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

To identify range deviations by using Compton cameras (CCs), tomographic image reconstruction of CC data is needed. Within this context, image reconstruction is usually performed using maximum likelihood expectation maximization (MLEM), and more recently, the origin ensemble (OE) algorithm. In this article, we investigate how MLEM and OE affect the precision and accuracy of estimated range deviations. In particular, we focus on the effects of data selection, statistical fluctuations, and artifact reduction. The use of external information of the beam path through a hodoscope was also explored. Additionally, two methods to calculate range deviations were tested. To this aim, realistic proton beams were simulated using GATE and data from single spots as well as from seven contiguous spots of an energy layer were reconstructed. MLEM and OE reacted differently to the poor data statistics. In general, both algorithms were able to detect range shifts for single spots, particularly when multiple coincidences were also considered. Selection of events corresponding to the most relevant energy peaks decreased the identification performance due to the lower statistics. When data from several contiguous spots were jointly reconstructed, the accuracy of the results degraded significantly, and nonzero shifts were assigned when no shifts had occurred. The limited size of the cameras and the subsequent restriction in the orientation and aperture of detected cones, as well as in the number of detected events are major challenges. Future efforts should be devoted to noise regularization and compensation for data truncation. Index Terms-Compton camera (CC), image reconstruction, particle therapy, prompt gamma imaging, range verification. I. INTRODUCTION I N RECENT years, Compton cameras (CCs) have attracted attention for range verification in particle therapy [1]-[6]. Within this context, the goal of the CC is to detect prompt gamma-rays (PG) that are emitted during de-excitation of Manuscript

Semi-automatically derived "lymph node volume" and "bi-dimensional WHO" significantly reduce the number of misclassified patients in the CT follow-up of malignant lymphoma by at least 10 %. However, lymph node volumetry does not outperform bi-dimensional WHO.

• In a multicentre setting, semi-automatic measurements are more accurate than manual assessments. • Lymph node volumetry outperforms all other semi-automatically and manually performed measurements. • Use of semi-automatic lymph node analyses can reduce the inter-observer variability.

Properties of different scintillating fibers were examined and compared, as a part of the design optimization of the SiFi-CC detector, currently under development for proton therapy monitoring. Three scintillating materials were considered as candidates to constitute the active part of the detector: LYSO:Ce, LuAG:Ce and GAGG:Ce. All investigated samples had an elongated, fiber-like shape and were read out on both ends using silicon photomultipliers (SiPMs). Samples of LYSO:Ce material provided by four different manufacturers were included in the survey. Additionally, different types of optical coupling media, wrapping and coating materials were investigated. The following properties of the scintillating fibers were determined: attenuation length, position-, energy-, timing resolution and light collection. Two models were used to describe the propagation of scintillating light in the fiber and quantify the light attenuation: exponential light attenuation model (ELA) and exponential light attenuation model with light reflection (ELAR). Energy and position reconstruction were also performed using the two above methods. It was shown, that the ELAR model performs better in terms of description of the light attenuation process. However, energy and position reconstruction results are comparable for the two proposed methods. Based on the results of measurements with scintillating fibers in different configurations we concluded that LYSO:Ce fibers wrapped in Al foil (bright side facing towards the fiber) provided the best trade-off between the energy- (8.56% at 511 keV) and position (32 mm) resolutions and thus will be the optimal choice for the SiFi-CC detector. Additionally, the study of different optical coupling media showed, that the silicone pads coupling ensures good stability of the system performance and parameters.