Variation in cartilage T2 and T2* mapping of the wrist: a comparison between 3- and 7-T MRI.

BACKGROUND: To analyze regional variations in T2 and T2* relaxation times in wrist joint cartilage and the triangular fibrocartilage complex (TFCC) at 3 and 7 T and to compare values between field strengths. METHODS: Twenty-five healthy controls and 25 patients with chronic wrist pain were examined at 3 and 7 T on the same day using T2- and T2*-weighted sequences. Six different regions of interest (ROIs) were evaluated for cartilage and 3 ROIs were evaluated at the TFCC based on manual segmentation. Paired t-tests were used to compare T2 and T2* values between field strengths and between different ROIs. Spearman's rank correlation was calculated to assess correlations between T2 and T2* time values at 3 and 7 T. RESULTS: T2 and T2* time values of the cartilage differed significantly between 3 and 7 T for all ROIs (p ≤ 0.045), with one exception: at the distal lunate, no significant differences in T2 values were observed between field strengths. T2* values differed significantly between 3 and 7 T for all ROIs of the TFCC (p ≤ 0.001). Spearman's rank correlation between 3 and 7 T ranged from 0.03 to 0.62 for T2 values and from 0.01 to 0.48 for T2* values. T2 and T2* values for cartilage varied across anatomic locations in healthy controls at both 3 and 7 T. CONCLUSION: Quantitative results of T2 and T2* mapping at the wrist differ between field strengths, with poor correlation between 3 and 7 T. Local variations in cartilage T2 and T2* values are observed in healthy individuals. RELEVANCE STATEMENT: T2 and T2* mapping are feasible for compositional imaging of the TFCC and the cartilage at the wrist at both 3 and 7 T, but the clinical interpretation remains challenging due to differences between field strengths and variations between anatomic locations. KEY POINTS: •Field strength and anatomic locations influence T2 and T2* values at the wrist. •T2 and T2* values have a poor correlation between 3 and 7 T. •Local reference values are needed for each anatomic location for reliable interpretation.

Personalized computed tomography - Automated estimation of height and weight of a simulated digital twin using a 3D camera and artificial intelligence. / Personalisierte Computertomografie ­ automatisierte Abschätzung von Größe und Gewicht durch Simulation eines digitalen Zwillings mit einer 3D-Kamera und künstlicher Intelligenz.

PURPOSE: The aim of this study was to develop an algorithm for automated estimation of patient height and weight during computed tomography (CT) and to evaluate its accuracy in everyday clinical practice. MATERIALS AND METHODS: Depth images of 200 patients were recorded with a 3D camera mounted above the patient table of a CT scanner. Reference values were obtained using a calibrated scale and a measuring tape to train a machine learning algorithm that fits a patient avatar into the recorded patient surface data. The resulting algorithm was prospectively used on 101 patients in clinical practice and the results were compared to the reference values and to estimates by the patient himself, the radiographer and the radiologist. The body mass index was calculated from the collected values for each patient using the WHO formula. A tolerance level of 5 kg was defined in order to evaluate the impact on weight-dependent contrast agent dosage in abdominal CT. RESULTS: Differences between values for height, weight and BMI were non-significant over all assessments (p > 0.83). The most accurate values for weight were obtained from the patient information (R²â€Š= 0.99) followed by the automated estimation via 3D camera (R²â€Š= 0.89). Estimates by medical staff were considerably less precise (radiologist: R²â€Š= 0.78, radiographer: R²â€Š= 0.77). A body-weight dependent dosage of contrast agent using the automated estimations matched the dosage using the reference measurements in 65 % of the cases. The dosage based on the medical staff estimates would have matched in 49 % of the cases. CONCLUSION: Automated estimation of height and weight using a digital twin model from 3D camera acquisitions provide a high precision for protocol design in computer tomography. KEY POINTS: · Machine learning can calculate patient-avatars from 3D camera acquisitions.. · Height and weight of the digital twins are comparable to real measurements of the patients.. · Estimations by medical staff are less precise.. · The values can be used for calculation of contrast agent dosage.. CITATION FORMAT: · Geissler F, Heiß R, Kopp M et al. Personalized computed tomography - Automated estimation of height and weight of a simulated digital twin using a 3D camera and artificial intelligence. Fortschr Röntgenstr 2021; 193: 437 - 445.