ARTIFICIAL INTELLIGENCE BASED 3D RECONSTRUCTION

A projection dataset from a cone beam computed tomography (CBCT) can be input into a first set of one or more neural networks trained for at least one of saturation correction, truncation correction, and scatter correction. Reconstruction can then be performed on the output projection dataset to pro...

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Bibliographische Detailangaben
Hauptverfasser:	LU, Yujie, DUTTA, Komal, CHANG, Yubing, BAUMGART, John, HU, Yi, HUA, Rui, MANAK, Joseph
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA IMAGE DATA PROCESSING OR GENERATION, IN GENERAL INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES MEASURING PHYSICS TESTING
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Beschreibung
Zusammenfassung:	A projection dataset from a cone beam computed tomography (CBCT) can be input into a first set of one or more neural networks trained for at least one of saturation correction, truncation correction, and scatter correction. Reconstruction can then be performed on the output projection dataset to produce an image dataset. Thereafter, this image dataset can be input into a second set of one or more neural networks trained for at least one of noise reduction and artefact reduction, thereby generating a higher quality CBCT image.