Flowchart of the denoising convolutional neural network metal artifact reduction hybrid reconstruction (DnCNN-MARHR) algorithm.

<p>The DnCNN-MARHR algorithm was employed to decrease metal artifacts in weighted hybrid reconstructed images (maximum likelihood expectation maximization [MLEM] and back projection). This was achieved using a training network (mini-batch stochastic gradient descent algorithm with momentum) to...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Tsutomu Gomi (7382183) (author)
مؤلفون آخرون:	Rina Sakai (7382186) (author), Hidetake Hara (7382189) (author), Yusuke Watanabe (608534) (author), Shinya Mizukami (426933) (author)
منشور في:	2019
الموضوعات:	Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified network metal artifact reduction MLEM DEMDRA DnCNN-MARHR algorithm gradient descent algorithm reconstruction technique-total variation projection data denoising convolutional SART-TV image Artifact spread functions dual-energy material decomposition reconstruction algorithm ASF FBP texture analysis DT likelihood expectation maximization AI VM MAR processing metal artifact reduction
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Flowchart of the denoising convolutional neural network metal artifact reduction hybrid reconstruction (DnCNN-MARHR) algorithm.

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