Removing Artifacts from Computed Tomography Images of Heart Using Neural Network with Partial Convolution Layer

Mangileva, Daria; Dokuchaev, Arsenii; Khamzin, Svyatoslav; Zubarev, Stepan; Lyubimtseva, Tamara; Lebedev, Dmitry; Solovyova, Olga

doi:10.1109/usbereit48449.2020.9117624

Cited by 1 publication

(2 citation statements)

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“…However, improvements in the corresponding image domain were not shown, which is a crucial end objective of MAR. The benefit of using metal masks has also been shown by [11] for inpainting in projections and by [12] in images.…”

Section: Introductionmentioning

confidence: 89%

“…We tested the developed model on a clinical dataset against and our results show improvements in image quality for different shape, size and location of metal. The results were compared against LiMAR [3], U-net [9], Mask pyramid [11], and Partial convolution [12] based methods using the same data. Our contribution is to apply deep neural gated convolutional networks based mask guidance for metal inpainting in CBCT images.…”

Section: Introductionmentioning

confidence: 99%

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Metal Artifact Reduction In Cone-Beam Extremity Images Using Gated Convolutions

Agrawal

Hietanen

Särkkä

2021

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

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Quality of cone-beam computed tomography (CBCT) images are marred by artifacts in the presence of metallic implants. Metal artifact correction is a challenging problem in CBCT scanning especially for large metallic objects. The appearance of artifacts also change greatly with the body part being scanned. Metal artifacts are more pronounced in orthopedic imaging, when metals are in close proximity of other high density materials, such as bones. Recently introduced mask incorporating deep learning networks for metal inpainting showed improvements over classical methods in CBCT image quality. However, generalization of results for more than one body part is still not investigated. We investigate, the use of gated convolutions for mask guidance inpainting to improve the filling of the corrupt metal area in projection domain. The neural network was trained with eight clinical metal affected datasets by incorporating data augmentation techniques. In the end, we validate our method on six clinical datasets. Our method shows promising results both in projections and reconstructed images.

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Section: Introductionmentioning

confidence: 89%