Recently a new image reconstruction algorithm based on Deep-learning has been developed (Precise Image, Philips Healthcare). Initial studies on phantoms have shown that this algorithm improves image quality and reduces patient dose compared to the iDose4 iterative reconstruction algorithm. Feasibility studies have validated the image quality for low-dose levels (LD). However, this algorithm has a strong potential to maintain sufficient image quality suitable for diagnosis with ultra-low dose (ULD) chest and abdomen-pelvis scans.~The hypothesis is that the images obtained with the Precise Image algorithm for ULD acquisitions are of sufficient and suitable quality for the diagnosis of certain lung, abdominal-pelvic and bone lesions.~The purpose of this study is to evaluate the concordance of the global quality of thoraco-abdominopelvic images of a ULD scan acquisition compared to a standard dose CT acquisition and measure the global agreement of the global quality of the images with a 4-point Likert scale.~The ULD acquisition will allow a significant reduction in the X-ray dose delivered to patients compared to a standard dose conventional scanner. This reduction is estimated at 70%.
The new image reconstruction algorithm (Precise Image, Philips Healthcare) has a strong potential to maintain sufficient image quality suitable for diagnosis with ultra-low dose (ULD) chest and abdomen-pelvis scans.~The hypothesis is that the images obtained with the Precise Image algorithm for ULD acquisitions are of sufficient and suitable quality for the diagnosis of certain lung, abdominal-pelvic and bone lesions.