Horizontal extension of the field-of-view in cone-beam tomography at a laboratory instrument

This paper presents a method for horizontally extending the field-of-view in cone-beam tomography to overcome the limitations of traditional detector sizes. The acquisition algorithms are implemented on an Exciscope Polaris phase-contrast micro-CT instrument, which ensures sufficient motion accuracy for horizontal extension solely reliant on stage positions, even at submicron voxel sizes. This innovation enables the construction of an automated data pipeline that bypasses the need for user input or image registration to recombine frames. Utilizing cloud computing for reconstruction, we efficiently handle large data sizes and computational demands. We demonstrate the method both with a reconstruction diameter of 12,500 voxels and with submicron voxel sizes, showcasing significant improvements in imaging capabilities.

Authors

Publication Details

Published
2024-10-17
DOI
https://doi.org/10.1117/12.3027106
Citations
1
Primary Topic
Medical Imaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.40
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Horizontal extension of the field-of-view in cone-beam tomography at a laboratory instrument

William Twengström, Marian Willner, Jakob C. Larsson, Sebastian Allner et al.
1 citations
Medical Imaging Techniques and Applications
0.40
article

Horizontal extension of the field-of-view in cone-beam tomography at a laboratory instrument

William Twengström, Marian Willner, Jakob C. Larsson, Sebastian Allner, Tomas Gidén
article en
1 citations

Abstract

This paper presents a method for horizontally extending the field-of-view in cone-beam tomography to overcome the limitations of traditional detector sizes. The acquisition algorithms are implemented on an Exciscope Polaris phase-contrast micro-CT instrument, which ensures sufficient motion accuracy for horizontal extension solely reliant on stage positions, even at submicron voxel sizes. This innovation enables the construction of an automated data pipeline that bypasses the need for user input or image registration to recombine frames. Utilizing cloud computing for reconstruction, we efficiently handle large data sizes and computational demands. We demonstrate the method both with a reconstruction diameter of 12,500 voxels and with submicron voxel sizes, showcasing significant improvements in imaging capabilities.

Openalex Percentile: Top 34%
Medical Imaging Techniques and Applications
0.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.