · $79k

• NIH Grant R01AR057091

  NIH · $1.7M · 2015

Frequent coauthors

• Melissa T. Hooijmans

  45 shared

• Carly A. Lockard

  38 shared

• John C. Gore

  Vanderbilt University

  31 shared

• Jonathan H. Soslow

  Vanderbilt University

  30 shared

• Larry W. Markham

  Indiana University – Purdue University Indianapolis

  25 shared

• Theodore F. Towse

  Grand Valley State University

  25 shared

• Crystal L. Coolbaugh

  Vanderbilt University Medical Center

  22 shared

• Xingyu Zhou

  Dalian Ocean University

  21 shared

Education

• PhD, Molecular and Integrative Physiology

  University of Illinois Urbana-Champaign

  2000

• First year medical studies, School of Medicine

  University of Illinois Urbana-Champaign

  1998

• MS, Kinesiology

  University of Illinois Urbana-Champaign

  1993