The present CAD method using texture analysis to analyze the distribution/heterogeneity of SUV and CT values for malignant and benign bone and soft-tissue lesions improved the differential diagnosis on (18)F-FDG PET/CT images.

Electronic cleansing (EC) is used for computational removal of residual feces and fluid tagged with an orally administered contrast agent on CT colonographic images to improve the visibility of polyps during virtual endoscopic "fly-through" reading. A recent trend in CT colonography is to perform a low-dose CT scanning protocol with the patient having undergone reduced-or noncathartic bowel preparation. Although several EC schemes exist, they have been developed for use with cathartic bowel preparation and highradiation-dose CT, and thus, at a low dose with noncathartic bowel preparation, they tend to generate cleansing artifacts that distract and mislead readers. Deep learning can be used for improvement of the image quality with EC at CT colonography. Deep learning EC can produce substantially fewer cleansing artifacts at dual-energy than at single-energy CT colonography, because the dual-energy information can be used to identify relevant material in the colon more precisely than is possible with the single x-ray attenuation value. Because the number of annotated training images is limited at CT colonography, transfer learning can be used for appropriate training of deep learning algorithms. The purposes of this article are to review the causes of cleansing artifacts that distract and mislead readers in conventional EC schemes, to describe the applications of deep learning and dual-energy CT colonography to EC of the colon, and to demonstrate the improvements in image quality with EC and deep learning at single-energy and dual-energy CT colonography with noncathartic bowel preparation. © RSNA, 2018 • Abbreviations: DCNN = deep convolutional neural network, EC = electronic cleansing, MFI = multimaterial feature image, 3D = three-dimensional © RSNA, 2018After completing this journal-based SA-CME activity, participants will be able to: ■ Describe the fundamentals of EC methods and the cleansing artifacts that the current EC methods generate. ■ Discuss an effective application of deep learning to virtual bowel cleansing. ■ Explain how the combined use of deep learning and dual-energy CT colonography can improve the image quality with EC. See rsna.org/learning-center-rg. SA-CME LEArning ObjECTivESThis copy is for personal use only. To order printed copies, contact reprints@rsna.org RG • Volume 38 Number 7Tachibana et al 2035

Abstract. Visual inspection of diffuse lung disease (DLD) patterns on high-resolution computed tomography (HRCT) is difficult because of their high complexity. We proposed a bag of words based method on the classification of these textural patters in order to improve the detection and diagnosis of DLD for radiologists. Six kinds of typical pulmonary patterns were considered in this work. They were consolidation, ground-glass opacity, honeycombing, emphysema, nodular and normal tissue. Because they were characterized by both CT values and shapes, we proposed a set of statistical measure based local features calculated from both CT values and the eigen-values of Hessian matrices. The proposed method could achieve the recognition rate of 95.85%, which was higher comparing with one global feature based method and two other CT values based bag of words methods.

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SUMMARYComputer-aided diagnosis (CAD) systems on diffuse lung diseases (DLD) were required to facilitate radiologists to read highresolution computed tomography (HRCT) scans. An important task on developing such CAD systems was to make computers automatically recognize typical pulmonary textures of DLD on HRCT. In this work, we proposed a bag-of-features based method for the classification of six kinds of DLD patterns which were consolidation (CON), ground-glass opacity (GGO), honeycombing (HCM), emphysema (EMP), nodular (NOD) and normal tissue (NOR). In order to successfully apply the bag-of-features based method on this task, we focused to design suitable local features and the classifier. Considering that the pulmonary textures were featured by not only CT values but also shapes, we proposed a set of statistical measures based local features calculated from both CT values and eigenvalues of Hessian matrices. Additionally, we designed a support vector machine (SVM) classifier by optimizing parameters related to both kernels and the soft-margin penalty constant. We collected 117 HRCT scans from 117 subjects for experiments. Three experienced radiologists were asked to review the data and their agreed-regions where typical textures existed were used to generate 3009 3D volume-of-interest (VOIs) with the size of 32×32×32. These VOIs were separated into two sets. One set was used for training and tuning parameters, and the other set was used for evaluation. The overall recognition accuracy for the proposed method was 93.18%. The precisions/sensitivities for each texture were 96.67%/95.08% (CON), 92.55%/94.02% (GGO), 97.67%/99.21% (HCM), 94.74%/93.99% (EMP), 81.48%/86.03%(NOD) and 94.33%/90.74% (NOR). Additionally, experimental results showed that the proposed method performed better than four kinds of baseline methods, including two state-of-the-art methods on classification of DLD textures.