Joost Frederik Peters

Impact of Image Quality on the Performance of Computer-Aided Detection of Pulmonary Embolism

OBJECTIVE The purpose of this article is to assess the relationship between CT image quality and the number and type of false-positive (FP) findings found by a prototype computer-aided detection (CAD) algorithm for automatic detection of pulmonary embolism (PE). MATERIALS AND METHODS This retrospective study included 278 subjects (138 men and 140 women; mean age, 57 years; range, 18-88 years) who underwent consecutive CT pulmonary angiographies performed during off hours. Twenty-four percent (68/278) of studies were reported as positive for PE. CAD findings were classified as true-positive or FP by two independent readers and, in cases of discordance, by a third radiologist. Each FP result was classified according to underlying cause. The degree of vascular enhancement, image noise, motion artifacts, overall quality, and presence of underlying lung disease were rated on a 4- or 5-point scale. Chi-square tests and t tests were used to test significance of differences. RESULTS The mean number of FP CAD findings was 4.7 (median, 2) per examination. Most were caused by veins (30% [389/1,298]) or airspace consolidations (22% [286/1,298]). There was a significant positive association between the number of FP findings and image noise, motion artifacts, low vascular enhancement, low overall quality, and the extent of underlying disease. On a per-embolism basis, sensitivity decreased from 70.6% (214/303) for scans with zero to five FP findings, to 62.3% (33/53) for scans with six to 10 FP findings, to 60% (12/20) for scans with more than 10 FP findings. CONCLUSION There is a strong association between CT image quality and the number of FP findings indicated by a CAD algorithm for the detection of PE.

Improved Efficiency in Clinical Workflow of Reporting Measured Oncology Lesions Via PACS-Integrated Lesion Tracking Tool

OBJECTIVE. Imaging provides evidence for the response to oncology treatment by the serial measurement of reference lesions. Unfortunately, the identification, comparison, measurement, and documentation of several reference lesions can be an inefficient process. We tested the hypothesis that optimized workflow orchestration and tight integration of a lesion tracking tool into the PACS and speech recognition system can result in improvements in oncologic lesion measurement efficiency. SUBJECTS AND METHODS. A lesion management tool tightly integrated into the PACS workflow was developed. We evaluated the effect of the use of the tool on measurement reporting time by means of a prospective time-motion study on 86 body CT examinations with 241 measureable oncologic lesions with four radiologists. RESULTS. Aggregated measurement reporting time per lesion was 11.64 seconds in standard workflow, 16.67 seconds if readers had to register measurements de novo, and 6.36 seconds for each subsequent follow-up study. Differences were statistically significant (p < 0.05) for each reader, except for one difference for one reader. CONCLUSION. Measurement reporting time can be reduced by using a PACS workflow-integrated lesion management tool, especially for patients with multiple follow-up examinations, reversing the onetime efficiency penalty at baseline registration.

Improved classification of pulmonary nodules by automated detection of benign subpleural lymph nodes

A new computer algorithm is presented to distinguish a special, most probably benign, subclass of lung nodules called perifissural opacities (PFOs), from potentially malignant nodules. The method focuses on the quantification of two characteristic properties of PFOs, namely the typical flattened surface of the nodule and its attachment to plate-like structures in the direct neighborhood of the nodule (the lung fissures). For the detection of fissures in the proximity of the nodule, an analysis based on the eigenvalues of the Hessian matrix has been developed. Further processing with a voxel grouping algorithm is shown to substantially improve the results of the fissure detection. Through a comparison of Hough transforms of the nodule boundary and the detected fissure voxels, features are constructed that enable a reliable separation of benign PFO from other lesions

Effects of filtering on colorectal polyp detection in ultra low dose CT

We have evaluated the feasibility of polyp detection on simulated ultra low dose CT Colonography data by a computer aided polyp detection (CAD) algorithm. We compared the results of ultra low dose to normal dose data. Twenty-three extensively prepared patients were scanned in prone and supine position at 25 to 100 mAs (average 70 mAs) depending on their waist circumference. Noise was added and the scans were reconstructed at 6.25 and 1.39 mAs. To evaluate the performance of the CAD system, polyps detected by an experienced reviewer and confirmed at colonoscopy were used as ground truth. Curvature, concavity and sphericity of the colon surface were used to detect polyp candidates. Bilateral filtering was used to reduce noise. We present the results for 40 polyps of 6 mm or larger as measured during colonoscopy. The by-polyp sensitivity was 80% for medium size polyps (6-9 mm) and 97% for large polyps (10 mm or larger) at an average value of 5 false-positives per scan for normal dose data. The by-polyp sensitivity was 81% for medium size polyps and 85% for large size polyps at an average value of 5 false-positives per scan for low dose data (6.25 mAs). Finally for the ultra low dose data (1.39 mAs) we achieved a by-polyp sensitivity of 75% for medium size polyps and 97% for large polyps at an average value of 5 false-positives per scan. The conclusion of our study is that CAD for polyp detection is feasible on ultra low dose CT colonography data.