Approximate volumetric reconstruction from projected images

Gabor Fichtinger, Sheng Xu, Attila Tanacs, Kieran Murphy, Lee Myers, Jeffery Williams

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A significant problem in planning of volumetrically prescribed localized treatments is the mathematical impossibility to determine the exact three dimensional shape and volume of a target object from its projected images. Reconstruction accuracy also varies with viewing angle, depending on the convexity and aspect ratios of the target object. In response to this problem, we are developing a robust and efficient technique for approximate volumetric reconstruction, which (A) uses no prior information of the shape and volume of the target, (B) does not require exact silhouettes, (C) accepts arbitrary number of images, (D) produces solid object and measure of its volume, (E) provides confidence measure of the reconstruction and drawing of silhouettes, (F) is robust, fast and easy to implement. Preliminary tests suggest that fairly convex objects can be reconstructed from four views, and typically six views with table rotation allow us to reconstruct fine details as small as 1 mm. The method is applicable for any X-ray guided volumetric treatment. Pilot applications will be planning of radiosurgery of arterioveneous malformations (AVMs) and radiofrequency ablation of soft tissue lesions.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages1376-1378
Number of pages3
Volume2208
ISBN (Print)3540426973, 9783540454687
DOIs
StatePublished - 2001
Event4th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2001 - Utrecht, Netherlands
Duration: Oct 14 2001Oct 17 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2208
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other4th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2001
CountryNetherlands
CityUtrecht
Period10/14/0110/17/01

Fingerprint

Planning
Silhouette
Ablation
Target
Aspect ratio
Tissue
Confidence Measure
X rays
Preliminary Test
Soft Tissue
Prior Information
Aspect Ratio
Convexity
Table
Vary
Angle
Three-dimensional
Object
Arbitrary
Mm

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Fichtinger, G., Xu, S., Tanacs, A., Murphy, K., Myers, L., & Williams, J. (2001). Approximate volumetric reconstruction from projected images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2208, pp. 1376-1378). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2208). Springer Verlag. https://doi.org/10.1007/3-540-45468-3_235

Approximate volumetric reconstruction from projected images. / Fichtinger, Gabor; Xu, Sheng; Tanacs, Attila; Murphy, Kieran; Myers, Lee; Williams, Jeffery.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2208 Springer Verlag, 2001. p. 1376-1378 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2208).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Fichtinger, G, Xu, S, Tanacs, A, Murphy, K, Myers, L & Williams, J 2001, Approximate volumetric reconstruction from projected images. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 2208, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2208, Springer Verlag, pp. 1376-1378, 4th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2001, Utrecht, Netherlands, 10/14/01. https://doi.org/10.1007/3-540-45468-3_235
Fichtinger G, Xu S, Tanacs A, Murphy K, Myers L, Williams J. Approximate volumetric reconstruction from projected images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2208. Springer Verlag. 2001. p. 1376-1378. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/3-540-45468-3_235
Fichtinger, Gabor ; Xu, Sheng ; Tanacs, Attila ; Murphy, Kieran ; Myers, Lee ; Williams, Jeffery. / Approximate volumetric reconstruction from projected images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 2208 Springer Verlag, 2001. pp. 1376-1378 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{04be76d55abe4c54aeed3911754e38cc,
title = "Approximate volumetric reconstruction from projected images",
abstract = "A significant problem in planning of volumetrically prescribed localized treatments is the mathematical impossibility to determine the exact three dimensional shape and volume of a target object from its projected images. Reconstruction accuracy also varies with viewing angle, depending on the convexity and aspect ratios of the target object. In response to this problem, we are developing a robust and efficient technique for approximate volumetric reconstruction, which (A) uses no prior information of the shape and volume of the target, (B) does not require exact silhouettes, (C) accepts arbitrary number of images, (D) produces solid object and measure of its volume, (E) provides confidence measure of the reconstruction and drawing of silhouettes, (F) is robust, fast and easy to implement. Preliminary tests suggest that fairly convex objects can be reconstructed from four views, and typically six views with table rotation allow us to reconstruct fine details as small as 1 mm. The method is applicable for any X-ray guided volumetric treatment. Pilot applications will be planning of radiosurgery of arterioveneous malformations (AVMs) and radiofrequency ablation of soft tissue lesions.",
author = "Gabor Fichtinger and Sheng Xu and Attila Tanacs and Kieran Murphy and Lee Myers and Jeffery Williams",
year = "2001",
doi = "10.1007/3-540-45468-3_235",
language = "English (US)",
isbn = "3540426973",
volume = "2208",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "1376--1378",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Approximate volumetric reconstruction from projected images

AU - Fichtinger, Gabor

AU - Xu, Sheng

AU - Tanacs, Attila

AU - Murphy, Kieran

AU - Myers, Lee

AU - Williams, Jeffery

PY - 2001

Y1 - 2001

N2 - A significant problem in planning of volumetrically prescribed localized treatments is the mathematical impossibility to determine the exact three dimensional shape and volume of a target object from its projected images. Reconstruction accuracy also varies with viewing angle, depending on the convexity and aspect ratios of the target object. In response to this problem, we are developing a robust and efficient technique for approximate volumetric reconstruction, which (A) uses no prior information of the shape and volume of the target, (B) does not require exact silhouettes, (C) accepts arbitrary number of images, (D) produces solid object and measure of its volume, (E) provides confidence measure of the reconstruction and drawing of silhouettes, (F) is robust, fast and easy to implement. Preliminary tests suggest that fairly convex objects can be reconstructed from four views, and typically six views with table rotation allow us to reconstruct fine details as small as 1 mm. The method is applicable for any X-ray guided volumetric treatment. Pilot applications will be planning of radiosurgery of arterioveneous malformations (AVMs) and radiofrequency ablation of soft tissue lesions.

AB - A significant problem in planning of volumetrically prescribed localized treatments is the mathematical impossibility to determine the exact three dimensional shape and volume of a target object from its projected images. Reconstruction accuracy also varies with viewing angle, depending on the convexity and aspect ratios of the target object. In response to this problem, we are developing a robust and efficient technique for approximate volumetric reconstruction, which (A) uses no prior information of the shape and volume of the target, (B) does not require exact silhouettes, (C) accepts arbitrary number of images, (D) produces solid object and measure of its volume, (E) provides confidence measure of the reconstruction and drawing of silhouettes, (F) is robust, fast and easy to implement. Preliminary tests suggest that fairly convex objects can be reconstructed from four views, and typically six views with table rotation allow us to reconstruct fine details as small as 1 mm. The method is applicable for any X-ray guided volumetric treatment. Pilot applications will be planning of radiosurgery of arterioveneous malformations (AVMs) and radiofrequency ablation of soft tissue lesions.

UR - http://www.scopus.com/inward/record.url?scp=84958213888&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84958213888&partnerID=8YFLogxK

U2 - 10.1007/3-540-45468-3_235

DO - 10.1007/3-540-45468-3_235

M3 - Conference contribution

AN - SCOPUS:84958213888

SN - 3540426973

SN - 9783540454687

VL - 2208

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1376

EP - 1378

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -