Advanced photon counting CT imaging pipeline for cardiac phenotyping of apolipoprotein E mouse models.

BACKGROUND: Cardiovascular disease (CVD) is associated with the apolipoprotein E (APOE) gene and lipid metabolism. This study aimed to develop an imaging-based pipeline to comprehensively assess cardiac structure and function in mouse models expressing different APOE genotypes using photon-counting computed tomography (PCCT). METHODS: 123 mice grouped based on APOE genotype (APOE2, APOE3, APOE4, APOE knockout (KO)), gender, human NOS2 factor, and diet (control or high fat) were used in this study. The pipeline included PCCT imaging on a custom-built system with contrast-enhanced in vivo imaging and intrinsic cardiac gating, spectral and temporal iterative reconstruction, spectral decomposition, and deep learning cardiac segmentation. Statistical analysis evaluated genotype, diet, sex, and body weight effects on cardiac measurements. RESULTS: Our results showed that PCCT offered high quality imaging with reduced noise. Material decomposition enabled separation of calcified plaques from iodine enhanced blood in APOE KO mice. Deep learning-based segmentation showed good performance with Dice scores of 0.91 for CT-based segmentation and 0.89 for iodine map-based segmentation. Genotype-specific differences were observed in left ventricular volumes, heart rate, stroke volume, ejection fraction, and cardiac index. Statistically significant differences were found between control and high fat diets for APOE2 and APOE4 genotypes in heart rate and stroke volume. Sex and weight were also significant predictors of cardiac measurements. The inclusion of the human NOS2 gene modulated these effects. CONCLUSIONS: This study demonstrates the potential of PCCT in assessing cardiac structure and function in mouse models of CVD which can help in understanding the interplay between genetic factors, diet, and cardiovascular health.

Leveraging 3D Bioprinting and Photon-Counting Computed Tomography to Enable Noninvasive Quantitative Tracking of Multifunctional Tissue Engineered Constructs.

3D bioprinting is revolutionizing the fields of personalized and precision medicine by enabling the manufacturing of bioartificial implants that recapitulate the structural and functional characteristics of native tissues. However, the lack of quantitative and noninvasive techniques to longitudinally track the function of implants has hampered clinical applications of bioprinted scaffolds. In this study, multimaterial 3D bioprinting, engineered nanoparticles (NPs), and spectral photon-counting computed tomography (PCCT) technologies are integrated for the aim of developing a new precision medicine approach to custom-engineer scaffolds with traceability. Multiple CT-visible hydrogel-based bioinks, containing distinct molecular (iodine and gadolinium) and NP (iodine-loaded liposome, gold, methacrylated gold (AuMA), and Gd2 O3 ) contrast agents, are used to bioprint scaffolds with varying geometries at adequate fidelity levels. In vitro release studies, together with printing fidelity, mechanical, and biocompatibility tests identified AuMA and Gd2 O3 NPs as optimal reagents to track bioprinted constructs. Spectral PCCT imaging of scaffolds in vitro and subcutaneous implants in mice enabled noninvasive material discrimination and contrast agent quantification. Together, these results establish a novel theranostic platform with high precision, tunability, throughput, and reproducibility and open new prospects for a broad range of applications in the field of precision and personalized regenerative medicine.

A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images.

Photon-counting CT (PCCT) is powerful for spectral imaging and material decomposition but produces noisy weighted filtered backprojection (wFBP) reconstructions. Although iterative reconstruction effectively denoises these images, it requires extensive computation time. To overcome this limitation, we propose a deep learning (DL) model, UnetU, which quickly estimates iterative reconstruction from wFBP. Utilizing a 2D U-net convolutional neural network (CNN) with a custom loss function and transformation of wFBP, UnetU promotes accurate material decomposition across various photon-counting detector (PCD) energy threshold settings. UnetU outperformed multi-energy non-local means (ME NLM) and a conventional denoising CNN called UnetwFBP in terms of root mean square error (RMSE) in test set reconstructions and their respective matrix inversion material decompositions. Qualitative results in reconstruction and material decomposition domains revealed that UnetU is the best approximation of iterative reconstruction. In reconstructions with varying undersampling factors from a high dose ex vivo scan, UnetU consistently gave higher structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) to the fully sampled iterative reconstruction than ME NLM and UnetwFBP. This research demonstrates UnetU's potential as a fast (i.e., 15 times faster than iterative reconstruction) and generalizable approach for PCCT denoising, holding promise for advancing preclinical PCCT research.

Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden.

The purpose of this study was to investigate if radiomic analysis based on spectral micro-CT with nanoparticle contrast-enhancement can differentiate tumors based on lymphocyte burden. High mutational load transplant soft tissue sarcomas were initiated in Rag2+/- and Rag2-/- mice to model varying lymphocyte burden. Mice received radiation therapy (20 Gy) to the tumor-bearing hind limb and were injected with a liposomal iodinated contrast agent. Five days later, animals underwent conventional micro-CT imaging using an energy integrating detector (EID) and spectral micro-CT imaging using a photon-counting detector (PCD). Tumor volumes and iodine uptakes were measured. The radiomic features (RF) were grouped into feature-spaces corresponding to EID, PCD, and spectral decomposition images. The RFs were ranked to reduce redundancy and increase relevance based on TL burden. A stratified repeated cross validation strategy was used to assess separation using a logistic regression classifier. Tumor iodine concentration was the only significantly different conventional tumor metric between Rag2+/- (TLs present) and Rag2-/- (TL-deficient) tumors. The RFs further enabled differentiation between Rag2+/- and Rag2-/- tumors. The PCD-derived RFs provided the highest accuracy (0.68) followed by decomposition-derived RFs (0.60) and the EID-derived RFs (0.58). Such non-invasive approaches could aid in tumor stratification for cancer therapy studies.