A Multi-Filter and Multi-Scale U-Net for Cone-Beam Computed Tomography with Hardware Constraints
Hauptmann, Andreas; Al-Rubaye, Mustafa; Nieminen, Miika T.; Brix, Mikael A.K. (2024-08-15)
IEEE
15.08.2024
A. Hauptmann, M. Al-Rubaye, M. T. Nieminen and M. A. K. Brix, "A Multi-Filter and Multi-Scale U-Net for Cone-Beam Computed Tomography with Hardware Constraints," 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Seoul, Korea, Republic of, 2024, pp. 69-70, doi: 10.1109/ICASSPW62465.2024.10627045.
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202409065734
https://urn.fi/URN:NBN:fi:oulu-202409065734
Tiivistelmä
Abstract
Learned reconstructions for 3D cone-beam computed tomography (CBCT) require significant hardware resources for training as well as evaluation. In this challenge paper we aim to improve performance of the U-Net architecture for post-processing by creating multiple inputs to the network using varying frequency filters. The networks are able to be trained on a single GPU and achieved 3rd place in the ICASSP 2024 3D-CBCT grand challenge.
Learned reconstructions for 3D cone-beam computed tomography (CBCT) require significant hardware resources for training as well as evaluation. In this challenge paper we aim to improve performance of the U-Net architecture for post-processing by creating multiple inputs to the network using varying frequency filters. The networks are able to be trained on a single GPU and achieved 3rd place in the ICASSP 2024 3D-CBCT grand challenge.
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