2026-04-29T01:00:00Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/66702024-09-27T10:12:02Zcom_20.500.12105_19604com_20.500.12105_2051col_20.500.12105_19607
Repisalud
author
de Molina, Claudia
author
Serrano, Estefania
author
Garcia-Blas, Javier
author
Carretero, Jesus
author
Desco, Manuel
author
Abella, Monica
funder
Ministerio de Economía y Competitividad (España)
funder
Ministerio de Economía, Industria y Competitividad (España)
funder
Ministerio de Sanidad y Consumo (España)
funder
Unión Europea. Comisión Europea
funder
Ministerio de Educación, Cultura y Deporte (España)
2018-11-22T08:10:51Z
2018-11-22T08:10:51Z
2018
BMC Bioinformatics. 2018; 19(1):171
1471-2105
http://hdl.handle.net/20.500.12105/6670
29764362
10.1186/s12859-018-2169-3
BMC Bioinformatics
Background: Standard cone-beam computed tomography (CBCT) involves the acquisition of at least 360 projections rotating through 360 degrees. Nevertheless, there are cases in which only a few projections can be taken in a limited angular span, such as during surgery, where rotation of the source-detector pair is limited to less than 180 degrees. Reconstruction of limited data with the conventional method proposed by Feldkamp, Davis and Kress (FDK) results in severe artifacts. Iterative methods may compensate for the lack of data by including additional prior information, although they imply a high computational burden and memory consumption. Results: We present an accelerated implementation of an iterative method for CBCT following the Split Bregman formulation, which reduces computational time through GPU-accelerated kernels. The implementation enables the reconstruction of large volumes (> 1024 3 pixels) using partitioning strategies in forward- and back-projection operations. We evaluated the algorithm on small-animal data for different scenarios with different numbers of projections, angular span, and projection size. Reconstruction time varied linearly with the number of projections and quadratically with projection size but remained almost unchanged with angular span. Forward- and back-projection operations represent 60\% of the total computational burden. Conclusion: Efficient implementation using parallel processing and large-memory management strategies together with GPU kernels enables the use of advanced reconstruction approaches which are needed in limited-data scenarios. Our GPU implementation showed a significant time reduction (up to 48x) compared to a CPU-only implementation, resulting in a total reconstruction time from several hours to few minutes.
eng
GPU
Memory management
Parallel processing
Iterative reconstruction
Split Bregman
Limited-data tomography
CBCT
CONE-BEAM CT
GPU-accelerated iterative reconstruction for limited-data tomography in CBCT systems
journal article
URL
https://repisalud.isciii.es/bitstreams/66177b59-83b7-4e14-8171-8afd2c7b6163/download
File
MD5
cba8ecb52a56513faa29ef40bd1a9454
1448624
application/pdf
GPUacceleratedIterativeReconstruction_2018.pdf
URL
https://repisalud.isciii.es/bitstreams/e6c3ece8-1819-45eb-b798-27bf816fd67f/download
File
MD5
7935a5530cf690061112e92412b0cdf9
26835
text/plain
GPUacceleratedIterativeReconstruction_2018.pdf.txt