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Objective. To determine whether novel multi-energy spectral photon-counting computed tomography (SPCCT) imaging can detect and differentiate between monosodium urate (MSU), calcium pyrophosphate (CPP), and hydroxyapatite (HA) crystal deposits ex vivo.Methods. A finger with a subcutaneous gouty tophus and a calcified knee meniscus excised at the time of surgery were obtained. The finger was imaged using plain x-ray, dual-energy CT (DECT), and multi-energy SPCCT. Plain x-ray and multi-energy SPCCT images of the meniscus were acquired. For validation purposes, samples of the crystals were obtained from the tophus and meniscus, and examined by polarized light microscopy and/or x-ray diffraction. As further validation, synthetic crystal suspensions of MSU, CPP, and HA were scanned using multi-energy SPCCT.Results. Plain x-ray of the gouty finger revealed bone erosions with overhanging edges. DECT and multi-energy SPCCT both showed MSU crystal deposits; SPCCT was able to show finer detail. Plain x-ray of the calcified meniscus showed chondrocalcinosis consistent with CPP, while SPCCT showed and differentiated CPP and HA.Conclusion. Multi-energy SPCCT can not only detect, differentiate, and quantify MSU crystal deposits in a gouty finger ex vivo, but also specifically detect, identify, and quantify CPP within an osteoarthritic meniscus, and distinguish them from HA crystal deposits. There is potential for multi-energy SPCCT to become useful in the diagnosis of crystal arthropathies.

In abdominal CT, switching from FBP to IR algorithms offers limited possibilities for achieving significant dose reductions while ensuring a constant objective LCD.

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.

ObjectiveWe investigated the variability in diagnostic information inherent in computed tomography (CT) images acquired at 68 different CT units, with the selected acquisition protocols aiming to answer the same clinical question.MethodsAn anthropomorphic abdominal phantom with two optional rings was scanned on 68 CT systems from 62 centres using the local clinical acquisition parameters of the portal venous phase for the detection of focal liver lesions. Low-contrast detectability (LCD) was assessed objectively with channelised Hotelling observer (CHO) using the receiver operating characteristic (ROC) paradigm. For each lesion size, the area under the ROC curve (AUC) was calculated and considered as a figure of merit. The volume computed tomography dose index (CTDIvol) was used to indicate radiation dose exposure.ResultsThe median CTDIvol used was 5.8 mGy, 10.5 mGy and 16.3 mGy for the small, medium and large phantoms, respectively. The median AUC obtained from clinical CT protocols was 0.96, 0.90 and 0.83 for the small, medium and large phantoms, respectively.ConclusionsOur study used a model observer to highlight the difference in image quality levels when dealing with the same clinical question. This difference was important and increased with growing phantom size, which generated large variations in patient exposure. In the end, a standardisation initiative may be launched to ensure comparable diagnostic information for well-defined clinical questions. The image quality requirements, related to the clinical question to be answered, should be the starting point of patient dose optimisation.Key Points• Model observers enable to assess image quality objectively based on clinical tasks.• Objective image quality assessment should always include several patient sizes.• Clinical diagnostic image quality should be the starting point for patient dose optimisation.• Dose optimisation by applying DRLs only is insufficient for ensuring clinical requirements.