Although x-ray intensity shaping filters (bowtie filters) have been used since the introduction of some of the earliest CT scanner models, the clinical implications on dose and noise are not well understood. To achieve the intended dose and noise advantage requires the patient to be centered in the scan field of view. In this study we explore the implications of patient centering in clinical practice. We scanned various size and shape phantoms on a GE LightSpeed VCT scanner using each available source filter with the phantom centers positioned at 0, 3, and 6 cm below the center of rotation (isocenter). Surface doses were measured along with image noise over a large image region. Regression models of surface dose and noise were generated as a function of phantom size and centering error. Methods were also developed to determine the amount of miscentering using a scout scan projection radiograph (SPR). These models were then used to retrospectively evaluate 273 adult body patients for clinical implications. When miscentered by 3 and 6 cm, the surface dose on a 32 cm CTDI phantom increased by 18% and 41% while image noise also increased by 6% and 22%. The retrospective analysis of adult body scout SPR scans shows that 46% of patients were miscentered in elevation by 20-60 mm with a mean position 23 mm below the center of rotation (isocenter). The analysis indicated a surface dose penalty of up to 140% with a mean dose penalty of 33% assuming that tube current is increased to compensate for the increased noise due to miscentering. Clinical image quality and dose efficiency can be improved on scanners with bowtie filters if care is exercised when positioning patients. Automatically providing patient specific centering and scan parameter selection information can help the technologist improve workflow, achieve more consistent image quality and reduce patient dose.

We are developing a computer-aided detection system to assist radiologists in the detection of lung nodules on thoracic computed tomography (CT) images. The purpose of this study was to improve the false-positive (FP) reduction stage of our algorithm by developing features that extract threedimensional (3D) shape information from volumes of interest identified in the prescreening stage. We formulated 3D gradient field descriptors, and derived 19 gradient field features from their statistics. Six ellipsoid features were obtained by computing the lengths and the length ratios of the principal axes of an ellipsoid fitted to a segmented object. Both the gradient field features and the ellipsoid features were designed to distinguish spherical objects such as lung nodules from elongated objects such as vessels. The FP reduction performance in this new 25-dimensional feature space was compared to the performance in a 19-dimensional space that consisted of features extracted using previously developed methods. The performance in the 44-dimensional combined feature space was also evaluated. Linear discriminant analysis with stepwise feature selection was used for classification. The parameters used for feature selection were optimized using the simplex algorithm. Training and testing were performed using a leave-one-patient-out scheme. The FP reduction performances in different feature spaces were evaluated by using the area A z under the receiver operating characteristic curve and the number of FPs per CT section at a given sensitivity as accuracy measures. Our data set consisted of 82 CT scans (3551 axial sections) from 56 patients with section thickness ranging from 1.0 to 2.5 mm. Our prescreening algorithm detected 111 of the 116 solid nodules (nodule size: 3.0-30.6 mm) marked by experienced thoracic radiologists. The test A z values were 0.95±0.01, 0.88±0.02, and 0.94±0.01 in the new, previous, and combined feature spaces, respectively. The number of FPs per section at 80% sensitivity in these three feature spaces were 0.37, 1.61, and 0.34, respectively. The improvement in the test A z with the 25 new features was statistically significant (p<0.0001) compared to that with the previous 19 features alone.

We are developing a computer-aided detection system to aid radiologists in diagnosing lung cancer in thoracic computed tomographic (CT) images. The purpose of this study was to improve the false-positive (FP) reduction stage of our algorithm by developing and incorporating a gradient field technique. This technique extracts 3D shape information from the gray-scale values within a volume of interest. The gradient field feature values are higher for spherical objects, and lower for elongated and irregularly-shaped objects. A data set of 55 thin CT scans from 40 patients was used to evaluate the usefulness of the gradient field technique. After initial nodule candidate detection and rule-based first stage FP reduction, there were 3487 FP and 65 true positive (TP) objects in our data set. Linear discriminant classifiers with and without the gradient field feature were designed for the second stage FP reduction. The accuracy of these classifiers was evaluated using the area A z under the receiver operating characteristic (ROC) curve. The A z values were 0.93 and 0.91 with and without the gradient field feature, respectively. The improvement with the gradient field feature was statistically significant (p=0.01).

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