About

Cristian T. Badea, PhD, is a Professor of Radiology and Biomedical Engineering and a faculty member in Medical Physics at Duke University. He directs the Quantitative Imaging & Analysis Lab (QIAL), where his research bridges imaging, computation, and biology. His work focuses on designing and building advanced micro-CT and preclinical photon-counting CT (PCCT) systems, alongside developing reconstruction and analysis methods that extend CT imaging from anatomical visualization toward functional imaging and theranostics. This integration of hardware innovation, compressed-sensing, deep learning, and quantitative imaging biomarkers supports studies in cardiac, neuro, and cancer research. The overarching goal of his research is to connect quantitative preclinical imaging with human translation, enabling precision diagnostics and therapeutic innovation. Professor Badea's current research directions include photon-counting and spectral CT, emphasizing multi-energy CT and material decomposition, as well as 5D CT that combines spectral and temporal imaging with deep learning-based denoising and reconstruction algorithms. His work also explores functional CT and theranostics using nanoparticles such as iodine, gold, and barium as contrast agents for quantitative CT imaging. Additionally, he develops CT-based and multimodal models for predicting organ-specific biological age, virtual preclinical CT pipelines using realistic digital phantoms and AI for accelerated design and validation, and translational imaging research through co-clinical imaging trials linking preclinical and human studies. With approximately 200 peer-reviewed publications in micro-CT, spectral CT, and translational imaging, Professor Badea is recognized as a top expert globally in X-ray micro-tomography and has mentored numerous PhD students and postdoctoral researchers.

Research topics

• Computer Science
• Artificial Intelligence
• Radiology
• Pathology
• Medicine
• Nuclear medicine
• Biomedical engineering
• Medical physics

Selected publications

• Photon-Counting Micro-CT for Bone Morphometry in Murine Models

  Tomography · 2025-11-13

  articleOpen accessSenior authorCorresponding

  BACKGROUND/OBJECTIVES: This study evaluates photon-counting CT (PCCT) for the imaging of mouse femurs and investigates how APOE genotype, sex, and humanized nitric oxide synthase (HN) expression influence bone morphology during aging. METHODS: A custom-built micro-CT system with a photon-counting detector (PCD) was used to acquire dual-energy scans of mouse femur samples. PCCT projections were corrected for tile gain differences, iteratively reconstructed with 20 µm isotropic resolution, and decomposed into calcium and water maps. PCD spatial resolution was benchmarked against an energy-integrating detector (EID) using line profiles through trabecular bone. The contrast-to-noise ratio quantified the effects of iterative reconstruction and material decomposition. Femur features such as mean cortical thickness, mean trabecular spacing (TbSp_mean), and trabecular bone volume fraction (BV/TV) were extracted from calcium maps using BoneJ. The statistical analysis used 57 aged mice representing the APOE22, APOE33, and APOE44 genotypes, including 27 expressing HN. We used generalized linear models (GLMs) to evaluate the main interaction effects of age, sex, genotype, and HN status on femur features and Mann-Whitney U tests for stratified analyses. RESULTS: PCCT outperformed EID-CT in spatial resolution and enabled the effective separation of calcium and water. Female HN mice exhibited reduced BV/TV compared to both male HN and female non-HN mice. While genotype effects were modest, a genotype-by-sex stratified analysis found significant effects of HN status in female APOE22 and APOE44 mice only. Linear regression showed that age significantly decreased cortical thickness and increased TbSp_mean in male mice only. CONCLUSIONS: These results demonstrate PCCT's utility for femur analysis and reveal strong effects of sex/HN interaction on trabecular bone health in mice.

  PublisherDOI

• Supplementary figure FS3 from Neoadjuvant Radiation Therapy and Surgery Improves Metastasis-Free Survival over Surgery Alone in a Primary Mouse Model of Soft Tissue Sarcoma

  2025-11-27

  articleOpen accessSenior author

  <p>Long-term survival of an autochthonous p53/MCA mouse model in immunocompetent 129/SvJae wild-type mice induced by CRISPR/Cas9</p>

  PublisherDOI

• Photon-counting micro-CT scanner for deep learning-enabled small animal perfusion imaging

  Physics in Medicine and Biology · 2025-06-27

  articleSenior author

  Abstract Objective. In this work, we introduce a benchtop, turn-table photon-counting (PC) micro-computed tomography (CT) scanner and highlight its application for dynamic small animal perfusion imaging. Approach. Built on recently published hardware, the system now features a CdTe-based PC detector. We validated its static spectral PC micro-CT imaging using conventional phantoms and assessed dynamic performance with a custom flow-configurable dual-compartment perfusion phantom. The phantom was scanned under varied flow conditions during injections of a low molecular weight iodinated contrast agent. In vivo mouse studies with identical injection settings demonstrated potential applications. A pretrained denoising convolutional neural network (CNN) processed large multi-energy, temporal datasets (20 timepoints ×4 energies ×3 spatial dimensions), reconstructed via weighted filtered back projection. A separate CNN, trained on simulated data, performed gamma variate-based 2D perfusion mapping, evaluated qualitatively in phantom and in vivo tests. Main Results. Full five-dimensional reconstructions were denoised using a CNN in ∼3% of the time of iterative reconstruction, reducing noise in water at the highest energy threshold from 1206 HU to 86 HU. Decomposed iodine maps, which improved contrast to noise ratio from 16.4 (in the lowest energy CT images) to 29.4 (in the iodine maps), were used for perfusion analysis. The perfusion CNN outperformed pixelwise gamma variate fitting by ∼33%, with a test set error of 0.04 vs. 0.06 in blood flow index (BFI) maps, and quantified linear BFI changes in the phantom with a coefficient of determination of 0.98. Significance. This work underscores the PC micro-CT scanner’s utility for high-throughput small animal perfusion imaging, leveraging spectral PC micro-CT and iodine decomposition. It provides a versatile platform for preclinical vascular research and advanced, time-resolved studies of disease models and therapeutic interventions.

  PublisherDOI

• Image-Guided Cardiac Regeneration Via a 3d Bioprinted Vascular Patch with Built-In Ct Visibility

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Deep-learning micro-CT perfusion quantification

  2025-04-08

  articleSenior author

  This study investigates the use of a convolutional neural network to perform micro-CT perfusion quantification. Perfusion CT has demonstrated substantial benefits in human medicine, being widely used for assessing cerebral blood flow in stroke patients, evaluating myocardial perfusion in cardiac diseases, and monitoring tumor vascularity in oncology. The ability to quantify perfusion metrics such as blood flow, blood volume, and mean transit time provides valuable insights into tissue viability and function, aiding diagnosis, treatment planning, and monitoring therapeutic responses. Preclinical micro-CT perfusion imaging holds significant promise in advancing our understanding of various physiological and pathological processes in small animal models. Various methods have been developed to quantify perfusion metrics from CT data, including gamma-variate parameter fittings and deconvolution methods. However, these methods have notable drawbacks, particularly their voxel-by-voxel nature, which can introduce significant noise and variability into the perfusion maps. Deep learning is a promising alternative for perfusion analysis due to its ability to learn complex patterns and relationships from large datasets. In this work, we demonstrate a deep learning approach to perfusion quantification. The network input consists of micro-CT images at 20 timepoints of time-attenuation curves. The output of the network consists of 4 parametric maps representing the numerical parameters of a gamma variate curve. The network was able to predict idealized gamma variate curves from noisy, distorted inputs with a mean absolute percent error of less than 3.4%. When applied to real data, a significant amount of noise was present as expected; however, realistic flow in the inferior vena cava and circle of Willis was visible.

  PublisherDOI

• A perfusion phantom for dynamic micro-CT imaging

  2025-04-08

  articleSenior author

  This study presents the development, implementation, and testing of a 3D printed phantom with two compartments designed for dynamic micro-CT imaging using low molecular weight contrast agents. The phantom was evaluated through both optical and micro-CT imaging to assess its ability to generate and repeat various time attenuation curves (TACs). The optical tests demonstrated the phantom’s capability to produce a wide variety of time contrast curves by adjusting valve positions, while maintaining a repeatable input curve. Micro-CT tests confirmed the generation of diverse time attenuation curves, although repeatability was affected by the increased viscosity of the ISOVUE contrast agent. Using a gamma variate function to model the generated TAC shapes, the phantom was able to generate attenuation starting times ranging from ~12-25 seconds and peak times ranging from ~25-60 seconds. Having patterned our design after a fully tested clinical CT perfusion phantom, this preclinical phantom promises to be a valuable tool for validating and quantifying perfusion micro-CT measurements. This work represents one of the first adaptations and implementations of a dynamic perfusion phantom for CT at the preclinical level, providing a standardized method for quality assurance in preclinical and research settings. Future research should focus on addressing the observed inconsistencies and exploring the phantom’s potential in various preclinical applications.

  PublisherDOI

• Photon-Counting Micro-CT for High-Resolution Bone Morphometry in Murine Models

  Preprints.org · 2025-09-16

  preprintOpen accessSenior author

  Background/Objectives: This study evaluates photon-counting CT (PCCT) for high-resolution imaging of mouse femurs and investigates how APOE genotype, sex, and humanized nitric oxide synthase (HN) expression influence bone morphology during aging. Methods: A custom-built micro-CT system with a photon-counting detector (PCD) was used to acquire dual-energy scans of mouse femur samples. PCCT projections were corrected for tile gain differences, iteratively reconstructed with 20 µm isotropic resolution, and decomposed into calcium and water maps. PCD performance was benchmarked against an energy-integrating detector (EID) using modulation transfer functions and line profiles. Contrast-to-noise ratio quantified effects of iterative reconstruction and material decomposition. Femur features such as mean cortical thickness, mean trabecular spacing (TbSp_mean), and trabecular bone volume fraction (BV/TV) were extracted from calcium maps using BoneJ. Statistical analysis used 57 aged mice representing APOE22, APOE33, and APOE44 genotypes, including 27 expressing HN. We used generalized linear models (GLMs) to evaluate main and interaction effects of age, sex, genotype, and HN status on femur features and Mann-Whitney U tests for stratified analyses. Results: PCCT outperformed EID-CT in spatial resolution and enabled effective separation of calcium and water. GLMs revealed significant interactions between sex and HN status affecting trabecular features. Female HN mice exhibited reduced BV/TV and increased TbSp_mean compared to both male HN and female non-HN mice. While genotype effects were modest, genotype by sex stratified analysis found significant effects of HN status only in female APOE22 and APOE44 mice. Conclusions: These results demonstrate PCCT’s utility for femur analysis in mice, supporting its application in skeletal disease research.

  PublisherOA PDFDOI

• Exploring VivoVist™ for ex vivo micro-CT imaging of mouse vasculature

  2025-04-02

  article1st authorCorresponding

  This study aims to develop a simpler, quicker, and more effective method for ex vivo vascular imaging using VivoVist™, a barium-based micro-CT contrast agent, combined with an anticoagulant. VivoVist™ (Nanoprobes, Inc.) is a commercially available contrast agent that enables rapid and uniform vascular distribution due to its high-water solubility. While previous studies demonstrated its utility for in vivo photon-counting micro-CT, this work evaluates its application in ex vivo imaging as a potential replacement for the lead-based contrast agent MicroFil. Three mice with APOE genotypes were imaged using three distinct preparation protocols. Two mice were scanned with an Energy-Integrating Detector (EID) at 22 μm resolution, while the third underwent imaging at 65 μm on a photon-counting CT (PCCT) system with material decomposition capabilities. The results confirm that VivoVist™ provides excellent vascular contrast, enabling detailed visualization of vascular structures in key organs such as the lung, liver, heart, and brain. The material decomposition from PCCT enabled segmentation-free vascular separation, which is crucial for applications such as identifying atherosclerotic plaques. The imaging outcomes suggest that a fixative is not always necessary, highlighting the potential for a simplified, reproducible protocol. VivoVist™ streamlines imaging workflows by reducing procedural complexity and eliminating the extensive post-mortem steps required for MicroFil. These findings establish VivoVist™ as a useful alternative for ex vivo vascular imaging and highlight the synergy between VivoVist™ and photon-counting CT for advanced preclinical imaging studies.

  PublisherDOI

• Denoising pediatric cardiac photon‐counting CT data with sparse coding and data‐adaptive, self‐supervised deep learning

  Medical Physics · 2025-07-01

  articleOpen accessSenior authorCorresponding

  BACKGROUND: The judicious use of CT in pediatric cardiac applications is warranted because young patients face the need for repeated imaging and increased lifetime cancer risk after ionizing radiation exposure. The quality of pediatric cardiac CT scans is variable because of limited protocols optimizations for pediatric patients, the common presence of metallic implants following treatment, and disparities in denoising algorithm performance between adult and pediatric scans. Two recent technological developments promise to improve the average quality of pediatric CT scans at fixed or reduced dose: clinical photon-counting CT (PCCT) and deep learning (DL) algorithms for CT image denoising. Given advancements to accommodate variable image quality, these technologies will deliver improved spatial resolution, noise performance, and contrast resolution for pediatric cardiac CT imaging. PURPOSE: To advance self-supervised DL denoising methods to accommodate variable image quality in pediatric cardiac CT data. METHODS: Starting with the popular Vision Transformer (ViT) DL architecture, two targeted architectural changes were made: (1) the multi-layer perceptrons (MLPs) were modified to allow cross-token recombination of encoded image data following attention computations (parallels patch-wise weighting and averaging in non-local means [NLM]), and (2) the network head was replaced with the equivalent of an overcomplete dictionary to perform dictionary sparse coding (SC). This modified, 3D ViT (mViT) was then trained in a dynamic fashion: the balance between data fidelity and representation sparsity was adjusted during training such that the average fidelity error remained consistent with localized estimates of image noise. To demonstrate the newly proposed method, the mViT was trained with pediatric cardiac photon-counting x-ray CT data with variable levels of image noise (NAEOTOM Alpha PCCT scanner; retrospective data from 20 patients scanned at Duke University; ages: 1-18 years; iterative reconstruction noise level in the left ventricle: 20-55 HU). Data from one patient with the highest levels of noise was reserved for validation. Testing data included Alpha data from three additional Duke patients (2 < 1 year old) and a murine cardiac PCCT data set acquired on a preclinical system. RESULTS: The validation denoising results demonstrate that SC with the mViT preserves anatomic structures relevant to the diagnosis and treatment of congenital heart defects (coronary artery origins; valve leaflets; left ventricle boundaries) while achieving similar intensity bias and lower intensity variance values than competing denoising methods (bilateral filtration [BF], NLM, dictionary SC, block matching 4D, orthogonal matching pursuit, Noise2Void). Applying the trained mViT network to preclinical PCCT demonstrated robust generalization performance to high levels of image noise (∼230 HU) and differing image contrast; however, applying the network to clinical PCCT data in younger patients (< 1 year old) demonstrated some smoothing of image details in data already heavily denoised during reconstruction. CONCLUSIONS: This work demonstrates robust, self-supervised denoising of pediatric cardiac PCCT data through data adaptation during network training based on local noise estimates. The trained network generalizes to data sets with high levels of noise and differing image contrast relative to the training data, suggesting that self-supervised fine tuning may allow the trained network to address related CT denoising problems.

  PublisherOA PDFDOI

• Assessing the cardioprotective effects of exercise in APOE mouse models using deep learning and photon-counting micro-CT

  PLoS ONE · 2025-04-10 · 2 citations

  articleOpen accessSenior author

  BACKGROUND: The allelic variations of the apolipoprotein E (APOE) gene play a critical role in regulating lipid metabolism and significantly impact cardiovascular disease risk (CVD). This study aimed to evaluate the impact of exercise on cardiac structure and function in mouse models expressing different APOE genotypes using photon-counting computed tomography (PCCT) and deep learning-based segmentation. METHODS: A total of 140 mice were grouped based on APOE genotype (APOE2, APOE3, APOE4), sex, and exercise regimen. All mice were maintained on a controlled diet to isolate the effects of exercise. Low dose cardiac photon counting micro-CT imaging with intrinsic gating was performed using a custom-built micro-PCCT system and data was reconstructed with an iterative algorithm incorporating both temporal and spectral dimensions. A liposomal-iodine nanoparticle contrast agent was intravenously administered to uniformly opacify cardiovascular structures. Cardiac structures were segmented using a 3D U-Net deep learning model that was trained and validated on manually labeled data. Statistical analyses, including ANOVA, post-hoc analysis, and stratified group comparisons, were used to assess the effects of genotype, sex, and exercise on key cardiac metrics, including ejection fraction and cardiac index. RESULTS: The PCCT imaging pipeline provided high-resolution images with enhanced contrast between blood compartment and myocardium allowing for precise segmentation of cardiac features. Deep learning-based segmentation achieved high accuracy with an average Dice coefficient of 0.85. Exercise significantly improved cardiac performance, with ejection fraction increasing by up to 18% and cardiac index by 46% in exercised males, who generally benefited more from exercise. Females, particularly those with the APOE4 genotype, also showed improvements, with a 31% higher ejection fraction in exercised versus non-exercised mice. Stratified analyses confirmed that both sexes benefited from exercise, with males showing larger effect sizes. APOE3 and APOE4 genotypes derived the greatest benefit, while APOE2 mice showed no significant improvement. CONCLUSIONS: This study demonstrates the utility of PCCT combined with deep learning segmentation in assessing the cardioprotective effects of exercise in APOE mouse models. These findings highlight the importance of genotype-specific approaches in understanding and potentially mitigating the impact of CVD through lifestyle interventions such as exercise.

  PublisherDOI

Recent grants

• A multi-channel reconstruction toolkit for computed tomography

  NIH · $1.9M · 2021–2024

• NIH Grant P41RR005959

  NIH · $4.6M · 2013

• A multi-channel reconstruction toolkit for computed tomography

  NIH · $226k · 2021–2024

• Development of a New Generation Micro-CT imaging for Functional and Molecular Imaging of Cancer

  NIH · $2.3M · 2015–2022

• NIH Grant R21CA124584

  NIH · $343k · 2009

Frequent coauthors

• G. Allan Johnson

  Duke University

  288 shared

• Yi Qi

  247 shared

• Stanley Cohen

  247 shared

• Wen‐Yih Liang

  Taipei Veterans General Hospital

  208 shared

• Yair Rivenson

  208 shared

• Stephanie J. Blocker

  Duke University

  188 shared

• Aydogan Özcan

  University of California, Los Angeles

  182 shared

• Gary P. Cofer

  181 shared

Labs

• Quantitative Imaging and Analysis LabPI

Education

• PostDoc, Radiology

  Duke University

  2005

• PhD, Medical Physics and Bio-medical Engineering

  University of Patras

  2001