Capability of MLEM and OE to Detect Range Shifts With a Compton Camera in Particle Therapy

International audienceTo 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

Absent optic chiasm presenting with horizontal nystagmus

A female infant with horizontal nystagmus and normal ophthalmic examination had isolated absence of the optic chiasm on magnetic resonance imaging. Eye movements were recorded on video and reviewed. Horizontal nystagmus without see-saw nystagmus was observed. Visual evoked potential showed inter-hemispheric asymmetry compatible with the absence of crossing chiasmal fibers. Systemic abnormalities in this patient included cleft lip, preauricular skin tags, broad thumbs, and an anteriorly positioned anus, suggestive of Townes - Brock syndrome. Copyright © SLACK Incorporated

Deep Learning Electronic Cleansing for Single- and Dual-Energy CT Colonography.

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 high-radiation-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