Patient-based CT phantoms, with realistic image texture and densities, are essential tools for assessing and verifying CT performance in clinical practice. This study extends our previously presented 3D printing solution (PixelPrint) to patient-based phantoms with soft tissue and bone structures. To expand the Hounsfield Unit (HUs) range, we utilize a stone-based filament. Applying PixelPrint, we converted patient DICOM images directly into FDM printer instructions (G-code). Density was modeled as the ratio of filament to voxel volume to emulate attenuation profiles for each voxel, with the filament ratio controlled through continuous modification of the printing speed. Two different phantoms were designed to demonstrate the high reproducibility of our approach with micro-CT acquisitions, and to determine the mapping between filament line widths and HU values on a clinical CT system. Moreover, a third phantom based on a clinical cervical spine scan was manufactured and scanned with a clinical spectral CT scanner. CT image of the patient-based phantom closely resembles the original CT image both in texture and contrast levels. Measured differences between patient and phantom are around 10 HU for bone marrow voxels and around 150 HU for cortical bone. In addition, stone-based filament can accurately represent boney tissue structures across the different x-ray energies, as measured by spectral CT. This study demonstrates the feasibility of our 3D-printed patient-based phantoms to be extended to soft-tissue and bone structure while maintaining accurate organ geometry, image texture, and attenuation profiles for spectral CT.

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.

Dual-source photon-counting computed tomography (PCCT) enables a novel ultra-high-resolution (UHR) scanning mode that can provide UHR conventional images (0.2 mm) as well as spectral results (0.4 mm). To evaluate the spatial resolution and quantitative capabilities of the UHR mode, with a focus on thoracic imaging, a PixelPrint lung phantom mimicking interstitial lung disease with honeycombing and iodine rods of different diameters and concentrations directly attached to the phantom were scanned at doses from 1.0 to 7.5 mGy. Virtual monoenergetic images at 50 keV, virtual non-contrast, and iodine density maps at 0.4 and 1 mm slice thickness were reconstructed as well as conventional images at 0.2, 0.4, and 1 mm slice thickness, all with standard lung and quantitative reconstruction kernels (matrix size 512x512). Iodine quantification was performed for the attached rods, and clinically relevant features in the lung phantom were utilized to evaluate spatial resolution. Overall, iodine quantification was stable across radiation dose, reconstruction kernels, and slice thickness with errors of 0.25, 0.20, and 0.40 mg/mL for 1, 2, and 5 mg/mL iodine, respectively. Even the smallest iodine core rod was detected in the extended CT phantom for a higher dose. For the diseased lung region, images at 0.2 mm slice thickness appeared sharper and depicted smaller structures better, even with increased noise in comparison to thicker slices. In conclusion, UHR mode demonstrated high spatial resolution with detection of small features and accurate iodine quantification, which may provide diagnostic advantage to thoracic imaging with more precise and accurate information.

Introduction: In patients with cryptogenic stroke (CS), complex aortic plaque may be a potential underlying etiology. We performed a systematic review to determine the prevalence of complex aortic plaque in CS patients. Methods: A systematic review and meta-analysis were performed according to PRISMA guidelines (PROSPERO: CRD42022300865). PubMed and EMBASE databases were searched from Jan 1980 to Nov 2021 for studies assessing aortic (ascending, arch, descending) plaque by transesophageal echocardiogram (TEE), CT/CTA, or MRI in at least 10 CS patients. Prevalence rates were pooled using a random-effects model. I 2 statistics assessed heterogeneity. An Egger’s test assessed publication bias. Results: From 2712 articles, 31 met inclusion criteria. Ascending, arch, and descending aorta were assessed in 65%, 100%, 55% of studies, respectively. Studies investigated aortic plaque by TEE (84%), CT/CTA (19%) and MRI (16%). The prevalence of complex aortic plaque in 4666 CS patients was heterogeneous across studies and yielded a summary prevalence of 30% (95% CI 23-38%, I 2 = 96%; Figure 1) contrasting with 11% (95% CI 5-20%, I 2 =83%) in 677 patients without stroke. Prevalence rates in women and men were 26% (95% CI 14%-43%, I 2 = 94%) and 35% (95% CI 21-52%, I 2 = 97%), respectively. To investigate geographic differences, 14 studies from Europe were pooled (32%, 95% CI 23-43%, I 2 =93%), 3 from the Middle East (34%, 95% CI 11-67%, I 2 =94%) and 3 from the US (28%, 95% CI 13-51%, I 2 =90%). No publication bias was detected (p=0.66). Sources of heterogeneity included patient selection, imaging technology (e.g, transducer frequency) and plaque measurement criteria. Conclusions: Studies suggest a prevalence rate of complex aortic plaque in approximately 30% of CS patients. However, significant heterogeneity in the results indicate a need for less variability in CS patient selection and more reproducible imaging methods/criteria for detecting complex aortic plaque.

Cardiovascular disease diagnosis relies heavily on diagnostic imaging. Advancement in computed tomography (CT) technology has particularly improved diagnosis in patients with coronary artery disease. In particular, the improved spatial resolution and iodine quantification capabilities of photon-counting CT (PCCT) have the potential to further improve the diagnostic workflow. Since iodine quantification has become a critical aspect of clinical diagnosis, several studies have been conducted to evaluate its effectiveness and the parameters that may affect it. An additional relationship, the effect of spatial resolution and vessel size on iodine quantification, was examined with a designed phantom. A phantom consisted of six different tubes of changing diameters (2 to 12 mm), along with a cone and an hourglass-shaped tube with diameters from 3 to 8 mm. It was scanned on a PCCT after being filled with an iodine solution. Iodine density maps, VNC, and VMI 70keV were then reconstructed with different fields of view (250 mm, 350 mm, 450 mm). Regions of interest were placed on spectral results along the length of the hourglass. Spectral results were highly accurate for vessels larger than 4 mm in diameter and regions of interest larger than 3 mm. The bias in iodine quantification increases with smaller diameters. Conversely, VNC increased, illustrating a directly proportional relationship between VNC and iodine density. The proposed phantom design allows for future studies that further investigate the relationship between spatial resolution and iodine quantification, especially in clinical workflow for optimizing protocols, implementing new CT technologies, and harmonizing protocols between different CT platforms.