Ricardo Scott Avila

Pulmonary Nodule Detection with Low-Dose CT of the Lung: Agreement Among Radiologists

OBJECTIVE The purpose of our study was to assess relative intra- and interobserver agreement in detecting pulmonary nodules when interpreting low-dose chest CT screening examinations. MATERIALS AND METHODS Two hundred ninety-three selected low-dose CT examinations of the lung were independently interpreted by three radiologists to detect and classify pulmonary nodules. The data set selected was enriched with examinations depicting pulmonary nodules. A subset of 30 examinations was interpreted twice. All pulmonary nodules greater than 1.0 mm were marked. All nodules greater than 3.0 mm were marked, measured, and scored as to their probability of being benign or malignant. Nodule-based and examination-based relative reviewer agreements were evaluated using percentage of agreement and kappa statistics. Similar assessments were performed on the subset of examinations interpreted twice. RESULTS The three radiologists identified a total of 470, 729, and 876 pulmonary nodules of which 395, 641, and 778 were rated as noncalcified with some level of suspicion for being malignant. Nodule-based interobserver agreement among the radiologists was poor (highest kappa value in a paired comparison, 0.120). Examination-based agreement was higher (highest kappa value in a paired comparison, 0.458). Intraobserver agreement was higher than interobserver agreement for examination-based agreement (highest kappa = 0.889) but lower for nodule-based agreement (highest kappa = -0.035). Agreement improved as the suspicion of malignancy increased. CONCLUSION Unaided intra- and interobserver agreement in detecting pulmonary nodules in low-dose CT of the lung is relatively low. Computer-assisted detection may provide the consistency that is needed for this purpose.

Model-based detection of lung nodules in computed tomography exams1

Abstract Rationale and objectives In this study, we developed a prototype model-based computer aided detection (CAD) system designed to automatically detect both solid and subsolid pulmonary nodules in computed tomography (CT) images. By using this CAD algorithm, along with the radiologist’s initial interpretation, we aim to improve the sensitivity of radiologic readings of CT lung exams. Materials and methods We have developed a model-based CAD algorithm through the use of precise mathematic models that capture scanner physics and anatomic information. Our model-based CAD algorithm uses multiple segmentation algorithms to extract noteworthy structures in the lungs and a Bayesian statistical model selection framework to determine the probability of various anatomical events throughout the lung. We tested this algorithm on 50 low-dose CT lung cancer screening cases in which ground truth was produced through readings by three expert chest radiologists. Results Using this model-based CAD algorithm on 50 low-dose CT cases, we measured potential sensitivity improvements of 7% and 5% in two radiologists with respect to all noncalcified nodules, solid and subsolid, greater than 5 mm in diameter. The third radiologist did not miss any nodules in the ground truth set. The CAD algorithm produced 8.3 false positives per case. Conclusion Our prototype CAD system demonstrates promising results as a tool to improve the quality of radiologic readings by increasing radiologist sensitivity. A significant advantage of this model-based approach is that it can be easily extended to support additional anatomic models as clinical understanding and scanning practices improve.

Coronary Artery Angiography Using Multislice Computed Tomography Images

Multislice CT scanners are the newest class of CT scanners and they have not one but many detectors. These scanners can acquire up to 4 slices of data from the body in the same time it takes a single-slice CT scanner to acquire one. Multislice CT allows for rapid cardiac imaging during a single breath-hold. A multislice scanner operated in helical mode provides information that can be used to reconstruct 3D cardiac images in arbitrary phases of the cardiac cycle. A 71-year-old man with hypertension, hypercholesterolemia, and known aortic and peripheral vascular disease was imaged with a LightSpeed 4-slice, multislice CT scanner (GE Medical Systems). Ten minutes before the cardiac scan, the patient received intravenous contrast material (150 mL of 300 mgI/mL) for a CT study of his abdomen. The cardiac …

Model-based detection of lung lesions in CT exams

Abstract The thorough detection of nodules in high-resolution CT lung scans is an increasingly difficult, labor-intensive, and critical radiological task. Recent clinical research on early lung cancer CT presentation has demonstrated the significant clinical need to detect the more subtle subsolid nodules as well as the traditional solid nodule. We have developed a model-based computer-aided detection (CAD) algorithm designed to automatically detect both of these nodule presentation types through the use of precise mathematical models that capture scanner physics and anatomy and pathology domain knowledge. Our model-based CAD algorithm utilizes a Bayesian framework for determining the probability of multiple competing anatomical and pathological events throughout the lung. Using this model-based CAD algorithm on 50 low-dose CT lung cancer screening cases, we measured a 3.9% average improvement in radiologist sensitivity (93.8% to 97.7%) with 8.3 false positives per case for all nodules ≥5 mm in size. This model-based approach can be easily extended to support additional anatomy and pathology models as clinical understanding and scanning practices improve.

Region of interest and windowing-based progressive medical image delivery using JPEG2000

An important telemedicine application is the perusal of CT scans (digital format) from a central server housed in a healthcare enterprise across a bandwidth constrained network by radiologists situated at remote locations for medical diagnostic purposes. It is generally expected that a viewing station respond to an image request by displaying the image within 1-2 seconds. Owing to limited bandwidth, it may not be possible to deliver the complete image in such a short period of time with traditional techniques. In this paper, we investigate progressive image delivery solutions by using JPEG 2000. An estimate of the time taken in different network bandwidths is performed to compare their relative merits. We further make use of the fact that most medical images are 12-16 bits, but would ultimately be converted to an 8-bit image via windowing for display on the monitor. We propose a windowing progressive RoI technique to exploit this and investigate JPEG 2000 RoI based compression after applying a favorite or a default window setting on the original image. Subsequent requests for different RoIs and window settings would then be processed at the server. For the windowing progressive RoI mode, we report a 50% reduction in transmission time.