Photon-counting cine-cardiac CT in the mouse

High-resolution hybrid micro-CT imaging pipeline for mouse brain region segmentation and volumetric morphometry

BACKGROUND

Brain region segmentation and morphometry in humanized apolipoprotein E (APOE) mouse models with a human NOS2 background (HN) contribute to Alzheimer's disease (AD) research by demonstrating how various risk factors affect the brain. Photon-counting detector (PCD) micro-CT provides faster scan times than MRI, with superior contrast and spatial resolution to energy-integrating detector (EID) micro-CT. This paper presents a pipeline for mouse brain imaging, segmentation, and morphometry from PCD micro-CT.

METHODS

We used brains of 26 mice from 3 genotypes (APOE22HN, APOE33HN, APOE44HN). The pipeline included PCD and EID micro-CT scanning, hybrid (PCD and EID) iterative reconstruction, and brain region segmentation using the Small Animal Multivariate Brain Analysis (SAMBA) tool. We applied SAMBA to transfer brain region labels from our new PCD CT atlas to individual PCD brains via diffeomorphic registration. Region-based and voxel-based analyses were used for comparisons by genotype and sex.

RESULTS

Together, PCD and EID scanning take ~5 hours to produce images with a voxel size of 22 μm, which is faster than MRI protocols for mouse brain morphometry with voxel size above 40 μm. Hybrid iterative reconstruction generates PCD images with minimal artifacts and higher spatial resolution and contrast than EID images. Our PCD atlas is qualitatively and quantitatively similar to the prior MRI atlas and successfully transfers labels to PCD brains in SAMBA. Male and female mice had significant volume differences in 26 regions, including parts of the entorhinal cortex and cingulate cortex. APOE22HN brains were larger than APOE44HN brains in clusters from the hippocampus, a region where atrophy is associated with AD.

CONCLUSIONS

This work establishes a pipeline for mouse brain analysis using PCD CT, from staining to imaging and labeling brain images. Our results validate the effectiveness of the approach, setting a foundation for research on AD mouse models while reducing scanning durations.

Report on the AAPM deep-learning spectral CT Grand Challenge

BACKGROUND

This Special Report summarizes the 2022 AAPM Grand Challenge on Deep-Learning spectral Computed Tomography (DL-spectral CT) image reconstruction.

PURPOSE

The purpose of the challenge is to develop the most accurate image reconstruction algorithm possible for solving the inverse problem associated with a fast kilovolt switching dual-energy CT scan using a three tissue-map decomposition. Participants could choose to use a deep-learning (DL), iterative, or a hybrid approach.

METHODS

The challenge is based on a 2D breast CT simulation, where the simulated breast phantom consists of three tissue maps: adipose, fibroglandular, and calcification distributions. The phantom specification is stochastic so that multiple realizations can be generated for DL approaches. A dual-energy scan is simulated where the x-ray source potential of successive views alternates between 50 and 80 kilovolts (kV). A total of 512 views are generated, yielding 256 views for each source voltage. We generate 50 and 80 kV images by use of filtered back-projection (FBP) on negative logarithm processed transmission data. For participants who develop a DL approach, 1000 cases are available. Each case consists of the three 512 × 512 tissue maps, 50 and 80-kV transmission data sets and their corresponding FBP images. The goal of the DL network would then be to predict the material maps from either the transmission data, FBP images, or a combination of the two. For participants developing a physics-based approach, all of the required modeling parameters are made available: geometry, spectra, and tissue attenuation curves. The provided information also allows for hybrid approaches where physics is exploited as well as information about the scanned object derived from the 1000 training cases. Final testing is performed by computation of root-mean-square error (RMSE) for predictions on the tissue maps from 100 new cases.

RESULTS

Test phase submission were received from 18 research groups. Of the 18 submissions, 17 were results obtained with algorithms that involved DL. Only the second place finishing team developed a physics-based image reconstruction algorithm. Both the winning and second place teams had highly accurate results where the RMSE was nearly zero to single floating point precision. Results from the top 10 also achieved a high degree of accuracy; and as a result, this special report outlines the methodology developed by each of these groups.

CONCLUSIONS

The DL-spectral CT challenge successfully established a forum for developing image reconstruction algorithms that address an important inverse problem relevant for spectral CT.

Quantitative multi-energy micro-CT: A simulation and phantom study for simultaneous imaging of four different contrast materials using an energy integrating detector

Emerging from the development of single-energy Computed Tomography (CT) and Dual-Energy Computed Tomography, Multi-Energy Computed Tomography (MECT) is a promising tool allowing advanced material and tissue decomposition and thereby enabling the use of multiple contrast materials in preclinical research. The scope of this work was to evaluate whether a usual preclinical micro-CT system is applicable for the decomposition of different materials using MECT together with a matrix-inversion method and how different changes of the measurement-environment affect the results. A matrix-inversion based algorithm to differentiate up to five materials (iodine, iron, barium, gadolinium, residual material) by applying four different acceleration voltages/energy levels was established. We carried out simulations using different ratios and concentrations (given in fractions of volume units, VU) of the four different materials (plus residual material) at different noise-levels for 30 keV, 40 keV, 50 keV, 60 keV, 80 keV and 100 keV (monochromatic). Our simulation results were then confirmed by using region of interest-based measurements in a phantom-study at corresponding acceleration voltages. Therefore, different mixtures of contrast materials were scanned using a micro-CT. Voxel wise evaluation of the phantom imaging data was conducted to confirm its usability for future imaging applications and to estimate the influence of varying noise-levels, scattering, artifacts and concentrations. The analysis of our simulations showed the smallest deviation of 0.01 (0.003-0.15) VU between given and calculated concentrations of the different contrast materials when using an energy-combination of 30 keV, 40 keV, 50 keV and 100 keV for MECT. Subsequent MECT phantom measurements, however, revealed a combination of acceleration voltages of 30 kV, 40 kV, 60 kV and 100 kV as most effective for performing material decomposition with a deviation of 0.28 (0-1.07) mg/ml. The feasibility of our voxelwise analyses using the proposed algorithm was then confirmed by the generation of phantom parameter-maps that matched the known contrast material concentrations. The results were mostly influenced by the noise-level and the concentrations used in the phantoms. MECT using a standard micro-CT combined with a matrix inversion method is feasible at four different imaging energies and allows the differentiation of mixtures of up to four contrast materials plus an additional residual material.

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.

The technical development of photon-counting detector CT

Since 1971 and Hounsfield's first CT system, clinical CT systems have used scintillating energy-integrating detectors (EIDs) that use a two-step detection process. First, the X-ray energy is converted into visible light, and second, the visible light is converted to electronic signals. An alternative, one-step, direct X-ray conversion process using energy-resolving, photon-counting detectors (PCDs) has been studied in detail and early clinical benefits reported using investigational PCD-CT systems. Subsequently, the first clinical PCD-CT system was commercially introduced in 2021. Relative to EIDs, PCDs offer better spatial resolution, higher contrast-to-noise ratio, elimination of electronic noise, improved dose efficiency, and routine multi-energy imaging. In this review article, we provide a technical introduction to the use of PCDs for CT imaging and describe their benefits, limitations, and potential technical improvements. We discuss different implementations of PCD-CT ranging from small-animal systems to whole-body clinical scanners and summarize the imaging benefits of PCDs reported using preclinical and clinical systems. KEY POINTS: • Energy-resolving, photon-counting-detector CT is an important advance in CT technology. • Relative to current energy-integrating scintillating detectors, energy-resolving, photon-counting-detector CT offers improved spatial resolution, improved contrast-to-noise ratio, elimination of electronic noise, increased radiation and iodine dose efficiency, and simultaneous multi-energy imaging. • High-spatial-resolution, multi-energy imaging using energy-resolving, photon-counting-detector CT has been used in investigations into new imaging approaches, including multi-contrast imaging.

MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data

BACKGROUND

The advancement of x-ray CT into the domains of photon counting spectral imaging and dynamic cardiac and perfusion imaging has created many new challenges and opportunities for clinicians and researchers. To address challenges such as dose constraints and scanning times while capitalizing on opportunities such as multi-contrast imaging and low-dose coronary angiography, these multi-channel imaging applications require a new generation of CT reconstruction tools. These new tools should exploit the relationships between imaging channels during reconstruction to set new image quality standards while serving as a platform for direct translation between the preclinical and clinical domains.

PURPOSE

We outline and demonstrate a new Multi-Channel Reconstruction (MCR) Toolkit for GPU-based analytical and iterative reconstruction of preclinical and clinical multi-energy and dynamic x-ray CT data. To promote open science, open-source distribution of the Toolkit will coincide with the release of this publication (GPL v3; gitlab.oit.duke.edu/dpc18/mcr-toolkit-public).

METHODS

The MCR Toolkit source code is implemented in C/C++ and NVIDIA's CUDA GPU programming interface, with scripting support from MATLAB and Python. The Toolkit implements matched, separable footprint CT reconstruction operators for projection and backprojection in two geometries: planar, cone-beam CT (CBCT) and 3rd generation, cylindrical multi-detector row CT (MDCT). Analytical reconstruction is performed using filtered backprojection (FBP) for circular CBCT, weighted FBP (WFBP) for helical CBCT, and cone-parallel projection rebinning followed by WFBP for MDCT. Arbitrary combinations of energy and temporal channels are iteratively reconstructed under a generalized multi-channel signal model for joint reconstruction. We solve this generalized model algebraically using the split Bregman optimization method and the BiCGSTAB(l) linear solver interchangeably for both CBCT and MDCT data. Rank-sparse kernel regression (RSKR) and patch-based singular value thresholding (pSVT) are used to regularize the energy and time dimensions, respectively. Under a Gaussian noise model, regularization parameters are estimated automatically from the input data, dramatically reducing algorithm complexity for end users. Multi-GPU parallelization of the reconstruction operators is supported to manage reconstruction times.

RESULTS

Denoising with RSKR and pSVT and post-reconstruction material decomposition are illustrated with preclinical and clinical cardiac photon-counting (PC)CT data. A digital MOBY mouse phantom with cardiac motion is used to illustrate single energy (SE), multi-energy (ME), time resolved (TR), and combined multi-energy and time-resolved (METR) helical, CBCT reconstruction. A fixed set of projection data is used across all reconstruction cases to demonstrate the Toolkit's robustness to increasing data dimensionality. Identical reconstruction code is applied to in vivo cardiac PCCT data acquired in a mouse model of atherosclerosis (METR). Clinical cardiac CT reconstruction is illustrated using the XCAT phantom and the DukeSim CT simulator, while dual-source, dual-energy CT reconstruction is illustrated for data acquired with a Siemens Flash scanner. Benchmarking results with NVIDIA RTX 8000 GPU hardware demonstrate 61%-99% efficiency in scaling computation from one to four GPUs for these reconstruction problems.

CONCLUSIONS

The MCR Toolkit provides a robust solution for temporal and spectral x-ray CT reconstruction problems and was built from the ground up to facilitate translation of CT research and development between preclinical and clinical applications.

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.

Performance of Spectral Photon-Counting Coronary CT Angiography and Comparison with Energy-Integrating-Detector CT: Objective Assessment with Model Observer

AIMS

To evaluate spectral photon-counting CT's (SPCCT) objective image quality characteristics in vitro, compared with standard-of-care energy-integrating-detector (EID) CT.

METHODS

We scanned a thorax phantom with a coronary artery module at 10 mGy on a prototype SPCCT and a clinical dual-layer EID-CT under various conditions of simulated patient size (small, medium, and large). We used filtered back-projection with a soft-tissue kernel. We assessed noise and contrast-dependent spatial resolution with noise power spectra (NPS) and target transfer functions (TTF), respectively. Detectability indices (d') of simulated non-calcified and lipid-rich atherosclerotic plaques were computed using the non-pre-whitening with eye filter model observer.

RESULTS

SPCCT provided lower noise magnitude (9-38% lower NPS amplitude) and higher noise frequency peaks (sharper noise texture). Furthermore, SPCCT provided consistently higher spatial resolution (30-33% better TTF10). In the detectability analysis, SPCCT outperformed EID-CT in all investigated conditions, providing superior d'. SPCCT reached almost perfect detectability (AUC ≈ 95%) for simulated 0.5-mm-thick non-calcified plaques (for large-sized patients), whereas EID-CT had lower d' (AUC ≈ 75%). For lipid-rich atherosclerotic plaques, SPCCT achieved 85% AUC vs. 77.5% with EID-CT.

CONCLUSIONS

SPCCT outperformed EID-CT in detecting simulated coronary atherosclerosis and might enhance diagnostic accuracy by providing lower noise magnitude, markedly improved spatial resolution, and superior lipid core detectability.

Advances in micro-CT imaging of small animals

PURPOSE

Micron-scale computed tomography (micro-CT) imaging is a ubiquitous, cost-effective, and non-invasive three-dimensional imaging modality. We review recent developments and applications of micro-CT for preclinical research.

METHODS

Based on a comprehensive review of recent micro-CT literature, we summarize features of state-of-the-art hardware and ongoing challenges and promising research directions in the field.

RESULTS

Representative features of commercially available micro-CT scanners and some new applications for both in vivo and ex vivo imaging are described. New advancements include spectral scanning using dual-energy micro-CT based on energy-integrating detectors or a new generation of photon-counting x-ray detectors (PCDs). Beyond two-material discrimination, PCDs enable quantitative differentiation of intrinsic tissues from one or more extrinsic contrast agents. When these extrinsic contrast agents are incorporated into a nanoparticle platform (e.g. liposomes), novel micro-CT imaging applications are possible such as combined therapy and diagnostic imaging in the field of cancer theranostics. Another major area of research in micro-CT is in x-ray phase contrast (XPC) imaging. XPC imaging opens CT to many new imaging applications because phase changes are more sensitive to density variations in soft tissues than standard absorption imaging. We further review the impact of deep learning on micro-CT. We feature several recent works which have successfully applied deep learning to micro-CT data, and we outline several challenges specific to micro-CT.

CONCLUSIONS

All of these advancements establish micro-CT imaging at the forefront of preclinical research, able to provide anatomical, functional, and even molecular information while serving as a testbench for translational research.

Dual source hybrid spectral micro-CT using an energy-integrating and a photon-counting detector

Preclinical micro-CT provides a hotbed in which to develop new imaging technologies, including spectral CT using photon counting detector (PCD) technology. Spectral imaging using PCDs promises to expand x-ray CT as a functional imaging modality, capable of molecular imaging, while maintaining CT's role as a powerful anatomical imaging modality. However, the utility of PCDs suffers due to distorted spectral measurements, affecting the accuracy of material decomposition. We attempt to improve material decomposition accuracy using our novel hybrid dual-source micro-CT system which combines a PCD and an energy integrating detector. Comparisons are made between PCD-only and hybrid CT results, both reconstructed with our iterative, multi-channel algorithm based on the split Bregman method and regularized with rank-sparse kernel regression. Multi-material decomposition is performed post-reconstruction for separation of iodine (I), gold (Au), gadolinium (Gd), and calcium (Ca). System performance is evaluated first in simulations, then in micro-CT phantoms, and finally in an in vivo experiment with a genetically modified p53^fl/fl mouse cancer model with Au, Gd, and I nanoparticle (NP)-based contrasts agents. Our results show that the PCD-only and hybrid CT reconstructions offered very similar spatial resolution at 10% MTF (PCD: 3.50 lp mm^-1; hybrid: 3.47 lp mm^-1) and noise characteristics given by the noise power spectrum. For material decomposition we note successful separation of the four basis materials. We found that hybrid reconstruction reduces RMSE by an average of 37% across all material maps when compared to PCD-only of similar dose but does not provide much difference in terms of concentration accuracy. The in vivo results show separation of targeted Au and accumulated Gd NPs in the tumor from intravascular iodine NPs and bone. Hybrid spectral micro-CT can benefit nanotechnology and cancer research by providing quantitative imaging to test and optimize various NPs for diagnostic and therapeutic applications.