CT image fusion in the optimization of replanning during the course of 3-dimensional conformal radiotherapy for non-small-cell lung cancer

Xu, Ximing; Deng, Junjian; Guo, Hong-Ting; Xiang, Miao; Li, Changhu; Xu, Liming; Ge, Wei; Yuan, Guang-Jin; Li, Qianwen; Shan, Shun-Lin

doi:10.1109/bmei.2010.5639366

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…There are several application areas where CT images are considered the prime modality. some of these major applications are the following: (1) head and neck cancer diagnosis [219,192,220,221,222,199], (2) cancer treatment [51,223], (3) image segmentation and integration [51,216], (4) lung cancer treatment [224,225,182,226,227,228,229,230,231,232], (5) prostate cancer treatment [148,233,234,150,235,151,154,155,156,236,237,157,158,238,159,239,160,216,128,240,164,241,165,167,168,242,133], (6) 3D conformal radiation therapy…”

Section: Computerized Tomographymentioning

confidence: 99%

“…The ability to distinguish a damaged tissue, cancerous tissue and a healthy tissue is not an easy task in early diagnosis. Image fusion techniques have been shown to improve the diagnostic performance and screening, and especially improve the clinical monitoring outcomes [224,225,182,226,227,229,256,230,231,272,232,317]. Example of medical image analysis on lungs includes localization study on potentially operable non-small cell lung cancer [225] and dosimetric planning for nonsmall-cell lung cancer [317].…”

Section: Lungsmentioning

confidence: 99%

“…Example of medical image analysis on lungs includes localization study on potentially operable non-small cell lung cancer [225] and dosimetric planning for nonsmall-cell lung cancer [317]. There exists several modalities that is applied in lungs studies such as SPECT [224], PET-CT [275], FDG-PET [318], CT [224,225,182,226,227,229,256,230,231,232], PET [225,226,229,256,272] and FDG [226,227,229,272].…”

Section: Lungsmentioning

confidence: 99%

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Medical image fusion: A survey of the state of the art

2014

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Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

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Section: Computerized Tomographymentioning

confidence: 99%

Section: Lungsmentioning

confidence: 99%

Section: Lungsmentioning

confidence: 99%

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Medical image fusion: A survey of the state of the art

2014

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“…A significant dose distribution difference is typically observed between the second plan and the initial plan, and the difference is often larger in NSCLC tumors with larger volumes. An accurate prediction of the actual dose received by patients has always been one of the important challenges of radiotherapy (11).…”

Section: Discussionmentioning

confidence: 99%

“…However, daily images are difficult to acquire in many institutions, and repositioning and replanning after approximately 20 fractions of radiotherapy is more common (4,10). Moreover, the second round of planning is usually based on a second CT scan, without consideration of the initial treatment plan, which may affect the dosimetric outcomes of the entire treatment (11). Therefore, obtaining the cumulative dose of the whole treatment from multiple plans based on different sets of CT images is very important.…”

Section: Introductionmentioning

confidence: 99%

Dosimetric comparison of dose accumulation between rigid registration and deformation registration in intensity-modulated radiation therapy for large volume non-small cell lung cancer

Ren¹,

Gong²,

Yao³

et al. 2019

Transl Cancer Res TCR

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Background: To evaluate the cumulative dose to the target volumes and organs at risk (OARs) after replanning during intensity-modulated radiation therapy (IMRT) for large volume non-small cell lung cancer (NSCLC) based on rigid registration and deformation registration technologies.Methods: Thirty patients with large volume NSCLC who were treated with IMRT were selected, and two four-dimensional computed tomography (4DCT) scans were acquired before radiotherapy and after 20 fractions of radiotherapy. The initial treatment plan (Plan 1 ) based on the average density projection CT (CT 1-avg ) of the first 4DCT images and the second treatment plan (Plan 2 ) based on CT 2-avg of the second 4DCT images were calculated. Then, the dose distributions of Plan 2 and Plan 1 were accumulated based on rigid and deformation registration technologies to obtain Plan rig and Plan def , respectively. Finally, the volume changes of the gross tumor volume (GTV) and OARs between the two CT scans, and the dose-volume parameters among Plan 1 , Plan 2 , Plan rig and Plan def were compared.Results: Compared with those on the first CT, the mean GTV and heart volume on the second CT decreased by 44.2% and 5.5%, respectively, while the mean volumes of the ipsilateral lung, contralateral lung and total lung increased by 5.2%, 6.2% and 5.8%, respectively. The differences in the above volume parameters between the two CT scans were statistically significant (P<0.05). Compared with those in Plan 1 , the D 95 , D 98 and V 100% values of the IGTV (GTV fusion of 10 CT phases) and planning target volume (PTV) in Plan 2 did not change significantly (P>0.05), and those of Plan rig and Plan def decreased slightly (P<0.05).The dose-volume parameters of the spinal cord, heart, ipsilateral lung and total lung in Plan 2 , Plan rig and Plan def were significantly lower than those in Plan 1 (P<0.05). Among these parameters, V 30 and the mean dose to the heart in Plan 2 , Plan rig and Plan def decreased by 27.3%, 16.5%, and 15.3% and 15.2%, 6.6%, and 5.6% compared to those in Plan 1 , respectively; V 20 and the mean dose to the total lung in Plan 2 , Plan rig and Plan def decreased by 15.6%, 4.5%, and 3.7% and 15.7%, 6.2%, and 5.1% compared to those in Plan 1 , respectively. Some dose-volume parameters (including D 95 and D 98 to the target volume, V 40 of the heart, V 20 and the mean dose to the ipsilateral lung and the total lung) of Plan def were slightly higher than those in Plan rig (P<0.05). The Dice similarity coefficients (DSCs) of the OARs after deformation registration were significantly higher than those after rigid registration (P<0.05). Conclusions:The dose-volume parameters of OARs in Plan 2 were noticeably different from those in Plan 1 , so all of these parameters have large deviations in evaluating the actual dose to the OARs. And, the dose-volume parameters obtained by deformation registration can better predict the actual dose than those obtained by rigid registration.

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Information fusion in the realm of medical applications – A bibliographic glimpse at its growing appeal

Dasarathy¹

2012

Information Fusion

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CT image fusion in the optimization of replanning during the course of 3-dimensional conformal radiotherapy for non-small-cell lung cancer

Cited by 4 publications

References 12 publications

Medical image fusion: A survey of the state of the art

Medical image fusion: A survey of the state of the art

Dosimetric comparison of dose accumulation between rigid registration and deformation registration in intensity-modulated radiation therapy for large volume non-small cell lung cancer

Information fusion in the realm of medical applications – A bibliographic glimpse at its growing appeal

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