Optimizing imaging protocols in small animal micro-CT for dose minimization is important, especially in longitudinal studies. To find the optimal number of projection angles over a full 360 rotation given a fixed dose as constraint, the tradeoff to be found is between elevated image noise for projections approaching the quantum limit and streak artifacts due to sparse angular sampling. Scan protocols are therefore simulated by forward projections of a quasi-noise-free mouse volume using different numbers ofview angles and addition of an appropriate amount of noise to preserve a constant overall dose. Energy integrating (EI) and photon counting (PC) detectors are considered and compared. Evaluation is based on the performance of imaging tasks: The left ventricular volume is determined using the segmentation algorithm by Otsu and compared to a ground truth. A model observer with nonprewhitening matched filter template is used for the detection of a small lesion and its performance is evaluated to confirm the findings. Results for EI detectors indicate an optimal number of 180 view angles over 360. The absence of electronic noise in the case of a PC detector leads to increasing image quality for an increasing number of view angles. The findings will allow for designing and improving future in-vivo imaging protocols for both detector types.
[1]
L. Feldkamp,et al.
Practical cone-beam algorithm
,
1984
.
[2]
Kyle J Myers,et al.
Evaluating imaging and computer-aided detection and diagnosis devices at the FDA.
,
2012,
Academic radiology.
[3]
Yoshito Otake,et al.
Low-dose preview for patient-specific, task-specific technique selection in cone-beam CT.
,
2014,
Medical physics.
[4]
N. Otsu.
A threshold selection method from gray level histograms
,
1979
.
[5]
Marc Kachelrieß,et al.
Assessment of dedicated low-dose cardiac micro-CT reconstruction algorithms using the left ventricular volume of small rodents as a performance measure.
,
2014,
Medical physics.
[6]
Marcus Brehm,et al.
Cardiorespiratory motion-compensated micro-CT image reconstruction using an artifact model-based motion estimation.
,
2015,
Medical physics.