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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 utilizing the Alvarez-Macovski model without approximations. These spectral physical density quantifications allow for non-invasive mass measurements and temperature evaluations by manipulating the definition of physical density and thermal volumetric expansion, respectively. To develop the model, original and parametrized versions of the 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. A linear fit utilizing the thermal volumetric expansion was performed to assess the correlation. The parametrized Alvarez-Macovski model performed best in both model development and validation with errors within ±0.02 g/mL. As observed with muscle, physical density was not significantly affected by dose and acquisition mode but was slightly affected by collimation. These effects were also reflected in mass measurements, which demonstrated accuracy with a maximum percent error of 0.34%, further validating the physical density model. 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.

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.

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.

ObjectiveEvaluation of quantification capabilities at ultra-low radiation dose levels of a first-generation dual-source Photon-Counting Computed Tomography (PCCT) compared to a dual-source dual-energy CT (DECT) scanner.MethodsA multi-energy CT phantom was imaged with and without extension ring on both scanners over a range of radiation dose levels (CTDIvol 0.4 - 15.0 mGy). Scans were performed in different modes of acquisition for PCCT with 120 kVp and DECT with 70/Sn150 kVp and 100/Sn150 kVp. Various tissue inserts were used to characterize the precision and repeatability of Hounsfield Units (HUs) on virtual mono-energetic images between 40 and 190 keV. Image noise was additionally investigated at ultra-low radiation dose to illustrate PCCT’s ability to remove electronic background noise.ResultsOur results demonstrate high precision of HU measurements for a wide range of inserts and radiation exposure levels with PCCT. We report high performance for both scanners across a wide range of radiation exposure levels with PCCT outperforming at low exposures compared to DECT. PCCT scans at lowest radiation exposures illustrate significant reduction in electronic background noise, with a mean percent reduction of 74% (p-value ∼10−8) compared to the 70/Sn150 kVp and 60% (p-value ∼10−6) compared to the 100/Sn150 kVp.ConclusionsThis paper reports first experiences with a clinical dual-source PCCT scanner with Quantum technology. PCCT provides reliable HUs without disruption from electronic background noise for a wide range of dose values. Diagnostic benefits are not only for quantification at ultra-low-dose but also for imaging of obese patients.Key PointsPCCT scanners with Quantum technology provide precise and reliable quantitative Hounsfield Units at ultra-low-dose levels.Influence of electronic background noise can be removed at ultra-low dose acquisitions with PCCT.Both spectral platforms have high performance along a wide range of radiation exposure levels with PCCT outperforming at low radiation exposures.