Localization of liver lesions in abdominal CT imaging: I. Correlation of human observer performance between anatomical and uniform backgrounds

Abstract

The purpose of this study was to determine the correlation between human observer performance for localization of small low contrast lesions within uniform water background versus an anatomical liver background, under the conditions of varying dose, lesion size, and reconstruction algorithm. Liver lesions (5 mm, 7 mm, and 9 mm, contrast: −21 HU) were digitally inserted into CT projection data of ten normal patients in vessel-free liver regions. Noise was inserted into the projection data to create three image sets: full dose and simulated half and quarter doses. Images were reconstructed with a standard filtered back projection (FBP) and an iterative reconstruction (IR) algorithm. Lesion and noise insertion procedures were repeated with water phantom data. Two-dimensional regions of interest (66 lesion-present, 34 lesion-absent) were selected, randomized, and independently reviewed by three medical physicists to identify the most likely location of the lesion and provide a confidence score. Locations and confidence scores were assessed using the area under the localization receiver operating characteristic curve (AzLROC). We examined the correlation between human performance for the liver and uniform water backgrounds as dose, lesion size, and reconstruction algorithm varied. As lesion size or dose increased, reader localization performance improved. For full dose IR images, the AzLROC for 5, 7, and 9 mm lesions were 0.53, 0.91, and 0.97 (liver) and 0.51, 0.96, and 0.99 (uniform water), respectively. Similar trends were seen with other parameters. Performance values for liver and uniform backgrounds were highly correlated for both reconstruction algorithms, with a Spearman correlation of ρ = 0.97, and an average difference in AzLROC of 0.05 ± 0.04. For the task of localizing low contrast liver lesions, human observer performance was highly correlated between anatomical and uniform backgrounds, suggesting that lesion localization studies emulating a clinical test of liver lesion detection can be performed using a uniform background.

Localization of liver lesions in abdominal CT imaging: II. Mathematical model observer performance correlates with human observer performance for localization of liver lesions in abdominal CT imaging

“…For each of the 36 datasets, the average reader performances served as the reference standard to which our model observer performance was compared. More details of the human observer study are provide in Part I (Dilger et al 2019).…”

Low‐dose CT image and projection dataset

“…As additional examples of the value of these data, within our own research program and clinical practice, we have used these and other data to. determine optimal protocol settings in our large subspecialty clinical practice, 35–38 conduct multireader, multicase (MRMC) studies to discern the impact of different reconstruction algorithms, patient dose levels and other factors on radiologist diagnostic performance and confidence, 12–16,20,35–37,39,40 develop, and evaluate using MRMC studies, nonlocal means and deep learning‐based image denoising methods, 41–43 and develop model observers and deep learning methods from phantom or patient data to predict human observer performance of radiologists when interpreting patient data to allow rapid optimization of protocols for any scanner model, exam type, or patient characteristics 17–19 …”

“…To evaluate the impact of these effects on the ability of human observers to perform clinically relevant tasks, multireader, multicase observer performance studies and model observer performance studies have become essential to adequately demonstrate the ability of a new algorithm to maintain or exceed a desired level of diagnostic performance under the condition of reduced patient radiation dose. A subset of cases from this data library has been successfully used for such studies 12–19 . Since conducting the 2016 Low Dose CT Grand Challenge, 16 in conjunction with the American Association of Physicists in Medicine and support from NIH awards EB017095 and EB017185, over 500 investigators from over 40 countries have requested access to the 30 abdominal CT studies used in the Grand Challenge.…”

“…A subset of cases from this data library has been successfully used for such studies. [12][13][14][15][16][17][18][19] Since conducting the 2016 Low Dose CT Grand Challenge, 16 in conjunction with the American Association of Physicists in Medicine and support from NIH awards EB017095 and EB017185, over 500 investigators from over 40 countries have requested access to the 30 abdominal CT studies used in the Grand Challenge. In the first 2 weeks after the data library was made public, over 22 TB of data consisting of nearly 7000 image series have been downloaded (one scan results in one image series and one projection data series).…”

3D printing of anatomically realistic phantoms with detection tasks to assess the diagnostic performance of CT images

“…Previous work evaluated low-contrast lesions in cylindrical phantoms with textured background [7] or in CT images with digitally inserted lesions [19,20]. However, to the authors' knowledge, no previous work created anatomically realistic phantoms with low-contrast lesions.…”