SU-F-207-08: CT Protocol Management Using Quantitative Clinical Image Quality Measurement

2015 ◽  
Vol 42 (6Part26) ◽  
pp. 3543-3543
Author(s):  
J Och ◽  
P Yorks ◽  
V Mehra
2017 ◽  
Vol 59 (6) ◽  
pp. 649-656 ◽  
Author(s):  
Teresa Monserrat ◽  
Elena Prieto ◽  
Benigno Barbés ◽  
Luis Pina ◽  
Arlette Elizalde ◽  
...  

Background In 2014, Siemens developed a new software-based scatter correction (Progressive Reconstruction Intelligently Minimizing Exposure [PRIME]), enabling grid-less digital mammography. Purpose To compare doses and image quality between PRIME (grid-less) and standard (with anti-scatter grid) modes. Material and Methods Contrast-to-noise ratio (CNR) was measured for various polymethylmethacrylate (PMMA) thicknesses and dose values provided by the mammograph were recorded. CDMAM phantom images were acquired for various PMMA thicknesses and inverse Image Quality Figure (IQFinv) was calculated. Values of incident entrance surface air kerma (ESAK) and average glandular dose (AGD) were obtained from the DICOM header for a total of 1088 pairs of clinical cases. Two experienced radiologists compared subjectively the image quality of a total of 149 pairs of clinical cases. Results CNR values were higher and doses were lower in PRIME mode for all thicknesses. IQFinv values in PRIME mode were lower for all thicknesses except for 40 mm of PMMA equivalent, in which IQFinv was slightly greater in PRIME mode. A mean reduction of 10% in ESAK and 12% in AGD in PRIME mode with respect to standard mode was obtained. The clinical image quality in PRIME and standard acquisitions resulted to be similar in most of the cases (84% for the first radiologist and 67% for the second one). Conclusion The use of PRIME software reduces, in average, the dose of radiation to the breast without affecting image quality. This reduction is greater for thinner and denser breasts.


2020 ◽  
Vol 61 (10) ◽  
pp. 1421-1430
Author(s):  
Felix W Feldhaus ◽  
Georg Böning ◽  
Johannes Kahn ◽  
Uli Fehrenbach ◽  
Martin Maurer ◽  
...  

Background In computed tomography (CT) scans, artifacts caused by metallic orthopedic implants still hamper the visualization of important, periprosthetic tissues. Smart MAR metal artifact reduction tool is a promising three-stage, projection-based, post-processing algorithm. Purpose To determine whether the Smart MAR tool improves subjective and objective image quality and diagnostic confidence in patients with orthopedic implants of the hip, spine, and shoulder. Material and Methods Seventy-two patients with orthopedic screws, hip/shoulder replacement, or spine spondylodesis were included. CT scans were performed on a single-source multislice CT scanner, raw data were post-processed using Smart MAR. Image quality was evaluated both quantitatively (ROI-based) and qualitatively (rater-based) and compared to iterative reconstructions (ASIR V). As comparative standard for artificial prosthetic breaks or loosening, follow-up examinations were used. Results Smart MAR reconstructions of the hip (n = 23), spine (n = 26), and shoulder (n = 23) showed a significantly reduced attenuation and noise of regions adjacent to metallic implants ( P<0.002). Subjective image quality ( P<0.005, shoulder P = 0.038/ P = 0.046) and overall diagnostic confidence were higher in Smart MAR (all regions P<0.002). Signal-to-noise ratio (SNR; P = 0.72/ P = 0.96) was not improved. Compared to standard ASIR V new, artificial metal extinctions (up to 50%) or periprosthetic hem lines (48%–73%) were introduced by Smart MAR. Conclusion Smart MAR improved image quality of the hip, spine, and shoulder CT scans resulting in higher diagnostic confidence in evaluation of periprosthetic soft tissues. As shown for spine implants, it should be used with caution and as a complementary tool for evaluation of periprosthetic loosening or integrity of metal implant, as in many cases it introduced new artifacts.


2014 ◽  
Vol 162 (4) ◽  
pp. 597-604 ◽  
Author(s):  
A. G. Davies ◽  
A. J. Gislason-Lee ◽  
A. R. Cowen ◽  
S. M. Kengyelics ◽  
M. Lupton ◽  
...  

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