Fourier Analysis of Noise Characteristics in Cone-Beam Microtomography Laboratory Scanners

Modelling the skeletal muscle injury recovery using in vivo contrast-enhanced micro-CT: a proof-of-concept study in a rat model

Background

Skeletal muscle injury characterisation during healing supports trauma prognosis. Given the potential interest of computed tomography (CT) in muscle diseases and lack of in vivo CT methodology to image skeletal muscle wound healing, we tracked skeletal muscle injury recovery using in vivo micro-CT in a rat model to obtain a predictive model.

Methods

Skeletal muscle injury was performed in 23 rats. Twenty animals were sorted into five groups to image lesion recovery at 2, 4, 7, 10, or 14 days after injury using contrast-enhanced micro-CT. Injury volumes were quantified using a semiautomatic image processing, and these values were used to build a prediction model. The remaining 3 rats were imaged at all monitoring time points as validation. Predictions were compared with Bland-Altman analysis.

Results

Optimal contrast agent dose was found to be 20 mL/kg injected at 400 μL/min. Injury volumes showed a decreasing tendency from day 0 (32.3 ± 12.0mm³, mean ± standard deviation) to day 2, 4, 7, 10, and 14 after injury (19.6 ± 12.6, 11.0 ± 6.7, 8.2 ± 7.7, 5.7 ± 3.9, and 4.5 ± 4.8 mm³, respectively). Groups with single monitoring time point did not yield significant differences with the validation group lesions. Further exponential model training with single follow-up data (R² = 0.968) to predict injury recovery in the validation cohort gave a predictions root mean squared error of 6.8 ± 5.4 mm³. Further prediction analysis yielded a bias of 2.327.

Conclusion

Contrast-enhanced CT allowed in vivo tracking of skeletal muscle injury recovery in rat.

Human and model observer performance for lesion detection in breast cone beam CT images with the FDK reconstruction

We investigate the detectability of breast cone beam computed tomography images using human and model observers and the variations of exponent, β, of the inverse power-law spectrum for various reconstruction filters and interpolation methods in the Feldkamp-Davis-Kress (FDK) reconstruction. Using computer simulation, a breast volume with a 50% volume glandular fraction and a 2mm diameter lesion are generated and projection data are acquired. In the FDK reconstruction, projection data are apodized using one of three reconstruction filters; Hanning, Shepp-Logan, or Ram-Lak, and back-projection is performed with and without Fourier interpolation. We conduct signal-known-exactly and background-known-statistically detection tasks. Detectability is evaluated by human observers and their performance is compared with anthropomorphic model observers (a non-prewhitening observer with eye filter (NPWE) and a channelized Hotelling observer with either Gabor channels or dense difference-of-Gaussian channels). Our results show that the NPWE observer with a peak frequency of 7cyc/degree attains the best correlation with human observers for the various reconstruction filters and interpolation methods. We also discover that breast images with smaller β do not yield higher detectability in the presence of quantum noise.