PT - JOURNAL ARTICLE AU - Ernest Garcia AU - Russell Folks AU - Samuel Pak AU - Andrew Taylor TI - Automatic definition of renal regions-of-interests (ROIs) from MAG3 renograms in patients with suspected renal obstruction DP - 2008 May 01 TA - Journal of Nuclear Medicine PG - 386P--386P VI - 49 IP - supplement 1 4099 - http://jnm.snmjournals.org/content/49/supplement_1/386P.3.short 4100 - http://jnm.snmjournals.org/content/49/supplement_1/386P.3.full SO - J Nucl Med2008 May 01; 49 AB - 1629 Objectives: To evaluate an algorithm (AUTOROI) to automatically detect kidney contours and generate renal ROIs. Methods: The 2-3 min frames post MAG3 injection were summed and smoothed. Count profiles were summed vertically and horizontally; each profile was searched using threshold and first derivative tests to define boxes surrounding each kidney. Interpolative background subtraction, a Sobel operator and unsharp masking were applied. Resulting image histograms were equalized to better define poorly functioning kidneys. AUTOROI searched radially from the count-based center of mass every 9 degrees using a threshold + first derivative test to define each kidney’s ROI coordinates. AUTOROI was validated using MAG3 studies from 80 patients referred for suspected obstruction (79 L kidneys, 78 R kidneys). Renal ROIs were manually defined by a nuclear medicine technologist with 20+ years experience (gold standard) and an ABNM certified physician. AUTOROI and physician ROIs were automatically compared to the gold standard with error defined as the sum of the Euclidian difference between the renal border defined by the gold standard and that of AUTOROI and the physician, respectively, for each of the 40 angular samples. Results: AUTOROI detected the renal borders in 79/80 patients. The mean error of AUTOROI was 7.33±3.36 mm and 8.08±3.31 mm for the L and R kidneys, respectively, and was significantly higher than the physician’s error, 6.92±2.01 mm (L) and 6.55±2.0 mm (R), p<.001. Nevertheless, AUTOROI agreed with the gold standard more closely than the physician in 40/79 (51%) L kidneys and 29/77 (38%) R kidneys. Conclusions: AUTOROI provides an objective and promising approach to automated renal ROI detection. Research Support: National Library of Medicine, R01-LM007595.