Literature DB >> 18495981

Anatomical imaging for radiotherapy.

Philip M Evans1.   

Abstract

The goal of radiation therapy is to achieve maximal therapeutic benefit expressed in terms of a high probability of local control of disease with minimal side effects. Physically this often equates to the delivery of a high dose of radiation to the tumour or target region whilst maintaining an acceptably low dose to other tissues, particularly those adjacent to the target. Techniques such as intensity modulated radiotherapy (IMRT), stereotactic radiosurgery and computer planned brachytherapy provide the means to calculate the radiation dose delivery to achieve the desired dose distribution. Imaging is an essential tool in all state of the art planning and delivery techniques: (i) to enable planning of the desired treatment, (ii) to verify the treatment is delivered as planned and (iii) to follow-up treatment outcome to monitor that the treatment has had the desired effect. Clinical imaging techniques can be loosely classified into anatomic methods which measure the basic physical characteristics of tissue such as their density and biological imaging techniques which measure functional characteristics such as metabolism. In this review we consider anatomical imaging techniques. Biological imaging is considered in another article. Anatomical imaging is generally used for goals (i) and (ii) above. Computed tomography (CT) has been the mainstay of anatomical treatment planning for many years, enabling some delineation of soft tissue as well as radiation attenuation estimation for dose prediction. Magnetic resonance imaging is fast becoming widespread alongside CT, enabling superior soft-tissue visualization. Traditionally scanning for treatment planning has relied on the use of a single snapshot scan. Recent years have seen the development of techniques such as 4D CT and adaptive radiotherapy (ART). In 4D CT raw data are encoded with phase information and reconstructed to yield a set of scans detailing motion through the breathing, or cardiac, cycle. In ART a set of scans is taken on different days. Both allow planning to account for variability intrinsic to the patient. Treatment verification has been carried out using a variety of technologies including: MV portal imaging, kV portal/fluoroscopy, MVCT, conebeam kVCT, ultrasound and optical surface imaging. The various methods have their pros and cons. The four x-ray methods involve an extra radiation dose to normal tissue. The portal methods may not generally be used to visualize soft tissue, consequently they are often used in conjunction with implanted fiducial markers. The two CT-based methods allow measurement of inter-fraction variation only. Ultrasound allows soft-tissue measurement with zero dose but requires skilled interpretation, and there is evidence of systematic differences between ultrasound and other data sources, perhaps due to the effects of the probe pressure. Optical imaging also involves zero dose but requires good correlation between the target and the external measurement and thus is often used in conjunction with an x-ray method. The use of anatomical imaging in radiotherapy allows treatment uncertainties to be determined. These include errors between the mean position at treatment and that at planning (the systematic error) and the day-to-day variation in treatment set-up (the random error). Positional variations may also be categorized in terms of inter- and intra-fraction errors. Various empirical treatment margin formulae and intervention approaches exist to determine the optimum strategies for treatment in the presence of these known errors. Other methods exist to try to minimize error margins drastically including the currently available breath-hold techniques and the tracking methods which are largely in development. This paper will review anatomical imaging techniques in radiotherapy and how they are used to boost the therapeutic benefit of the treatment.

Entities:  

Mesh:

Year:  2008        PMID: 18495981     DOI: 10.1088/0031-9155/53/12/R01

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


  34 in total

Review 1.  PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques.

Authors:  Habib Zaidi; Issam El Naqa
Journal:  Eur J Nucl Med Mol Imaging       Date:  2010-03-25       Impact factor: 9.236

Review 2.  Technological advances in radiotherapy for esophageal cancer.

Authors:  Milan Vosmik; Jiri Petera; Igor Sirak; Miroslav Hodek; Petr Paluska; Jiri Dolezal; Marcela Kopacova
Journal:  World J Gastroenterol       Date:  2010-11-28       Impact factor: 5.742

3.  Assessing the image quality of pelvic MR images acquired with a flat couch for radiotherapy treatment planning.

Authors:  M McJury; A O'Neill; M Lawson; C McGrath; A Grey; W Page; J M O'Sullivan
Journal:  Br J Radiol       Date:  2011-08       Impact factor: 3.039

Review 4.  Task-based measures of image quality and their relation to radiation dose and patient risk.

Authors:  Harrison H Barrett; Kyle J Myers; Christoph Hoeschen; Matthew A Kupinski; Mark P Little
Journal:  Phys Med Biol       Date:  2015-01-07       Impact factor: 3.609

5.  Optimization of the design of thick, segmented scintillators for megavoltage cone-beam CT using a novel, hybrid modeling technique.

Authors:  Langechuan Liu; Larry E Antonuk; Youcef El-Mohri; Qihua Zhao; Hao Jiang
Journal:  Med Phys       Date:  2014-06       Impact factor: 4.071

6.  Proton-counting radiography for proton therapy: a proof of principle using CMOS APS technology.

Authors:  G Poludniowski; N M Allinson; T Anaxagoras; M Esposito; S Green; S Manolopoulos; J Nieto-Camero; D J Parker; T Price; P M Evans
Journal:  Phys Med Biol       Date:  2014-05-01       Impact factor: 3.609

7.  Evaluation of administered dose using portal images in craniospinal irradiation of pediatric patients.

Authors:  Carina Marques Coelho; Raquel Calçada; Sofia Rodrigues; Juan Antonio Barragán; Ana Cravo Sá; Ana Paula Macedo; Maria de Fátima Monsanto
Journal:  Radiol Phys Technol       Date:  2017-03-21

8.  High-performance GPU-based rendering for real-time, rigid 2D/3D-image registration and motion prediction in radiation oncology.

Authors:  Jakob Spoerk; Christelle Gendrin; Christoph Weber; Michael Figl; Supriyanto Ardjo Pawiro; Hugo Furtado; Daniella Fabri; Christoph Bloch; Helmar Bergmann; Eduard Gröller; Wolfgang Birkfellner
Journal:  Z Med Phys       Date:  2011-07-22       Impact factor: 4.820

9.  Modelling the transport of optical photons in scintillation detectors for diagnostic and radiotherapy imaging.

Authors:  Emilie Roncali; Mohammad Amin Mosleh-Shirazi; Aldo Badano
Journal:  Phys Med Biol       Date:  2017-10-04       Impact factor: 3.609

10.  Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs.

Authors:  Fan Tang; Shujun Liang; Tao Zhong; Xia Huang; Xiaogang Deng; Yu Zhang; Linghong Zhou
Journal:  Eur Radiol       Date:  2019-10-24       Impact factor: 5.315

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.