Abstract:
Image quality and lesion detection abilities are primary to accurate diagnosis in medical imaging; hence this study was aimed at examining the image quality and lesion detection abilities in Full-Field digital mammography and Computed Radiography digital mammography using the American College of Radiology Mammography Accreditation Phantom (ACR-MAP). Pre-exposure and exposure tests were conducted to establish the effective performance of the mammography systems used. DICOM images were obtained of the ACR-MAP at varying values mAs and kVp. Qualitative image quality assessment was made using the internationally recommended protocol for detection scoring. Quantitative image quality was also estimated using the ImageJ software and the Albert Rose Model to analyze image quality with reference to the Human Health Series numbers 2 and 17 of the International Atomic Energy Agency (HHS - IAEA). Results of the pre-exposure and exposure texts showed optimal and satisfactory performance of four of the systems. The half value layer result of the system D within the CRDM systems was below the recommended limit; hence the poor quality and detection exhibited by the machine of that facility. The obtained signal-to-noise ratio (SNR) and spatial resolution results indicated standard quality images were achievable at the 20 mm and 45 mm thicknesses within both systems but poor-quality images at 70 mm. Signal-to-noise ratio and spatial resolution decreased with increasing PMMA thickness. SNR was 16 % more in FFDM than that of CRDM, whiles the spatial resolution was 0.5 lp/mm, 1.0 lp/mm and 0.5 lp/mm more in in the FFDM systems compared to the CRDM systems within the respective PMMA thicknesses, indicating adequate quality within FFDM. Both FFDM and CRDM systems produced quality images proportional to increasing the detectability as the technique factors (kVp and mAs) increased with the ACR MAP, with the FFDM system’s average percent visibility at 89.05 % and that of CRDM at 75.00 %. The FFDM proved superiority in image quality and lesion detection over the CRDM.
