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.

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.

Spectral performance evaluation of a second-generation spectral detector CT.

PURPOSE: The aim of this study was to characterize a second-generation wide-detector dual-layer spectral computed tomography (CT) system for material quantification accuracy, acquisition parameter and patient size dependencies, and tissue characterization capabilities. METHODS: A phantom with multiple tissue-mimicking and material-specific inserts was scanned with a dual-layer spectral detector CT using different tube voltages, collimation widths, radiation dose levels, and size configurations. Accuracy of iodine density maps and virtual monoenergetic images (MonoE) were investigated. Additionally, differences between conventional and MonoE 70 keV images were calculated to evaluate acquisition parameter and patient size dependencies. To demonstrate material quantification and differentiation, liver-mimicking inserts with adipose and iron were analyzed with a two-base decomposition utilizing MonoE 50 and 150 keV, and root mean square error (RMSE) for adipose and iron content was reported. RESULTS: Measured inserts exhibited quantitative accuracy across a wide range of MonoE levels. MonoE 70 keV images demonstrated reduced dependence compared to conventional images for phantom size (1 vs. 27 HU) and acquisition parameters, particularly tube voltage (4 vs. 37 HU). Iodine density quantification was successful with errors ranging from -0.58 to 0.44 mg/mL. Similarly, inserts with different amounts of adipose and iron were differentiated, and the small deviation in values within inserts corresponded to a RMSE of 3.49 ± 1.76% and 1.67 ± 0.84 mg/mL for adipose and iron content, respectively. CONCLUSION: The second-generation dual-layer CT enables acquisition of quantitatively accurate spectral data without compromises from differences in patient size and acquisition parameters.

Reproducible spectral CT thermometry with liver-mimicking phantoms for image-guided thermal ablation.

Objectives. Evaluate the reproducibility, temperature tolerance, and radiation dose requirements of spectral CT thermometry in tissue-mimicking phantoms to establish its utility for non-invasive temperature monitoring of thermal ablations.Methods. Three liver mimicking phantoms embedded with temperature sensors were individually scanned with a dual-layer spectral CT at different radiation dose levels during heating (35 °C-80 °C). Physical density maps were reconstructed from spectral results using varying reconstruction parameters. Thermal volumetric expansion was then measured at each temperature sensor every 5 °C in order to establish a correlation between physical density and temperature. Linear regressions were applied based on thermal volumetric expansion for each phantom, and coefficient of variation for fit parameters was calculated to characterize reproducibility of spectral CT thermometry. Additionally, temperature tolerance was determined to evaluate effects of acquisition and reconstruction parameters. The resulting minimum radiation dose to meet the clinical temperature accuracy requirement was determined for each slice thickness with and without additional denoising.Results. Thermal volumetric expansion was robustly replicated in all three phantoms, with a correlation coefficient variation of only 0.43%. Similarly, the coefficient of variation for the slope and intercept were 9.6% and 0.08%, respectively, indicating reproducibility of the spectral CT thermometry. Temperature tolerance ranged from 2 °C to 23 °C, decreasing with increased radiation dose, slice thickness, and iterative reconstruction level. To meet the clinical requirement for temperature tolerance, the minimum required radiation dose ranged from 20, 30, and 57 mGy for slice thickness of 2, 3, and 5 mm, respectively, but was reduced to 2 mGy with additional denoising.Conclusions. Spectral CT thermometry demonstrated reproducibility across three liver-mimicking phantoms and illustrated the clinical requirement for temperature tolerance can be met for different slice thicknesses. The reproducibility and temperature accuracy of spectral CT thermometry enable its clinical application for non-invasive temperature monitoring of thermal ablation.

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.

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.

PixelPrint: A collection of three-dimensional printed CT phantoms of different respiratory diseases.

Imaging is often a first-line method for diagnostics and treatment. Radiological workflows increasingly mine medical images for quantifiable features. Variability in device/vendor, acquisition protocol, data processing, etc., can dramatically affect quantitative measures, including radiomics. We recently developed a method (PixelPrint) for 3D-printing lifelike computed tomography (CT) lung phantoms, paving the way for future diagnostic imaging standardization. PixelPrint generates phantoms with accurate attenuation profiles and textures by directly translating clinical images into printer instructions that control density on a voxel-by-voxel basis. The present study introduces a library of 3D printed lung phantoms covering a wide range of lung diseases, including usual interstitial pneumonia with advanced fibrosis, chronic hypersensitivity pneumonitis, secondary tuberculosis, cystic fibrosis, Kaposi sarcoma, and pulmonary edema. CT images of the patient-based phantom are qualitatively comparable to original CT images, both in texture, resolution and contrast levels allowing for clear visualization of even subtle imaging abnormalities. The variety of cases chosen for printing include both benign and malignant pathology causing a variety of alveolar and advanced interstitial abnormalities, both clearly visualized on the phantoms. A comparison of regions of interest revealed differences in attenuation below 6 HU. Identical features on the patient and the phantom have a high degree of geometrical correlation, with differences smaller than the intrinsic spatial resolution of the scans. Using PixelPrint, it is possible to generate CT phantoms that accurately represent different pulmonary diseases and their characteristic imaging features.

Revealing pelvic structures in the presence of metal hip prothesis via non-circular CBCT orbits.

As the expansion of Cone Beam CT (CBCT) to new interventional procedures continues, the burdensome challenge of metal artifacts remains. Photon starvation and beam hardening from metallic implants and surgical tools in the field of view can result in the anatomy of interest being partially or fully obscured by imaging artifacts. Leveraging the flexibility of modern robotic CBCT imaging systems, implementing non-circular orbits designed for reducing metal artifacts by ensuring data-completeness during acquisition has become a reality. Here, we investigate using non-circular orbits to reduce metal artifacts arising from metallic hip prostheses when imaging pelvic anatomy. As a first proof-of-concept, we implement a sinusoidal and a double-circle-arc orbit on a CBCT test bench, imaging a physical pelvis phantom, with two metal hip prostheses, housing a 3D-printed iodine-filled radial line-pair target. A standard circular orbit implemented with the CBCT test bench acted as comparator. Imaging data collection and processing, geometric calibration and image reconstruction was completed using in-house developed software programs. Imaging with the standard circular orbit, image artifacts were observed in the pelvic bones and only 33 out of the possible 45 line-pairs of the radial line-pair target were partially resolvable in the reconstructed images. Comparatively, imaging with both the sinusoid and double-circle-arc orbits reduced artifacts in the surrounding anatomy and enabled all 45 line-pairs to be visibly resolved in the reconstructed images. These results indicate the potential of non-circular orbits to assist in revealing previously obstructed structures in the pelvic region in the presence of metal hip prosthesis.

Reproducible spectral CT thermometry with liver-mimicking phantoms for image-guided thermal ablation.

Objectives: Evaluate the reproducibility, temperature sensitivity, and radiation dose requirements of spectral CT thermometry in tissue-mimicking phantoms to establish its utility for non-invasive temperature monitoring of thermal ablations. Materials and Methods: Three liver mimicking phantoms embedded with temperature sensors were individually scanned with a dual-layer spectral CT at different radiation dose levels during heating and cooling (35 to 80 °C). Physical density maps were reconstructed from spectral results using a range of reconstruction parameters. Thermal volumetric expansion was then measured at each temperature sensor every 5°C in order to establish a correlation between physical density and temperature. Linear regressions were applied based on thermal volumetric expansion for each phantom, and coefficient of variation for fit parameters was calculated to characterize reproducibility of spectral CT thermometry. Additionally, temperature sensitivity was determined to evaluate the effect of acquisition parameters, reconstruction parameters, and image denoising. The resulting minimum radiation dose to meet the clinical temperature sensitivity requirement was determined for each slice thickness, both with and without additional denoising. Results: Thermal volumetric expansion was robustly replicated in all three phantoms, with a correlation coefficient variation of only 0.43%. Similarly, the coefficient of variation for the slope and intercept were 9.6% and 0.08%, respectively, indicating reproducibility of the spectral CT thermometry. Temperature sensitivity ranged from 2 to 23 °C, decreasing with increased radiation dose, slice thickness, and iterative reconstruction level. To meet the clinical requirement for temperature sensitivity, the minimum required radiation dose ranged from 20, 30, and 57 mGy for slice thickness of 2, 3, and 5 mm, respectively, but was reduced to 2 mGy with additional denoising. Conclusions: Spectral CT thermometry demonstrated reproducibility across three liver-mimicking phantoms and illustrated the clinical requirement for temperature sensitivity can be met for different slice thicknesses. Moreover, additional denoising enables the use of more clinically relevant radiation doses, facilitating the clinical translation of spectral CT thermometry. The reproducibility and temperature accuracy of spectral CT thermometry enable its clinical application for non-invasive temperature monitoring of thermal ablation.

CT-Guided Liver Biopsy: Evaluation of Spectral Data From Dual-Layer Detector CT for Improved Lesion Detection.

PURPOSE: Evaluation of dual-layer spectral computed tomography (CT) for contrast enhancement during image-guided biopsy of liver lesions using virtual monoenergetic images (VMI) and virtual non-contrast (VNC) images. METHODS: Spectral CT data of 20 patients receiving CT-guided needle biopsy of focal liver lesions were used to generate VMI at energy levels from 40 to 200 keV and VNC images. Images were analyzed objectively regarding contrast-to-noise ratio between lesion center (CNRcent) or periphery (CNRperi) and normal liver parenchyma. Lesion visibility and image quality were evaluated on a 4-point Likert scale by two radiologists. RESULTS: Using VMI/VNC images, readers reported an increased visibility of the lesion compared to the conventional CT images in 18/20 cases. In 75% of cases, the highest visibility was derived by VMI-40. Showing all reconstructions simultaneously, VMI-40 offered the highest visibility in 75% of cases, followed by VNC in 12.5% of cases. Either CNRcent (17/20) or/and CNRperi (17/20) was higher (CNR increase > 50%) in 19/20 cases for VMI-40 or VNC images compared to conventional CT images. VMI-40 showed the highest CNRcent in 14 cases and the highest CNRperi in 12 cases. High image quality was present for all reconstructions with a minimum median of 3.5 for VMI-40 and VMI-50. CONCLUSIONS: When implemented in the CT scanner software, automated contrast enhancement of liver lesions during image-guided biopsy may facilitate the procedure.

Consistency of spectral results in cardiac dual-source photon-counting CT.

We evaluate stability of spectral results at different heart rates, acquisition modes, and cardiac phases in first-generation clinical dual-source photon-counting CT (PCCT). A cardiac motion simulator with a coronary stenosis mimicking a 50% eccentric calcium plaque was scanned at five different heart rates (0, 60-100 bpm) with the three available cardiac scan modes (high pitch prospectively ECG-triggered spiral, prospectively ECG-triggered axial, retrospectively ECG-gated spiral). Subsequently, full width half max (FWHM) of the stenosis, Dice score (DSC) for the stenosed region, and eccentricity of the non-stenosed region were calculated for virtual monoenergetic images (VMI) at 50, 70, and 150 keV and iodine density maps at both diastole and systole. FWHM averaged differences of - 0.20, - 0.28, and - 0.15 mm relative to static FWHM at VMI 150 keV across acquisition parameters for high pitch prospectively ECG-triggered spiral, prospectively ECG-triggered axial, and retrospectively ECG-gated spiral scans, respectively. Additionally, there was no effect of heart rate and acquisition mode on FWHM at diastole (p-values < 0.001). DSC demonstrated similarity among parameters with standard deviations of 0.08, 0.09, 0.11, and 0.08 for VMI 50, 70, and 150 keV, and iodine density maps, respectively, with insignificant differences at diastole (p-values < 0.01). Similarly, eccentricity illustrated small differences across heart rate and acquisition mode for each spectral result. Consistency of spectral results at different heart rates and acquisition modes for different cardiac phase demonstrates the added benefit of spectral results from PCCT to dual-source CT to further increase confidence in quantification and advance cardiovascular diagnostics.

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 have realistic image texture and densities, which are critical in 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 stone-based filament to increase 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 texture and contrast. Measured differences between patient and phantom were less than 15 HU for soft tissue and bone marrow. The stone-based filament accurately represented bony tissue structures across different X-ray energies, as measured by spectral CT. 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.

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 have realistic image texture and densities, which are critical in 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 stone-based filament to increase 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 texture and contrast. Measured differences between patient and phantom were less than 15 HU for soft tissue and bone marrow. The stone-based filament accurately represented bony tissue structures across different X-ray energies, as measured by spectral CT. 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.

Recent Progress on Passive, Thermally Localized Solar-Driven Multistage Water Evaporation.

Thermally localized solar-driven water evaporation (SWE) in recent years has increasingly been developed due to the potential of cost-efficient freshwater production from small-scale portable devices. In particular, the multistage SWE has attracted much attention as the systems possess mostly a simple foundational structure and high solar-to-thermal conversion output rates, enough to produce freshwater from 1.5 L m-2h-1 (LMH) to 6 LMH. In this study, the currently designed multistage SWE devices were reviewed and examined based on their unique characteristics as well as their performances in freshwater production. The main distinguishing factors in these systems were the condenser staging design and the spectrally selective absorbers either in a form of high solar absorbing material, photovoltaic (PV) cells for water and electricity co-production, and coupling of absorber and solar concentrator. Other elements of the devices involved differences such as the direction of water flow, the number of layers constructed, and the materials used for each layer of the system. The key factors to consider for these systems include the heat and mass transport in the device, solar-to-vapor conversion efficiency, gain output ratio (representing how many times the latent heat has been reused), water production rate/number of stages, and kWh/number of stages. It was evident that most of the studied devices involved slightly different mechanisms and material compositions to draw out higher efficiency rates from the current limitations. The reviewed designs showed the ability to be adopted into small-scale solar desalination allowing for accessibility of sufficient freshwater in needing regions.

Automatic bolus tracking in abdominal CT scans with convolutional neural networks.

Background: Bolus tracking can optimize the time delay between contrast injection and diagnostic scan initiation in contrast-enhanced computed tomography (CT), yet the procedure is time-consuming and subject to inter- and intra-operator variances which affect the enhancement levels in diagnostic scans. The objective of the current study is to use artificial intelligence algorithms to fully automate the bolus tracking procedure in contrast-enhanced abdominal CT exams for improved standardization and diagnostic accuracy while providing a simplified imaging workflow. Methods: This retrospective study used abdominal CT exams collected under a dedicated Institutional Review Board (IRB). Input data consisted of CT topograms and images with high heterogeneity in terms of anatomy, sex, cancer pathologies, and imaging artifacts acquired with four different CT scanner models. Our method consisted of two sequential steps: (I) automatic locator scan positioning on topograms, and (II) automatic region-of-interest (ROI) positioning within the aorta on locator scans. The task of locator scan positioning is formulated as a regression problem, where the limited amount of annotated data is circumvented using transfer learning. The task of ROI positioning is formulated as a segmentation problem. Results: Our locator scan positioning network offered improved positional consistency compared to a high degree of variance in manual slice positionings, verifying inter-operator variance as a significant source of error. When trained using expert-user ground-truth labels, the locator scan positioning network achieved a sub-centimeter error (9.76±6.78 mm) on a test dataset. The ROI segmentation network achieved a sub-millimeter absolute error (0.99±0.66 mm) on a test dataset. Conclusions: Locator scan positioning networks offer improved positional consistency compared to manual slice positionings and verified inter-operator variance as an important source of error. By significantly reducing operator-related decisions, this method opens opportunities to standardize and simplify the workflow of bolus tracking procedures for contrast-enhanced CT.

Convolutional neural network-based single-shot speckle tracking for x-ray phase-contrast imaging.

X-ray phase-contrast imaging offers enhanced sensitivity for weakly-attenuating materials, such as breast and brain tissue, but has yet to be widely implemented clinically due to high coherence requirements and expensive x-ray optics. Speckle-based phase contrast imaging has been proposed as an affordable and simple alternative; however, obtaining high-quality phase-contrast images requires accurate tracking of sample-induced speckle pattern modulations. This study introduced a convolutional neural network to accurately retrieve sub-pixel displacement fields from pairs of reference (i.e., without sample) and sample images for speckle tracking. Speckle patterns were generated utilizing an in-house wave-optical simulation tool. These images were then randomly deformed and attenuated to generate training and testing datasets. The performance of the model was evaluated and compared against conventional speckle tracking algorithms: zero-normalized cross-correlation and unified modulated pattern analysis. We demonstrate improved accuracy (1.7 times better than conventional speckle tracking), bias (2.6 times), and spatial resolution (2.3 times), as well as noise robustness, window size independence, and computational efficiency. In addition, the model was validated with a simulated geometric phantom. Thus, in this study, we propose a novel convolutional-neural-network-based speckle-tracking method with enhanced performance and robustness that offers improved alternative tracking while further expanding the potential applications of speckle-based phase contrast imaging.

Non-invasive mass and temperature quantifications with spectral CT.

Spectral CT has been increasingly implemented clinically for its better characterization and quantification of materials through its multi-energy results. It also facilitates calculation of physical density, allowing for non-invasive mass measurements and temperature evaluations by manipulating the definition of physical density and thermal volumetric expansion, respectively. To develop spectral physical density quantifications, original and parametrized Alvarez-Macovski model and electron density-physical density model were validated with a phantom. The best physical density model was then implemented on clinical spectral CT scans of ex vivo bovine muscle to determine the accuracy and effect of acquisition parameters on mass measurements. In addition, the relationship between physical density and changes in temperature was evaluated by scanning and subjecting the tissue to a range of temperatures. The parametrized Alvarez-Macovski model performed best in both model development and validation with errors within ± 0.02 g/mL. No effect from acquisition parameters was observed in mass measurements, which demonstrated accuracy with a maximum percent error of 0.34%. Furthermore, physical density was strongly correlated (R of 0.9781) to temperature changes through thermal volumetric expansion. Accurate and precise spectral physical density quantifications enable non-invasive mass measurements for pathological detection and temperature evaluation for thermal therapy monitoring in interventional oncology.

Three-dimensional printing of patient-specific computed tomography lung phantoms: a reader study.

In modern clinical decision-support algorithms, heterogeneity in image characteristics due to variations in imaging systems and protocols hinders the development of reproducible quantitative measures including for feature extraction pipelines. With the help of a reader study, we investigate the ability to provide consistent ground-truth targets by using patient-specific 3D-printed lung phantoms. PixelPrint was developed for 3D-printing lifelike computed tomography (CT) lung phantoms by directly translating clinical images into printer instructions that control density on a voxel-by-voxel basis. Data sets of three COVID-19 patients served as input for 3D-printing lung phantoms. Five radiologists rated patient and phantom images for imaging characteristics and diagnostic confidence in a blinded reader study. Effect sizes of evaluating phantom as opposed to patient images were assessed using linear mixed models. Finally, PixelPrint's production reproducibility was evaluated. Images of patients and phantoms had little variation in the estimated mean (0.03-0.29, using a 1-5 scale). When comparing phantom images to patient images, effect size analysis revealed that the difference was within one-third of the inter- and intrareader variabilities. High correspondence between the four phantoms created using the same patient images was demonstrated by PixelPrint's production repeatability tests, with greater similarity scores between high-dose acquisitions of the phantoms than between clinical-dose acquisitions of a single phantom. We demonstrated PixelPrint's ability to produce lifelike CT lung phantoms reliably. These phantoms have the potential to provide ground-truth targets for validating the generalizability of inference-based decision-support algorithms between different health centers and imaging protocols and for optimizing examination protocols with realistic patient-based phantoms. Classification: CT lung phantoms, reader study.

Phantom-based quantification of the spectral accuracy in dual-layer spectral CT for pediatric imaging at 100 kVp.

Background: To determine the spectral accuracy in detector-based dual-energy CT (DECT) at 100 kVp and wide (8 cm) collimation width for dose levels and object sizes relevant to pediatric imaging. Methods: A spectral CT phantom containing tissue-equivalent materials and iodine inserts of varying concentrations was scanned on the latest generation detector-based DECT system. Two 3D-printed extension rings were used to mimic varying pediatric patient sizes. Scans were performed at 100 and 120 kVp, 4 and 8 cm collimation widths, and progressively reduced radiation dose levels, down to 0.9 mGy CTDIvol. Virtual mono-energetic, iodine density, effective atomic number, and electron density results were quantified and compared to their expected values for all acquisition settings and phantom sizes. Results: DECT scans at 100 kVp provided highly accurate spectral results; however, a size dependence was observed for iodine quantification. For the medium phantom configuration (15 cm diameter), measurement errors in iodine density, effective atomic number, and electron density (ED) were below 0.3 mg/mL, 0.2 and 1.8 %EDwater, respectively. The average accuracy was slightly different from scans at 120 kVp; however, not statistically significant for all configurations. Collimation width had no substantial impact. Spectral results were accurate and reliable for radiation exposures down to 0.9 mGy CTDIvol. Conclusions: Detector-based DECT at 100 kVp can provide on-demand or retrospective spectral information with high accuracy even at extremely low doses, thereby making it an attractive solution for pediatric imaging.

Radiomic phenotyping of the lung parenchyma in a lung cancer screening cohort.

High-throughput extraction of radiomic features from low-dose CT scans can characterize the heterogeneity of the lung parenchyma and potentially aid in identifying subpopulations that may have higher risk of lung diseases, such as COPD, and lung cancer due to inflammation or obstruction of the airways. We aim to determine the feasibility of a lung radiomics phenotyping approach in a lung cancer screening cohort, while quantifying the effect of different CT reconstruction algorithms on phenotype robustness. We identified low-dose CT scans (n = 308) acquired with Siemens Healthineers scanners from patients who completed low-dose CT within our lung cancer screening program between 2015 and 2018 and had two different sets of image reconstructions kernel available (i.e., medium (I30f.), sharp (I50f.)) for the same acquisition. Following segmentation of the lung field, a total of 26 radiomic features were extracted from the entire 3D lung-field using a previously validated fully-automated lattice-based software pipeline, adapted for low-dose CT scans. The lattice in-house software was used to extract features including gray-level histogram, co-occurrence, and run-length descriptors. The lattice approach uses non-overlapping windows for traversing along pixels of images and calculates different features. Each feature was averaged for each scan within a range of lattice window sizes (W) of 4, 8 and 20 mm. The extracted imaging features from both datasets were harmonized to correct for differences in image acquisition parameters. Subsequently, unsupervised hierarchical clustering was applied on the extracted features to identify distinct phenotypic patterns of the lung parenchyma, where consensus clustering was used to identify the optimal number of clusters (K = 2). Differences between phenotypes for demographic and clinical covariates including sex, age, BMI, pack-years of smoking, Lung-RADS and cancer diagnosis were assessed for each phenotype cluster, and then compared across clusters for the two different CT reconstruction algorithms using the cluster entanglement metric, where a lower entanglement coefficient corresponds to good cluster alignment. Furthermore, an independent set of low-dose CT scans (n = 88) from patients with available pulmonary function data on lung obstruction were analyzed using the identified optimal clusters to assess associations to lung obstruction and validate the lung phenotyping paradigm. Heatmaps generated by radiomic features identified two distinct lung parenchymal phenotype patterns across different feature extraction window sizes, for both reconstruction algorithms (P < 0.05 with K = 2). Associations of radiomic-based clusters with clinical covariates showed significant differences for BMI and pack-years of smoking (P < 0.05) for both reconstruction kernels. Radiomic phenotype patterns were more similar across the two reconstructed kernels, when smaller window sizes (W = 4 and 8 mm) were used for radiomic feature extraction, as deemed by their entanglement coefficient. Validation of clustering approaches using cluster mapping for the independent sample with lung obstruction also showed two statistically significant phenotypes (P < 0.05) with significant difference for BMI and smoking pack-years. Radiomic analysis can be used to characterize lung parenchymal phenotypes from low-dose CT scans, which appear reproducible for different reconstruction kernels. Further work should seek to evaluate the effect of additional CT acquisition parameters and validate these phenotypes in characterizing lung cancer screening populations, to potentially better stratify disease patterns and cancer risk.