Results 31 to 40 of about 462,780 (278)
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information [PDF]
, 2019 Dimensionality reduction and manifold learning methods such as t-Distributed Stochastic Neighbor Embedding (t-SNE) are routinely used to map high-dimensional data into a 2-dimensional space to visualize and explore the data.
De Bie, Tijl +4 more
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Synthetic CT generation from CBCT images via deep learning
Medical Physics, 2020 PurposeCone‐beam computed tomography (CBCT) scanning is used daily or weekly (i.e., on‐treatment CBCT) for accurate patient setup in image‐guided radiotherapy. However, inaccuracy of CT numbers prevents CBCT from performing advanced tasks such as dose calculation and treatment planning. Motivated by the promising performance of deep learning in medical
Liyuan, Chen +4 more
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A Novel Synthetic CT Generation Method Using Multitask Maximum Entropy Clustering
IEEE Access, 2019 Due to the risk of radiation from computed tomography (CT) scanning on the human body, the number of CT scans that can be performed on an individual each year is limited. However, CT images play a very important role in medical diagnosis. Therefore, this
Yizhang Jiang +4 more
doaj +1 more source
Patient-specific stopping power calibration for proton therapy planning based on single-detector proton radiography. [PDF]
, 2015 A simple robust optimizer has been developed that can produce patient-specific calibration curves to convert x-ray computed tomography (CT) numbers to relative stopping powers (HU-RSPs) for proton therapy treatment planning.
Bentefour, EH +5 more
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Advances in generating sCT images from brain MR images based on deep learning
生物医学转化Radiotherapy is a crucial modality in the treatment of malignant tumors. Precision radiotherapy planning typically requires the integration of computed tomography (CT) and magnetic resonance imaging (MRI).
Yang Zhihui, Li Hao, Li Fengsen
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CT synthesis from MRI with an improved multi-scale learning network
Frontiers in Physics, 2023 Introduction: Using MRI to synthesize CT and substitute its function in radiation therapy has drawn wide research interests. Currently, deep learning models have become the first choice for MRI—CT synthesis because of their ability to study complex non ...
Yan Li, Sisi Xu, Yao Lu, Zhenyu Qi
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Development of a synthetic phantom for the selection of optimal scanning parameters in CAD-CT colonography [PDF]
, 2006 The aim of this paper is to present the development of a synthetic phantom that can be used for the selection of optimal scanning parameters in computed tomography (CT) colonography. In this paper we attempt to evaluate the influence of the main scanning
Acar +64 more
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Physics and Imaging in Radiation Oncology, 2023 Background and purpose: Adaptive radiotherapy (ART) decision-making benefits from dosimetric information to supplement image inspection when assessing the significance of anatomical changes.
Caitlin Allen +3 more
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Synthetic Gene Circuits: Design with Directed Evolution [PDF]
, 2007 Synthetic circuits offer great promise for generating insights into nature's underlying design principles or forward engineering novel biotechnology applications. However, construction of these circuits is not straightforward.
Arnold, Frances H., Haseltine, Eric L.
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Synthetic CT in Musculoskeletal Disorders
, 2023 AbstractRepeated computed tomography (CT) examinations increase patients' ionizing radiation exposure and health costs, making an alternative method desirable. Cortical and trabecular bone, however, have short T2 relaxation times, causing low signal intensity on conventional magnetic resonance (MR) sequences.
Lombardi, Alecio F +6 more
openaire +2 more sources