The Use of 4DCT to Reduce Lung Dose: A Dosimetric Analysis

Khan, Fazal Hameed; Bell, Glenn R.; Antony, Jacob; Palmer, Matthew T.; Balter, Peter A; Bucci, Kara; Chapman, Melissa Jane

doi:10.1016/j.meddos.2008.11.005

Cited by 23 publications

(10 citation statements)

References 7 publications

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“…The use of 4D CT scanning is not only able to determine the intrafractional tumor motion, 6 , 8 but also to eliminate respiration motion artifacts 9 , 11 . Moreover, the size of PTVs of lung cancers can be reduced using the technique of 4D CT compared with the technique of 3D CT 5 , 12 , 13 . This is particularly important in the radiotherapy of liver cancer, yet there are only a few reports on this field 14 , 16 …”

Section: Introductionmentioning

confidence: 99%

Analysis of the advantage of individual PTVs defined on axial 3D CT and 4D CT images for liver cancer

Xing

et al. 2012

J Applied Clin Med Phys

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The purpose of this study was to compare positional and volumetric differences of planning target volumes (PTVs) defined on axial three dimensional CT (3D CT) and four dimensional CT (4D CT) for liver cancer. Fourteen patients with liver cancer underwent 3D CT and 4D CT simulation scans during free breathing. The tumor motion was measured by 4D CT. Three internal target volumes (ITVs) were produced based on the clinical target volume from 3DCT (CTV3D): i) A conventional ITV (ITVconv) was produced by adding 10 mm in CC direction and 5 mm in LR and and AP directions to CTV3D; ii) A specific ITV (ITVspec) was created using a specific margin in transaxial direction; iii) ITVvector was produced by adding an isotropic margin derived from the individual tumor motion vector. ITV4D was defined on the fusion of CTVs on all phases of 4D CT. PTVs were generated by adding a 5 mm setup margin to ITVs. The average centroid shifts between PTVs derived from 3DCT and PTV4D in left–right (LR), anterior–posterior (AP), and cranial–caudal (CC) directions were close to zero. Comparing PTV4D to PTVconv, PTVspec, and PTVvector resulted in a decrease in volume size by 33.18% ±12.39%, 24.95% ±13.01%, 48.08% ±15.32%, respectively. The mean degree of inclusions (DI) of PTV4D in PTVconv, and PTV4D in PTVspec, and PTV4D in PTVvector was 0.98, 0.97, and 0.99, which showed no significant correlation to tumor motion vector (r=‐0.470, 0.259, and 0.244; p=0.090, 0.371, and 0.401). The mean DIs of PTVconv in PTV4D, PTVspec in PTV4D, and PTVvector in PTV4D was 0.66, 0.73, and 0.52. The size of individual PTV from 4D CT is significantly less than that of PTVs from 3DCT. The position of targets derived from axial 3DCT images scatters around the center of 4D targets randomly. Compared to conventional PTV, the use of 3D CT‐based PTVs with individual margins cannot significantly reduce normal tissues being unnecessarily irradiated, but may contribute to reducing the risk of missing targets for tumors with large motion.PACS number: 87

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Section: Introductionmentioning

confidence: 99%

Analysis of the advantage of individual PTVs defined on axial 3D CT and 4D CT images for liver cancer

Xing

et al. 2012

J Applied Clin Med Phys

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“…This helps the expansion of CTV in a more accurate and patientspecific way. [3][4][5] However, 4D-CT does not provide respiratory motion information between consecutive phases explicitly, which boosts the investigation of deformable registration methods for estimating the respiratory motion from 4D-CT images. For example, one of the most important applications of 4D-CT deformable registration is to automatically determine the motion envelope of tumor.…”

Section: Ia Motivationmentioning

confidence: 99%

“…10 With more application of 4D-CT, the 4D-CT deformable registration methods are expected to become more useful in the image-guided radiation therapy. 1,5,7 …”

Section: Ia Motivationmentioning

confidence: 99%

Estimating the 4D respiratory lung motion by spatiotemporal registration and super‐resolution image reconstruction

Wang

Lian

et al. 2013

Medical Physics

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Purpose:One of the main challenges in lung cancer radiation therapy is how to reduce the treatment margin but accommodate the geometric uncertainty of moving tumor. 4D-CT is able to provide the full range of motion information for the lung and tumor. However, accurate estimation of lung motion with respect to the respiratory phase is difficult due to various challenges in image registration, e.g., motion artifacts and large interslice thickness in 4D-CT. Meanwhile, the temporal coherence across respiration phases is usually not guaranteed in the conventional registration methods which consider each phase image in 4D-CT independently. To address these challenges, the authors present a unified approach to estimate the respiratory lung motion with two iterative steps. Methods: First, the authors propose a novel spatiotemporal registration algorithm to align all phase images of 4D-CT (in low-resolution) to a high-resolution group-mean image in the common space. The temporal coherence of registration is maintained by a set of temporal fibers that delineate temporal correspondences across different respiratory phases. Second, a super-resolution technique is utilized to build the high-resolution group-mean image with more anatomical details than any individual phase image, thus largely alleviating the registration uncertainty especially in correspondence detection. In particular, the authors use the concept of sparse representation to keep the group-mean image as sharp as possible. Results: The performance of our 4D motion estimation method has been extensively evaluated on both the simulated datasets and real lung 4D-CT datasets. In all experiments, our method achieves more accurate and consistent results in lung motion estimation than all other state-of-the-art approaches under comparison. Conclusions:The authors have proposed a novel spatiotemporal registration method to estimate the lung motion in 4D-CT. Promising results have been obtained, which indicates the high applicability of our method in clinical lung cancer radiation therapy.

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“…Techniques like controlled breath hold or respiratory gating can be uncomfortable or require long treatment sessions [8,12,13,14,15]. By contrast, in tumor tracking, the delivery field follows the tumor during the entire breathing cycle, allowing the patient to breathe freely [16].…”

Section: Introductionmentioning

confidence: 99%

Efficient parameter estimation for anatomy deformation models used in 4D-CT

2016

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Abstract.A critical feature of radiation therapy for cancerous tumors located in the thorax and abdomen is addressing tumor motion due to breathing. To achieve this goal, a CT study (ordinarily denoted as 4D-CT) showing tumor location, size, and shape against time is essential. Several 4D-CT reconstruction methods have been proposed that employ anatomy deformation models. The proposed method estimates temporal parameters for these models using an approach that does not require markers or manual designation of landmark anatomical features. A neural network is trained to estimate the parameters based on simple statistical features of the CT projections. The proposed method achieves an average estimation error of less than seconds, corresponding to a spatial error of less than mm. The accuracy of the proposed method is evaluated in the presence of several limiting constraints such as computational complexity and noise.

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The Use of 4DCT to Reduce Lung Dose: A Dosimetric Analysis

Cited by 23 publications

References 7 publications

Analysis of the advantage of individual PTVs defined on axial 3D CT and 4D CT images for liver cancer

Analysis of the advantage of individual PTVs defined on axial 3D CT and 4D CT images for liver cancer

Estimating the 4D respiratory lung motion by spatiotemporal registration and super‐resolution image reconstruction

Efficient parameter estimation for anatomy deformation models used in 4D-CT

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