We are developing a photon-counting silicon strip detector with 0.4 × 0.5 mm² detector elements for clinical CT applications. Except for the limited detection efficiency of approximately 0.8 for a spectrum of 80 kVp, the largest discrepancies from ideal spectral behaviour have been shown to be Compton interactions in the detector and electronic noise. Using the framework of cascaded system analysis, we reconstruct the 3D MTF and NPS of a silicon strip detector including the influence of scatter and charge sharing inside the detector. We compare the reconstructed noise and signal characteristics with a reconstructed 3D MTF and NPS of an ideal energy-integrating detector system with unity detection efficiency, no scatter or charge sharing inside the detector, unity presampling MTF and 1 × 1 mm² detector elements. The comparison is done by calculating the dose-normalized detectability index for some clinically relevant imaging tasks and spectra. This work demonstrates that although the detection efficiency of the silicon detector rapidly drops for the acceleration voltages encountered in clinical computed tomography practice, and despite the high fraction of Compton interactions due to the low atomic number, silicon detectors can perform on a par with ideal energy-integrating detectors for routine imaging tasks containing low-frequency components. For imaging tasks containing high-frequency components, the proposed silicon detector system can perform approximately 1.1-1.3 times better than a fully ideal energy-integrating system.
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http://dx.doi.org/10.1088/0031-9155/57/8/2373 | DOI Listing |
Individual choices shape life course trajectories of brain structure and function beyond genes and environment. We hypothesized that individual task engagement in response to a learning program results in individualized learning biographies and connectomics. Genetically identical female mice living in one large shared enclosure freely engaged in self-paced, automatically administered and monitored learning tasks.
View Article and Find Full Text PDFPurpose: With the widespread introduction of dual energy computed tomography (DECT), applications utilizing the spectral information to perform material decomposition became available. Among these, a popular application is to decompose contrast-enhanced CT images into virtual non-contrast (VNC) or virtual non-iodine images and into iodine maps. In 2021, photon-counting CT (PCCT) was introduced, which is another spectral CT modality.
View Article and Find Full Text PDFJMIR Serious Games
January 2025
Department of Interaction Design, National Taipei University of Technology, Rm.701-4, Design Building, No.1, Sec.3, Chung-hsiao E. Rd, Taipei, 10608, Taiwan, 886 912-595408, 886 2-87732913.
Background: Complications due to dysphagia are increasingly prevalent among older adults; however, the tediousness and complexity of conventional tongue rehabilitation treatments affect their willingness to rehabilitate. It is unclear whether integrating gameplay into a tongue training app is a feasible approach to rehabilitation.
Objective: Tongue training has been proven helpful for dysphagia treatment.
Med Image Comput Comput Assist Interv
October 2024
Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
Recent advancements in Contrastive Language-Image Pre-training (CLIP) [21] have demonstrated notable success in self-supervised representation learning across various tasks. However, the existing CLIP-like approaches often demand extensive GPU resources and prolonged training times due to the considerable size of the model and dataset, making them poor for medical applications, in which large datasets are not always common. Meanwhile, the language model prompts are mainly manually derived from labels tied to images, potentially overlooking the richness of information within training samples.
View Article and Find Full Text PDFIndian J Psychiatry
November 2024
Department of Psychiatry, All India Institute of Medical Sciences, New Delhi, India.
Background: Functional near-infrared spectroscopy (fNIRS) is being increasingly utilized to visualize the brain areas involved in cognitive activity to understand the human brain better. Its portability and easy setup give it an advantage over other functional brain imaging tools. The current study utilizes fNIRS while performing a Stroop test, which is commonly used to assess the impairment of information selection in depression.
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