Accuracy of iodine quantification in dual energy CT: A phantom study across 3 different CT systems.

INTRODUCTION: No study has rigorously compared the performances of iodine quantification on recent CT systems employing different emission-based technologies, depending on the manufacturers and models. METHODS: A specific bespoke phantom was used for this study, with 12 known concentrations of iodinated contrast agent: 0.4, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0, 10.0, 15.0, 20.0, 30.0 and 50.0 mg/mL. Three different dual-energy scanners were tested: one system using dual-source acquisition (CT#1) and two systems using Fast kilovolt-peak switching technology ± artificial intelligence (AI) reconstruction methods (CT#2 and #3) from two different manufacturers. For each system, helical scans were performed following recommended clinical protocols. Four acquisitions were performed per iodine concentration (mg/mL), and measurements were made on iodine-maps using ROIs. Mean measured values were compared to the known concentrations, and the absolute quantification error (AQE) and the relative percentage error (RPE) were used to compare the performances of each CT. RESULTS: The accuracy of the obtained measurements varied depending on the studied model but not on the acquisition mode (dual-source vs kVp switch ± AI). The quantification was more precise at high concentrations. RPE values were below 10 % with CT#2 (kVp switch) and below 25 % with CT#1 (dual-source), but were significantly higher with CT#3 (kVp switch + AI), exceeding 50 % at low concentrations (<3 mg/mL). CONCLUSIONS: With the help of a phantom, we identified variability in the results accuracy depending on the CT model, with sometimes significant deviation. Considering the performances of the different DECT technologies in iodine mapping, dual-source (CT#1) and kVp switch (CT#2) technologies appear more accurate than kVp switch technology combined with deep-learning-based reconstruction (CT#3) especially at low concentrations (<3 mg/mL). IMPLICATIONS FOR PRACTICE: As the primary and daily user of medical imaging devices, the radiographer role is to be attentive to the performance of imaging systems, particularly when performing quantitative acquisitions like iodine-quantification. In CT quantitative imaging (iodine map), it's essential for radiographers to consider their CT systems as measuring tools, and to be aware of their accuracies and limits.

Identification and measurement of cervical spinal cord atrophy in neuromyelitis optica spectrum disorders (NMOSD) and correlation with clinical characteristics and cervical spinal cord MRI data.

INTRODUCTION: The spinal cord is one of the two main targets of neuromyelitis optica (NMO). The aim of this study was to highlight cervical spinal cord atrophy in NMO patients as compared to controls and to assess correlations between atrophy and clinical characteristics and cervical spinal cord MRI data. METHODS: This prospective study investigated 15 patients with a diagnosis of NMOSD and 15 healthy controls. The whole cervical spinal cord was explored by MRI. The cross-sectional area (CSA) was estimate at every level of cup. This measurement was then averaged on the whole cervical spinal cord, providing a single measurement for every subject, denoted as mean CSA. RESULTS: Mean CSA was 68.5 mm2 in the population of NMO patients and 72.8 mm2 in the population of healthy subjects. NMO patients had significantly smaller cervical spinal cord area than healthy controls (T test=0.009). Cervical spinal cord atrophy was associated with clinical signs of medullary involvement (T test=0.0006). There was a tendency toward a relation between cervical spinal cord atrophy and the Expanded Disability Status Scale (EDSS) (T test=0.07). This correlation seems statistically significant (T test<0.05) at the level of the upper cervical spinal cord (C2-C3) CONCLUSION: This study provides the first evidence of cervical spinal cord atrophy in NMOSD by studying the entire cervical spinal cord. Upper cervical spinal cord atrophy was substantially correlated to clinical disability and seems more involved in the development of clinical disability in NMOSD patients in comparison to the lower cervical spinal cord.

Five simultaneous artificial intelligence data challenges on ultrasound, CT, and MRI.

PURPOSE: The goal of this data challenge was to create a structured dynamic with the following objectives: (1) teach radiologists the new rules of General Data Protection Regulation (GDPR), while building a large multicentric prospective database of ultrasound, computed tomography (CT) and MRI patient images; (2) build a network including radiologists, researchers, start-ups, large companies, and students from engineering schools, and; (3) provide all French stakeholders working together during 5 data challenges with a secured framework, offering a realistic picture of the benefits and concerns in October 2018. MATERIALS AND METHODS: Relevant clinical questions were chosen by the Société Francaise de Radiologie. The challenge was designed to respect all French ethical and data protection constraints. Multidisciplinary teams with at least one radiologist, one engineering student, and a company and/or research lab were gathered using different networks, and clinical databases were created accordingly. RESULTS: Five challenges were launched: detection of meniscal tears on MRI, segmentation of renal cortex on CT, detection and characterization of liver lesions on ultrasound, detection of breast lesions on MRI, and characterization of thyroid cartilage lesions on CT. A total of 5,170 images within 4 months were provided for the challenge by 46 radiology services. Twenty-six multidisciplinary teams with 181 contestants worked for one month on the challenges. Three challenges, meniscal tears, renal cortex, and liver lesions, resulted in an accuracy>90%. The fourth challenge (breast) reached 82% and the lastone (thyroid) 70%. CONCLUSION: Theses five challenges were able to gather a large community of radiologists, engineers, researchers, and companies in a very short period of time. The accurate results of three of the five modalities suggest that artificial intelligence is a promising tool in these radiology modalities.