Literature DB >> 26733349

A review of biomechanically informed breast image registration.

John H Hipwell1, Vasileios Vavourakis, Lianghao Han, Thomy Mertzanidou, Björn Eiben, David J Hawkes.   

Abstract

Breast radiology encompasses the full range of imaging modalities from routine imaging via x-ray mammography, magnetic resonance imaging and ultrasound (both two- and three-dimensional), to more recent technologies such as digital breast tomosynthesis, and dedicated breast imaging systems for positron emission mammography and ultrasound tomography. In addition new and experimental modalities, such as Photoacoustics, Near Infrared Spectroscopy and Electrical Impedance Tomography etc, are emerging. The breast is a highly deformable structure however, and this greatly complicates visual comparison of imaging modalities for the purposes of breast screening, cancer diagnosis (including image guided biopsy), tumour staging, treatment monitoring, surgical planning and simulation of the effects of surgery and wound healing etc. Due primarily to the challenges posed by these gross, non-rigid deformations, development of automated methods which enable registration, and hence fusion, of information within and across breast imaging modalities, and between the images and the physical space of the breast during interventions, remains an active research field which has yet to translate suitable methods into clinical practice. This review describes current research in the field of breast biomechanical modelling and identifies relevant publications where the resulting models have been incorporated into breast image registration and simulation algorithms. Despite these developments there remain a number of issues that limit clinical application of biomechanical modelling. These include the accuracy of constitutive modelling, implementation of representative boundary conditions, failure to meet clinically acceptable levels of computational cost, challenges associated with automating patient-specific model generation (i.e. robust image segmentation and mesh generation) and the complexity of applying biomechanical modelling methods in routine clinical practice.

Entities:  

Mesh:

Year:  2016        PMID: 26733349     DOI: 10.1088/0031-9155/61/2/R1

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  8 in total

1.  The compressed breast during mammography and breast tomosynthesis: in vivo shape characterization and modeling.

Authors:  Alejandro Rodríguez-Ruiz; Greeshma A Agasthya; Ioannis Sechopoulos
Journal:  Phys Med Biol       Date:  2017-08-07       Impact factor: 3.609

Review 2.  Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review.

Authors:  Rongrong Guo; Guolan Lu; Binjie Qin; Baowei Fei
Journal:  Ultrasound Med Biol       Date:  2017-10-26       Impact factor: 2.998

3.  Improvements of an objective model of compressed breasts undergoing mammography: Generation and characterization of breast shapes.

Authors:  Alejandro Rodríguez-Ruiz; Steve Si Jia Feng; Jan van Zelst; Suzan Vreemann; Jessica Rice Mann; Carl Joseph D'Orsi; Ioannis Sechopoulos
Journal:  Med Phys       Date:  2017-04-25       Impact factor: 4.071

4.  Image registration method using representative feature detection and iterative coherent spatial mapping for infrared medical images with flat regions.

Authors:  Hao-Jen Wang; Chia-Yen Lee; Jhih-Hao Lai; Yeun-Chung Chang; Chung-Ming Chen
Journal:  Sci Rep       Date:  2022-05-13       Impact factor: 4.996

Review 5.  Review of quantitative multiscale imaging of breast cancer.

Authors:  Michael A Pinkert; Lonie R Salkowski; Patricia J Keely; Timothy J Hall; Walter F Block; Kevin W Eliceiri
Journal:  J Med Imaging (Bellingham)       Date:  2018-01-22

6.  A Regression Model for Predicting Shape Deformation after Breast Conserving Surgery.

Authors:  Hooshiar Zolfagharnasab; Sílvia Bessa; Sara P Oliveira; Pedro Faria; João F Teixeira; Jaime S Cardoso; Hélder P Oliveira
Journal:  Sensors (Basel)       Date:  2018-01-09       Impact factor: 3.576

7.  Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs.

Authors:  Dinesh Pandey; Xiaoxia Yin; Hua Wang; Min-Ying Su; Jeon-Hor Chen; Jianlin Wu; Yanchun Zhang
Journal:  Heliyon       Date:  2018-12-17

8.  A Medical Image Registration Method Based on Progressive Images.

Authors:  Qian Zheng; Qiang Wang; Xiaojuan Ba; Shan Liu; Jiaofen Nan; Shizheng Zhang
Journal:  Comput Math Methods Med       Date:  2021-07-27       Impact factor: 2.238

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.