Validation of a multi-parameter algorithm for personalized contrast injection protocol in liver CT.

BACKGROUND: In liver computed tomography (CT), tailoring the contrast injection to the patient's specific characteristics is relevant for optimal imaging and patient safety. We evaluated a novel algorithm engineered for personalized contrast injection to achieve reproducible liver enhancement centered on 50 HU. METHODS: From September 2020 to August 31, 2022, CT data from consecutive adult patients were prospectively collected at our multicenter premises. Inclusion criteria consisted of an abdominal CT referral for cancer staging or follow-up. For all examinations, a web interface incorporating data from the radiology information system (patient details and examination information) and radiographer-inputted data (patient fat-free mass, imaging center, kVp, contrast agent details, and imaging phase) were used. Calculated contrast volume and injection rate were manually entered into the CT console controlling the injector. Iopamidol 370 mgI/mL or Iohexol 350 mgI/mL were used, and kVp varied (80, 100, or 120) based on patient habitus. RESULTS: We enrolled 384 patients (mean age 61.2 years, range 21.1-94.5). The amount of administered iodine dose (gI) was not significantly different across contrast agents (p = 0.700), while a significant increase in iodine dose was observed with increasing kVp (p < 0.001) and in males versus females (p < 0.001), as expected. Despite the differences in administered iodine load, image quality was reproducible across patients with 72.1% of the examinations falling within the desirable range of 40-60 HU. CONCLUSION: This study validated a novel algorithm for personalized contrast injection in adult abdominal CT, achieving consistent liver enhancement centered at 50 HU. RELEVANCE STATEMENT: In healthcare's ongoing shift towards personalized medicine, the algorithm offers excellent potential to improve diagnostic accuracy and patient management, particularly for the detection and follow-up of liver malignancies. KEY POINTS: The algorithm achieves reproducible liver enhancement, promising improved diagnostic accuracy and patient management in diverse clinical settings. The real-world study demonstrates this algorithm's adaptability to different variables ensuring high-quality liver imaging. A personalized algorithm optimizes liver CT, improving the visibility, conspicuity, and follow-up of liver lesions.

A high-resolution large-area detector for quality assurance in radiotherapy.

Hadron therapy is an advanced radiation modality for treating cancer, which currently uses protons and carbon ions. Hadrons allow for a highly conformal dose distribution to the tumour, minimising the detrimental side-effects due to radiation received by healthy tissues. Treatment with hadrons requires sub-millimetre spatial resolution and high dosimetric accuracy. This paper discusses the design, fabrication and performance tests of a detector based on Gas Electron Multipliers (GEM) coupled to a matrix of thin-film transistors (TFT), with an active area of 60 × 80 mm2 and 200 ppi resolution. The experimental results show that this novel detector is able to detect low-energy (40 kVp X-rays), high-energy (6 MeV) photons used in conventional radiation therapy and protons and carbon ions of clinical energies used in hadron therapy. The GEM-TFT is a compact, fully scalable, radiation-hard detector that measures secondary electrons produced by the GEMs with sub-millimetre spatial resolution and a linear response for proton currents from 18 pA to 0.7 nA. Correcting known detector defects may aid in future studies on dose uniformity, LET dependence, and different gas mixture evaluation, improving the accuracy of QA in radiotherapy.