Correlation between model observer and human observer performance in CT imaging when lesion location is uncertain. Issue 8 (10th July 2013)

• Record Type: Journal Article
• Authors: Leng, Shuai; Yu, Lifeng; Zhang, Yi; Carter, Rickey; Toledano, Alicia Y.; McCollough, Cynthia H.
• Is Part Of: Medical physics. Volume 40:Issue 8(2013)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-07-10
• Subjects: Computed tomography -- Dosimetry/exposure assessment; computerised tomography -- diseases -- dosimetry -- medical image processing -- phantoms; human observer -- model observer -- lesion detection and localization -- channelized Hotelling observer -- LROC; Computerised tomographs -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; Medical imaging -- Computed tomography -- Medical image reconstruction -- Signal generators -- Medical image quality -- Image analysis -- Biomedical modeling -- Medical image noise -- Decision analysis -- Dosimetry; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4812430
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : The purpose of this study was to investigate the correlation between model observer and human observer performance in CT imaging for the task of lesion detection and localization when the lesion location is uncertain. Methods: : Two cylindrical rods (3‐mm and 5‐mm diameters) were placed in a 35 × 26 cm torso‐shaped water phantom to simulate lesions with −15 HU contrast at 120 kV. The phantom was scanned 100 times on a 128‐slice CT scanner at each of four dose levels (CTDIvol = 5.7, 11.4, 17.1, and 22.8 mGy). Regions of interest (ROIs) around each lesion were extracted to generate images with signal‐present, with each ROI containing 128 × 128 pixels. Corresponding ROIs of signal‐absent images were generated from images without lesion mimicking rods. The location of the lesion (rod) in each ROI was randomly distributed by moving the ROIs around each lesion. Human observer studies were performed by having three trained observers identify the presence or absence of lesions, indicating the lesion location in each image and scoring confidence for the detection task on a 6‐point scale. The same image data were analyzed using a channelized Hotelling model observer (CHO) with Gabor channels. Internal noise was added to the decision variables for the model observer study. Area under the curve (AUC) of ROC and localization ROC (LROC) curves were calculated using a nonparametric approach. The Spearman's rank order correlation between the average performance of the human observers and the model observer performance was calculated for the AUC of both ROC and LROC curves for both the 3‐ and 5‐mm diameter lesions. Results: : In both ROC and LROC analyses, AUC values for the model observer agreed well with the average values across the three human observers. The Spearman's rank order correlation values for both ROC and LROC analyses for both the 3‐ and 5‐mm diameter lesions were all 1.0, indicating perfect rank ordering agreement of the figures of merit (AUC) between the average performance of the human observers and the model observer performance. Conclusions: : In CT imaging of different sizes of low‐contrast lesions (−15 HU), the performance of CHO with Gabor channels was highly correlated with human observer performance for the detection and localization tasks with uncertain lesion location in CT imaging at four clinically relevant dose levels. This suggests the ability of Gabor CHO model observers to meaningfully assess CT image quality for the purpose of optimizing scan protocols and radiation dose levels in detection and localization tasks for low‐contrast lesions.