DS04 - Vie, santé et bien-être

ROI tomography and dose reduction – ROIdore

Submission summary

In computerized tomography (CT), the density function, or more precisely the attenuation function, of a patient is obtained by reconstruction algorithms applied to radiological projections measured by a scanner. From these radiological projections, it is possible to calculate integrals of the patient attenuation function along lines joining the x-ray source to the pixels on the detector. Mathematically, the data amounts to sampling the X-ray transform or the 2D Radon transform, whose inversion and algorithms, developed more than 50 years ago, have driven the success of the CT scanner in medical imaging. CT scanners measure a full field-of-view (FFOV), i.e. a field of view that contains the entire support of the patient cross-section. Since about 15 years ago, new mathematical approaches and new algorithms have appeared that can perform region-of-interest (ROI) reconstruction from measurements over a reduced field-of-view (RFOV) of the scanner. When the RFOV intersects the exterior of the (known) patient support, stable reconstruction of an ROI inside the RFOV is possible, even if the patient cross-section is not entirely contained inside the RFOV. In this case there exist (measured) lines passing through the RFOV which traverse regions of the patient that are outside the RFOV. In this sense, the patient densities outside the RFOV “contaminate” the measured data when performing reconstruction of an ROI inside the RFOV.

The first objective of the project is to understand in detail the differences between ROI reconstruction from a FFOV scan and from a RFOV scan, in order to be able to quantify the possible loss of image quality of a ROI reconstruction from a RFOV scan compared to that from a FFOV scan. We will achieve this first objective by mathematical and algorithmic developments for ROI reconstruction as well as methods to quantify reconstructed image quality. Our goal is to understand the contamination effect ROI reconstruction in RFOV scanning, and to refine the analysis of the stability of ROI reconstruction.

The second objective concerns quantifying the dose reduction benefits in ROI reconstruction from an RFOV scan, compared to a FFOV scan, for equivalent reconstructed image quality. Our approach here is based on dose models, on experiments using real phantom data and exploiting part
of the results of the first objective.

The first two partners of the project, TIMC-IMAG and CREATIS, are very complementary: TIMC-IMAG has participated in the development of ROI reconstruction from RFOV scanning, and CREATIS has expertise in modeling, including dosimetric modeling, in radiotherapy (the researchers are based at the Léon Bérard Centre) et more general in medical radiology. These two partners are joined by international partners: Michel Defrise (VUB, Brussels), reconstruction expert (co-founder of ROI reconstruction), an Austrian company (“medPhoton”) and The Ottawa Hospital (TOH) who will supply scanner data, and (for TOH) medical expertise, in particular in radiation dosimetry.

The ultimate objective of ROIdoré is to precisely quantify the dose reduction benefits for ROI reconstruction from a RFOV scan compared to a FFOV scan, while maintaining equivalent image quality. The results we obtain will be published in high-impact journals in the field, the developed algorithms will be made available on the open-source platform RTK, and the objective to transfer the algorithms onto medical scanners will be pursued.

Project coordination

Laurent Desbat (Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications de Grenoble)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.


CREATIS - CNRS Centre de Recherche en Acquisition et Traitement d'Images pour la Santé
TIMC-IMAG Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications de Grenoble

Help of the ANR 309,555 euros
Beginning and duration of the scientific project: December 2017 - 48 Months

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