Image registration between CT, SPECT and dose map images of lung and its application to image analysis in radiation therapy

Haneishi, Hideaki; Takita, Naohito; Tsuchida, Daisuke; Mori, Yumi; Toyama, Hinako; Miyamoto, Takao

doi:10.1109/nssmic.2003.1352501

Cited by 4 publications

(5 citation statements)

References 5 publications

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“…Then, the image matching criterion is to find u that maximizes the matching degree between the reference image and the floating image. Therefore, the image registration problem can be equivalent to the minimization problem of the measure function C [ 22 ]:

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Section: Methodsmentioning

confidence: 99%

Correlation-Based Mutual Information Model for Analysis of Lung Cancer CT Image

Vadivu¹,

Gupta²,

Naveed

et al. 2022

BioMed Research International

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Most of the people all over the world pass away from complications related to lung cancer every single day. It is a deadly form of the disease. To improve a person’s chances of survival, an early diagnosis is a necessary prerequisite. In this regard, the existing methods of tumour detection, such as CT scans, are most commonly used to recognize infected regions. Despite this, there are certain obstacles presented by CT imaging, so this paper proposes a novel model which is a correlation-based model designed for analysis of lung cancer. When registering pictures of thoracic and abdominal organs with slider motion, the total variation regularization term may correct the border discontinuous displacement field, but it cannot maintain the local characteristics of the image and loses the registration accuracy. The thin-plate spline energy operator and the total variation operator are spatially weighted via the spatial position weight of the pixel points to construct an adaptive thin-plate spline total variation regular term for lung image CT single-mode registration and CT/PET dual-mode registration. The regular term is then combined with the CRMI similarity measure and the L-BFGS optimization approach to create a nonrigid registration procedure. The proposed method assures the smoothness of interior of the picture while ensuring the discontinuous motion of the border and has greater registration accuracy, according to the experimental findings on the DIR-Lab 4D-CT public dataset and the CT/PET clinical dataset.

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…”

Section: Methodsmentioning

confidence: 99%

Correlation-Based Mutual Information Model for Analysis of Lung Cancer CT Image

Vadivu¹,

Gupta²,

Naveed

et al. 2022

BioMed Research International

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“…Accurate image registration is important for integration of functional data into treatment planning [17,42] but matching of SPECT to treatment planning CT can be challenging [29,102,109,122,123].…”

Section: Image Acquisition and Registration Protocols For Functional mentioning

confidence: 99%

Functional Image-guided Radiotherapy Planning for Normal Lung Avoidance

et al. 2016

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ReuseThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) licence. This licence only allows you to download this work and share it with others as long as you credit the authors, but you can't change the article in any way or use it commercially. More information and the full terms of the licence here: https://creativecommons.org/licenses/ Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. 1 AbstractFor patients with lung cancer undergoing curative intent radiotherapy, functional lung imaging can be incorporated into treatment planning to modify the dose distribution within non-target volume lung by differentiation of lung regions that are functionally defective or viable. This concept of functional image-guided lung avoidance treatment planning has been investigated with several imaging modalities, primarily SPECT but also hyperpolarised gas MRI, PET and CT-based measures of lung biomechanics. Here, we review the application of each of these modalities, review practical issues of lung avoidance implementation, including image registration and the role of both ventilation and perfusion imaging, and provide guidelines for reporting of future lung avoidance planning studies.

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“…In diagnosis of lung disease, registration of lung images is increasingly used [4,5]. For example, registration of the CT images of the same patient, which were scanned at different time, was used to evaluate the development of the lung disease [6,7], and registration of the CT, SPECT and dose map images was used for radiation therapy [8,9].…”

Section: Introductionmentioning

confidence: 99%

Accurate Non-rigid Registration of Lung Images Based on Mutual Information

Cheng¹,

Cui²

2015

Nano BioMed ENG

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In clinical diagnosis of lung disease, registration of lung images from different imaging systems can provide a multi-informative image and improve its diagnostic accuracy. In this study, the original lung images were obtained from computed tomography (CT) and single-photon emission computed tomography (SPECT). After the decomposition of the images using wavelet transform, a non-rigid registration were proposed, in which CT image was used as the reference image and SPECT image was used as the floating image. In registration, the mutual information between the reference and floating images was calculated in the process of translation, rotation and elastic transformation. At last, the result of the registration was evaluated by the edges of the bone, muscle, thorax and lung tissues in CT and SPECT images. It showed an accuracy registration between lung CT and SPECT images.

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Image registration between CT, SPECT and dose map images of lung and its application to image analysis in radiation therapy

Cited by 4 publications

References 5 publications

Correlation-Based Mutual Information Model for Analysis of Lung Cancer CT Image

Correlation-Based Mutual Information Model for Analysis of Lung Cancer CT Image

Functional Image-guided Radiotherapy Planning for Normal Lung Avoidance

Accurate Non-rigid Registration of Lung Images Based on Mutual Information

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