Joint Material Decomposition and Scatter Estimation for Spectral CT.

Accurate scatter correction is essential to obtain highquality reconstructions in computed tomography. While many correction strategies for this longstanding issue have been developed, additional efforts may be required for spectral CT imaging - which is particularly sensitive to unmodeled biases. In this work we explore a joint estimation approach within a one-step model-based material decomposition framework to simultaneously estimate material densities and scatter profiles in spectral CT. The method is applied to simulated phantom data obtained using a parametric additive scatter mode, and compared to the unmodeled scatter scenario. In these preliminary experiments, We find that this joint estimation approach has the potential to significantly reduce artifacts associated with unmodeled scatter and to improve material density estimates.

CT Material Decomposition using Spectral Diffusion Posterior Sampling.

In this work, we introduce a new deep learning approach based on diffusion posterior sampling (DPS) to perform material decomposition from spectral CT measurements. This approach combines sophisticated prior knowledge from unsupervised training with a rigorous physical model of the measurements. A faster and more stable variant is proposed that uses a "jumpstarted" process to reduce the number of time steps required in the reverse process and a gradient approximation to reduce the computational cost. Performance is investigated for two spectral CT systems: dual-kVp and dual-layer detector CT. On both systems, DPS achieves high Structure Similarity Index Metric Measure(SSIM) with only 10% of iterations as used in the model-based material decomposition(MBMD). Jumpstarted DPS (JSDPS) further reduces computational time by over 85% and achieves the highest accuracy, the lowest uncertainty, and the lowest computational costs compared to classic DPS and MBMD. The results demonstrate the potential of JSDPS for providing relatively fast and accurate material decomposition based on spectral CT data.

Design Optimization of A Triple-Layer Flat-Panel Detector for Three-Material Decomposition.

Spectral radiography and fluoroscopy with multi-layer flat-panel detectors (FPD) is being actively investigated in a range of clinical applications. For applications involving contrast administration, maximal contrast resolution is achieved when overlaying background anatomy is completely removed. This calls for three-material decomposition (soft tissue, bone, and contrast) enabled by measurements in three energy channels. We have previously demonstrated the feasibility of such decomposition using a triple-layer detector. While algorithmic solutions can be adopted to mitigate noise in the material basis images, in this work, we seek to fundamentally improve the conditioning of the problem through optimized system design. Design parameters include source voltage, the thickness of the top two CsI scintillators, and the thickness of two copper interstitial filters. The design objective is to minimize noise in the basis image containing contrast, chosen as gadolinium in this work to improve separation from bone. The optimized design was compared with other designs with unoptimized scintillator thickness and/or without interstitial filtration. Results show that CsI thickness optimization and interstitial filtration can significantly reduce noise in the gadolinium image by 35.7% and 42.7% respectively within a lung ROI, which in turn boosts detectability of small vessels. Gadolinium and bone signals are separated in all cases. Visualization of coronary vessels is enabled by the combining optimized system design and regularization. Results from this work demonstrate that three-material decomposition can be significantly improved with system design optimization. Optimized designs obtained from this work can inform imaging techniques selection and triple-layer detector fabrication for spectral radiography.

Fourier Diffusion for Sparse CT Reconstruction.

Sparse CT reconstruction continues to be an area of interest in a number of novel imaging systems. Many different approaches have been tried including model-based methods, compressed sensing approaches, and most recently deep-learning-based processing. Diffusion models, in particular, have become extremely popular due to their ability to effectively encode rich information about images and to allow for posterior sampling to generate many possible outputs. One drawback of diffusion models is that their recurrent structure tends to be computationally expensive. In this work we apply a new Fourier diffusion approach that permits processing with many fewer time steps than the standard scalar diffusion model. We present an extension of the Fourier diffusion technique and evaluate it in a simulated breast cone-beam CT system with a sparse view acquisition.

End-to-end Deep Learning Restoration of GLCM Features from blurred and noisy images.

Radiomics involves the quantitative analysis of medical images to provide useful information for a range of clinical applications including disease diagnosis, treatment assessment, etc. However, the generalizability of radiomics model is often challenged by undesirable variability in radiomics feature values introduced by different scanners and imaging conditions. To address this issue, we developed a novel dual-domain deep learning algorithm to recover ground truth feature values given known blur and noise in the image. The network consists of two U-Nets connected by a differentiable GLCM estimator. The first U-Net restores the image, and the second restores the GLCM. We evaluated the performance of the network on lung CT image patches in terms of both closeness of recovered feature values to the ground truth and accuracy of classification between normal and COVID lungs. Performance was compared with an image restoration-only method and an analytical method developed in previous work. The proposed network outperforms both methods, achieving GLCM with the lowest mean-absolute-error from ground truth. Recovered GLCM feature values from the proposed method, on average, is within 2.19% error to the ground truth. Classification performance using recovered features from the network closely matches the "best case" performance achieved using ground truth feature values. The deep learning method has been shown to be a promising tool for radiomics standardization, paving the way for more reliable and repeatable radiomics models.

CT reconstruction using diffusion posterior sampling conditioned on a nonlinear measurement model.

Purpose: Recently, diffusion posterior sampling (DPS), where score-based diffusion priors are combined with likelihood models, has been used to produce high-quality computed tomography (CT) images given low-quality measurements. This technique permits one-time, unsupervised training of a CT prior, which can then be incorporated with an arbitrary data model. However, current methods rely on a linear model of X-ray CT physics to reconstruct. Although it is common to linearize the transmission tomography reconstruction problem, this is an approximation to the true and inherently nonlinear forward model. We propose a DPS method that integrates a general nonlinear measurement model. Approach: We implement a traditional unconditional diffusion model by training a prior score function estimator and apply Bayes' rule to combine this prior with a measurement likelihood score function derived from the nonlinear physical model to arrive at a posterior score function that can be used to sample the reverse-time diffusion process. We develop computational enhancements for the approach and evaluate the reconstruction approach in several simulation studies. Results: The proposed nonlinear DPS provides improved performance over traditional reconstruction methods and DPS with a linear model. Moreover, as compared with a conditionally trained deep learning approach, the nonlinear DPS approach shows a better ability to provide high-quality images for different acquisition protocols. Conclusion: This plug-and-play method allows the incorporation of a diffusion-based prior with a general nonlinear CT measurement model. This permits the application of the approach to different systems, protocols, etc., without the need for any additional training.

Multi-Material Decomposition Using Spectral Diffusion Posterior Sampling.

Many spectral CT applications require accurate material decomposition. Existing material decomposition algorithms are often susceptible to significant noise magnification or, in the case of one-step model-based approaches, hampered by slow convergence rates and large computational requirements. In this work, we proposed a novel framework - spectral diffusion posterior sampling (spectral DPS) - for one-step reconstruction and multi-material decomposition, which combines sophisticated prior information captured by one-time unsupervised learning and an arbitrary analytic physical system model. Spectral DPS is built upon a general DPS framework for nonlinear inverse problems. Several strategies developed in previous work, including jumpstart sampling, Jacobian approximation, and multi-step likelihood updates are applied facilitate stable and accurate decompositions. The effectiveness of spectral DPS was evaluated on a simulated dual-layer and a kV-switching spectral system as well as on a physical cone-beam CT (CBCT) test bench. In simulation studies, spectral DPS improved PSNR by 27.49% to 71.93% over baseline DPS and by 26.53% to 57.30% over MBMD, depending on the the region of interest. In physical phantom study, spectral DPS achieved a <1% error in estimating the mean density in a homogeneous region. Compared with baseline DPS, spectral DPS effectively avoided generating false structures in the homogeneous phantom and reduced the variability around edges. Both simulation and physical phantom studies demonstrated the superior performance of spectral DPS for stable and accurate material decomposition.

Generative Adversarial Networks with Radiomics Supervision for Lung Lesion Generation.

Data-driven methods for lesion generation are quickly emerging due to the need for realistic imaging targets for image quality assessment and virtual clinical trials. We proposed a generative adversarial network (GAN) architecture for conditional generation of lung lesions based on user-specified classes of lesion size and solidity. The network consists of two discriminators, one for volumetric lesion data, and one for radiomics features derived from the lesion volume. A Wasserstein loss with gradient penalty was adopted for each discriminator. Training data were drawn from contoured and annotated lesions from a public lung CT database. Four quantitative evaluation methods were devised to assess the network performance: 1) overfitting (similarity between generated and real lesions), 2) diversity (similarity among generated lesions), 3) conditional consistency (capability of generating lesions according to user-specified classes), and 4) similarity in distributions of various lesion properties between the generated and real lesions. Ablation studies were also performed to investigate the importance of individual network component. The proposed network was found to generate lesions that resemble real lesions by visual inspection. Solid lesions are distinct from non-solid ones, and lesion sizes largely correspond to their specified classes. With a classifier trained on real lesions, the classification accuracies of generated and real lesions in both solid and non-solid classes are similar. Radiomics features of generated and real lesions were found to have similar distributions, indicated by the relatively low Kullback-Leibler (KL) divergence values. Furthermore, the correlations between pairwise radiomics features in generated lesions were comparable to those of real lesions. The proposed network presents a promising approach for generating realistic lesions with clinically relevant features crucial for the comprehensive assessment of medical imaging systems.

Impact of patient habitus and acquisition protocol on iodine quantification in dual-source photon-counting computed tomography.

Purpose: Evaluation of iodine quantification accuracy with varying iterative reconstruction level, patient habitus, and acquisition mode on a first-generation dual-source photon-counting computed tomography (PCCT) system. Approach: A multi-energy CT phantom with and without its extension ring equipped with various iodine inserts (0.2 to 15.0 mg/ml) was scanned over a range of radiation dose levels ( CTDI vol 0.5 to 15.0 mGy) using two tube voltages (120, 140 kVp) and two different source modes (single-, dual-source). To assess the agreement between nominal and measured iodine concentrations, iodine density maps at different iterative reconstruction levels were utilized to calculate root mean square error (RMSE) and generate Bland-Altman plots by grouping radiation dose levels (ultra-low: < 1.5 ; low: 1.5 to 5; medium: 5 to 15 mGy) and iodine concentrations (low: < 5 ; high: 5 to 15 mg/mL). Results: Overall, quantification of iodine concentrations was accurate and reliable even at ultra-low radiation dose levels. RMSE ranged from 0.25 to 0.37, 0.20 to 0.38, and 0.25 to 0.37 mg/ml for ultra-low, low, and medium radiation dose levels, respectively. Similarly, RMSE was stable at 0.31, 0.28, 0.33, and 0.30 mg/ml for tube voltage and source mode combinations. Ultimately, the accuracy of iodine quantification was higher for the phantom without an extension ring (RMSE 0.21 mg/mL) and did not vary across different levels of iterative reconstruction. Conclusions: The first-generation PCCT allows for accurate iodine quantification over a wide range of iodine concentrations and radiation dose levels. Stable accuracy across iterative reconstruction levels may allow further radiation exposure reductions without affecting quantitative results.

Strategies for CT Reconstruction using Diffusion Posterior Sampling with a Nonlinear Model.

Diffusion Posterior Sampling(DPS) methodology is a novel framework that permits nonlinear CT reconstruction by integrating a diffusion prior and an analytic physical system model, allowing for one-time training for different applications. However, baseline DPS can struggle with large variability, hallucinations, and slow reconstruction. This work introduces a number of strategies designed to enhance the stability and efficiency of DPS CT reconstruction. Specifically, jumpstart sampling allows one to skip many reverse time steps, significantly reducing the reconstruction time as well as the sampling variability. Additionally, the likelihood update is modified to simplify the Jacobian computation and improve data consistency more efficiently. Finally, a hyperparameter sweep is conducted to investigate the effects of parameter tuning and to optimize the overall reconstruction performance. Simulation studies demonstrated that the proposed DPS technique achieves up to 46.72% PSNR and 51.50% SSIM enhancement in a low-mAs setting, and an over 31.43% variability reduction in a sparse-view setting. Moreover, reconstruction time is sped up from >23.5 s/slice to <1.5 s/slice. In a physical data study, the proposed DPS exhibits robustness on an anthropomorphic phantom reconstruction which does not strictly follow the prior distribution. Quantitative analysis demonstrates that the proposed DPS can accommodate various dose levels and number of views. With 10% dose, only a 5.60% and 4.84% reduction of PSNR and SSIM was observed for the proposed approach. Both simulation and phantom studies demonstrate that the proposed method can significantly improve reconstruction accuracy and reduce computational costs, greatly enhancing the practicality of DPS CT reconstruction.

CT Reconstruction using Diffusion Posterior Sampling conditioned on a Nonlinear Measurement Model.

Diffusion models have been demonstrated as powerful deep learning tools for image generation in CT reconstruction and restoration. Recently, diffusion posterior sampling, where a score-based diffusion prior is combined with a likelihood model, has been used to produce high quality CT images given low-quality measurements. This technique is attractive since it permits a one-time, unsupervised training of a CT prior; which can then be incorporated with an arbitrary data model. However, current methods rely on a linear model of x-ray CT physics to reconstruct or restore images. While it is common to linearize the transmission tomography reconstruction problem, this is an approximation to the true and inherently nonlinear forward model. We propose a new method that solves the inverse problem of nonlinear CT image reconstruction via diffusion posterior sampling. We implement a traditional unconditional diffusion model by training a prior score function estimator, and apply Bayes rule to combine this prior with a measurement likelihood score function derived from the nonlinear physical model to arrive at a posterior score function that can be used to sample the reverse-time diffusion process. This plug-and-play method allows incorporation of a diffusion-based prior with generalized nonlinear CT image reconstruction into multiple CT system designs with different forward models, without the need for any additional training. We develop the algorithm that performs this reconstruction, including an ordered-subsets variant for accelerated processing and demonstrate the technique in both fully sampled low dose data and sparse-view geometries using a single unsupervised training of the prior.

Lifelike PixelPrint phantoms for assessing clinical image quality and dose reduction capabilities of a deep learning CT reconstruction algorithm.

Deep learning CT reconstruction (DLR) has become increasingly popular as a method for improving image quality and reducing radiation exposure. Due to their nonlinear nature, these algorithms result in resolution and noise performance which are object-dependent. Therefore, traditional CT phantoms, which lack realistic tissue morphology, have become inadequate for assessing clinical imaging performance. We propose to utilize 3D-printed PixelPrint phantoms, which exhibit lifelike attenuation profiles, textures, and structures, as a better tool for evaluating DLR performance. In this study, we evaluate a DLR algorithm (Precise Image (PI), Philips Healthcare) using a custom PixelPrint lung phantom and perform head-to-head comparisons between DLR, iterative reconstruction, and filtered back projection (FBP) with scans acquired at a broad range of radiation exposures (CTDIvol: 0.5, 1, 2, 4, 6, 9, 12, 15, 19, and 20 mGy). We compared the performance of each resultant image using noise, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature-based similarity index (FSIM), information theoretic-based statistic similarity measure (ISSM) and universal image quality index (UIQ). Iterative reconstruction at 9 mGy matches the image quality of FBP at 12 mGy (diagnostic reference level) for all metrics, demonstrating a dose reduction capability of 25%. Meanwhile, DLR matches the image quality of diagnostic reference level FBP images at doses between 4 - 9 mGy, demonstrating dose reduction capabilities between 25% and 67%. This study shows that DLR allows for reduced radiation dose compared to both FBP and iterative reconstruction without compromising image quality. Furthermore, PixelPrint phantoms offer more realistic testing conditions compared to traditional phantoms in the evaluation of novel CT technologies. This, in turn, promotes the translation of new technologies, such as DLR, into clinical practice.

Hybrid spectral CT system with clinical rapid kVp-switching x-ray tube and dual-layer detector for improved iodine quantification.

Spectral computed tomography (CT) is a powerful diagnostic tool offering quantitative material decomposition results that enhance clinical imaging by providing physiologic and functional insights. Iodine, a widely used contrast agent, improves visualization in various clinical contexts. However, accurately detecting low-concentration iodine presents challenges in spectral CT systems, particularly crucial for conditions like pancreatic cancer assessment. In this study, we present preliminary results from our hybrid spectral CT instrumentation which includes clinical-grade hardware (rapid kVp-switching x-ray tube, dual-layer detector). This combination expands spectral datasets from two to four channels, wherein we hypothesize improved quantification accuracy for low-dose and low-iodine concentration cases. We modulate the system duty cycle to evaluate its impact on quantification noise and bias. We evaluate iodine quantification performance by comparing two hybrid weighting strategies alongside rapid kVp-switching. This evaluation is performed with a polyamide phantom containing seven iodine inserts ranging from 0.5 to 20 mg/mL. In comparison to alternative methodologies, the maximum separation configuration, incorporating data from both the 80 kVp, low photon energy detector layer and the 140 kVp, high photon energy detector layer produces spectral images containing low quantitative noise and bias. This study presents initial evaluations on a hybrid spectral CT system, leveraging clinical hardware to demonstrate the potential for enhanced precision and sensitivity in spectral imaging. This research holds promise for advancing spectral CT imaging performance across diverse clinical scenarios.

3D printed phantom with 12 000 submillimeter lesions to improve efficiency in CT detectability assessment.

BACKGROUND: The detectability performance of a CT scanner is difficult to precisely quantify when nonlinearities are present in reconstruction. An efficient detectability assessment method that is sensitive to small effects of dose and scanner settings is desirable. We previously proposed a method using a search challenge instrument: a phantom is embedded with hundreds of lesions at random locations, and a model observer is used to detect lesions. Preliminary tests in simulation and a prototype showed promising results. PURPOSE: In this work, we fabricated a full-size search challenge phantom with design updates, including changes to lesion size, contrast, and number, and studied our implementation by comparing the lesion detectability from a nonprewhitening (NPW) model observer between different reconstructions at different exposure levels, and by estimating the instrument sensitivity to detect changes in dose. METHODS: Designed to fit into QRM anthropomorphic phantoms, our search challenge phantom is a cylindrical insert 10 cm wide and 4 cm thick, embedded with 12 000 lesions (nominal width of 0.6 mm, height of 0.8 mm, and contrast of -350 HU), and was fabricated using PixelPrint, a 3D printing technique. The insert was scanned alone at a high dose to assess printing accuracy. To evaluate lesion detectability, the insert was placed in a QRM thorax phantom and scanned from 50 to 625 mAs with increments of 25 mAs, once per exposure level, and the average of all exposure levels was used as high-dose reference. Scans were reconstructed with three different settings: filtered-backprojection (FBP) with Br40 and Br59, and Sinogram Affirmed Iterative Reconstruction (SAFIRE) with strength level 5 and Br59 kernel. An NPW model observer was used to search for lesions, and detection performance of different settings were compared using area under the exponential transform of free response ROC curve (AUC). Using propagation of uncertainty, the sensitivity to changes in dose was estimated by the percent change in exposure due to one standard deviation of AUC, measured from 5 repeat scans at 100, 200, 300, and 400 mAs. RESULTS: The printed insert lesions had an average position error of 0.20 mm compared to printing reference. As the exposure level increases from 50 mAs to 625 mAs, the lesion detectability AUCs increase from 0.38 to 0.92, 0.42 to 0.98, and 0.41 to 0.97 for FBP Br40, FBP Br59, and SAFIRE Br59, respectively, with a lower rate of increase at higher exposure level. FBP Br59 performed best with AUC 0.01 higher than SAFIRE Br59 on average and 0.07 higher than FBP Br40 (all P < 0.001). The standard deviation of AUC was less than 0.006, and the sensitivity to detect changes in mAs was within 2% for FBP Br59. CONCLUSIONS: Our 3D-printed search challenge phantom with 12 000 submillimeter lesions, together with an NPW model observer, provide an efficient CT detectability assessment method that is sensitive to subtle effects in reconstruction and is sensitive to small changes in dose.

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm.

Objective. Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.Method. The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error, structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.Results.DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25%-83% in the small phantom and by 50%-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR.Conclusion. DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

Fully automatic online geometric calibration for non-circular cone-beam CT orbits using fiducials with unknown placement.

BACKGROUND: Cone-beam CT (CBCT) with non-circular scanning orbits can improve image quality for 3D intraoperative image guidance. However, geometric calibration of such scans can be challenging. Existing methods typically require a prior image, specialized phantoms, presumed repeatable orbits, or long computation time. PURPOSE: We propose a novel fully automatic online geometric calibration algorithm that does not require prior knowledge of fiducial configuration. The algorithm is fast, accurate, and can accommodate arbitrary scanning orbits and fiducial configurations. METHODS: The algorithm uses an automatic initialization process to eliminate human intervention in fiducial localization and an iterative refinement process to ensure robustness and accuracy. We provide a detailed explanation and implementation of the proposed algorithm. Physical experiments on a lab test bench and a clinical robotic C-arm scanner were conducted to evaluate spatial resolution performance and robustness under realistic constraints. RESULTS: Qualitative and quantitative results from the physical experiments demonstrate high accuracy, efficiency, and robustness of the proposed method. The spatial resolution performance matched that of our existing benchmark method, which used a 3D-2D registration-based geometric calibration algorithm. CONCLUSIONS: We have demonstrated an automatic online geometric calibration method that delivers high spatial resolution and robustness performance. This methodology enables arbitrary scan trajectories and should facilitate translation of such acquisition methods in a clinical setting.

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm.

Objective: Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels. Approach: The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different sized extension rings to mimic a small and medium sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error (RMSE), structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image. Main Results: DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25-83% in the small phantom and by 50-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR with a non-anatomical physics phantom. Significance: DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

Notice of Removal: Fourier Diffusion Models: A Method to Control MTF and NPS in Score-Based Stochastic Image Generation.

Removed.

Design and fabrication of 3D-printed patient-specific soft tissue and bone phantoms for CT imaging.

The objective of this study is to create patient-specific phantoms for computed tomography (CT) that possess accurate densities and exhibit visually realistic image textures. These qualities are crucial for evaluating CT performance in clinical settings. The study builds upon a previously presented 3D printing method (PixelPrint) by incorporating soft tissue and bone structures. We converted patient DICOM images directly into 3D printer instructions using PixelPrint and utilized calcium-doped filament to increase the Hounsfield unit (HU) range. Density was modeled by controlling printing speed according to volumetric filament ratio to emulate attenuation profiles. We designed micro-CT phantoms to demonstrate the reproducibility, and to determine mapping between filament ratios and HU values on clinical CT systems. Patient phantoms based on clinical cervical spine and knee examinations were manufactured and scanned with a clinical spectral CT scanner. The CT images of the patient-based phantom closely resembled original CT images in visual texture and contrast. Micro-CT analysis revealed minimal variations between prints, with an overall deviation of ± 0.8% in filament line spacing and ± 0.022 mm in line width. Measured differences between patient and phantom were less than 12 HU for soft tissue and 15 HU for bone marrow, and 514 HU for cortical bone. The calcium-doped filament accurately represented bony tissue structures across different X-ray energies in spectral CT (RMSE ranging from ± 3 to ± 28 HU, compared to 400 mg/ml hydroxyapatite). In conclusion, this study demonstrated the possibility of extending 3D-printed patient-based phantoms to soft tissue and bone structures while maintaining accurate organ geometry, image texture, and attenuation profiles.

Three-material decomposition using a dual-layer flat panel detector in the presence of soft tissue motion.

Digital subtraction angiography (DSA) is a widely used technique for the visualization of contrast-enhanced structures. However, temporal subtraction DSA is challenged by misregistration artifacts due to patient motion and incomplete separation of iodine contrast agent from background soft tissue and bone. In this work, we propose an approach that allows three-material decomposition using a dual-layer flat panel detector in the presence of soft tissue motion. We assume the calcium signal (bone) remains stationary in the pre- and post-contrast images but allow soft tissues to move freely (e.g. cardiac motion). The dual-layer pre- and post-injection images form and ensemble of four measurements that permits material decomposition of four bases (pre- and post-injection soft tissue, calcium, and iodine). We apply two different processing techniques: 1) a modified lookup table and; 2) a model-based material estimation. These are compared with previously proposed DSA methods using temporal subtraction and hybrid (dual-energy) subtraction. Investigations were performed using an XCAT thorax phantom simulating a breath-hold. The pre- and post-contrast measurements were simulated at different time points within a cardiac cycle. Both the lookup table and model-based algorithms eliminate motion artifact as a result of soft tissue motion and allow good separation of iodine, bone, and soft tissue. While the lookup table algorithm contains high noise at the simulated dose level, the model-based algorithm produced iodine images that allow the visualization of major vessels around the heart. In contrast, traditional temporal DSA is susceptible to subtraction artifacts and hybrid DSA shows increased noise.