A quantitative evaluation of the performances of the deformable image registration (DIR) algorithm implemented in MIM‐Maestro was performed using multiple similarity indices. Two phantoms, capable of mimicking different anatomical bending and tumor shrinking were built and computed tomography (CT) studies were acquired after applying different deformations. Three different contrast levels between internal structures were artificially created modifying the original CT values of one dataset. DIR algorithm was applied between datasets with increasing deformations and different contrast levels and manually refined with the Reg Refine tool. DIR algorithm ability in reproducing positions, volumes, and shapes of deformed structures was evaluated using similarity indices such as: landmark distances, Dice coefficients, Hausdorff distances, and maximum diameter differences between segmented structures. Similarity indices values worsen with increasing bending and volume difference between reference and target image sets. Registrations between images with low contrast (40 HU) obtain scores lower than those between images with high contrast (970 HU). The use of Reg Refine tool leads generally to an improvement of similarity parameters values, but the advantage is generally less evident for images with low contrast or when structures with large volume differences are involved. The dependence of DIR algorithm on image deformation extent and different contrast levels is well characterized through the combined use of multiple similarity indices.

A multiparametric method to assess the MIM deformable image registration algorithm / Silvia Calusi, Giusy Labanca, Margherita Zani, Marta Casati, Livia Marrazzo, Linhsia Noferini, Cinzia Talamonti, Franco Fusi, Isacco Desideri, Pierluigi Bonomo, Lorenzo Livi, Stefania Pallotta. - In: JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS. - ISSN 1526-9914. - ELETTRONICO. - 20:(2019), pp. 75-82. [10.1002/acm2.12564]

A multiparametric method to assess the MIM deformable image registration algorithm

Silvia Calusi
Writing – Original Draft Preparation
;
Margherita Zani
Methodology
;
Marta Casati
Validation
;
Livia Marrazzo
Investigation
;
Linhsia Noferini
Software
;
Cinzia Talamonti
Investigation
;
Franco Fusi
Investigation
;
Isacco Desideri
Membro del Collaboration Group
;
Pierluigi Bonomo
Methodology
;
Lorenzo Livi
Investigation
;
Stefania Pallotta
Supervision
2019

Abstract

A quantitative evaluation of the performances of the deformable image registration (DIR) algorithm implemented in MIM‐Maestro was performed using multiple similarity indices. Two phantoms, capable of mimicking different anatomical bending and tumor shrinking were built and computed tomography (CT) studies were acquired after applying different deformations. Three different contrast levels between internal structures were artificially created modifying the original CT values of one dataset. DIR algorithm was applied between datasets with increasing deformations and different contrast levels and manually refined with the Reg Refine tool. DIR algorithm ability in reproducing positions, volumes, and shapes of deformed structures was evaluated using similarity indices such as: landmark distances, Dice coefficients, Hausdorff distances, and maximum diameter differences between segmented structures. Similarity indices values worsen with increasing bending and volume difference between reference and target image sets. Registrations between images with low contrast (40 HU) obtain scores lower than those between images with high contrast (970 HU). The use of Reg Refine tool leads generally to an improvement of similarity parameters values, but the advantage is generally less evident for images with low contrast or when structures with large volume differences are involved. The dependence of DIR algorithm on image deformation extent and different contrast levels is well characterized through the combined use of multiple similarity indices.
2019
20
75
82
Silvia Calusi, Giusy Labanca, Margherita Zani, Marta Casati, Livia Marrazzo, Linhsia Noferini, Cinzia Talamonti, Franco Fusi, Isacco Desideri, Pierluigi Bonomo, Lorenzo Livi, Stefania Pallotta
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1153037
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