Dosimetric impact of 4-dimensional computed tomography ventilation imaging-based functional treatment planning for stereotactic body radiation therapy with 3-dimensional conformal radiation therapy

Kadoya, Noriyuki; Cho, Sang Yong; Kanai, Takashi; Onozato, Yusuke; Ito, Kengo; Dobashi, Suguru; Yamamoto, Takaya; Umezawa, Rei; Matsushita, Haruo; Takeda, Ken; Jingu, Keiichi

doi:10.1016/j.prro.2015.03.001

Cited by 28 publications

(19 citation statements)

References 34 publications

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“…Ventilation differences introduced by registration errors can be reduced by smoothing. This evaluation study and previous reports 15 , 18 support the use of ventilation calculated using 4D CT in clinical applications such as radiation treatment planning, 12 , 23 , 24 , 40 , 42 , 43 , 44 , 45 pulmonary function, 15 , 18 and lung disease (22) …”

Section: Discussionsupporting

confidence: 79%

“…For example, 4D CT‐based ventilation has been used in radiation therapy treatment planning to avoid functional lung and to estimate loss of lung function or normal tissue toxicity after radiotherapy in a dose‐dependent manner 40 , 42 , 43 , 44 , 45 . Additional medical applications, such as in diagnostic imaging, may be possible.…”

Section: Discussionmentioning

confidence: 99%

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Evaluation of the ΔV 4D CT ventilation calculation method usingin vivoxenon CT ventilation data and comparison to other methods

Zhang

Latifi

et al. 2016

J Applied Clin Med Phys

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Ventilation distribution calculation using 4D CT has shown promising potential in several clinical applications. This study evaluated the direct geometric ventilation calculation method, namely the ΔV method, with xenon‐enhanced CT (XeCT) ventilation data from four sheep, and compared it with two other published methods, the Jacobian and the Hounsfield unit (HU) methods. Spearman correlation coefficient (SCC) and Dice similarity coefficient (DSC) were used for the evaluation and comparison. The average SCC with one standard deviation was 0.44±0.13 with a range between 0.29 and 0.61 between the XeCT and DLV ventilation distributions. The average DSC value for lower 30% ventilation volumes between the XeCT and ΔV ventilation distributions was 0.55±0.07 with a range between 0.48 and 0.63. Ventilation difference introduced by deformable image registration errors improved with smoothing. In conclusion, ventilation distributions generated using ΔV‐4D CT and deformable image registration are in reasonably agreement with the in vivo XeCT measured ventilation distribution.PACS number(s): 87.57.N‐, 87.57.nj, 87.57.Q‐, 87.85.Pq

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Section: Discussionsupporting

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Evaluation of the ΔV 4D CT ventilation calculation method usingin vivoxenon CT ventilation data and comparison to other methods

Zhang

Latifi

et al. 2016

J Applied Clin Med Phys

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“…Functional planning methods have developed over time in tandem with improvements to treatment planning systems, from initial SPECT studies that used conventional plans with manually modified beam orientations [4,102,103]; to conformal (3D-CRT) planning [5,16,99,104], including a comparison of coplanar and non-coplanar fields [104]; and comparison of 3D-CRT and intensity modulated radiotherapy (IMRT) [105 107]; and a number of IMRT studies that have used between 3-10 beams [5,15,17 voxel-wise functional planning may prove a more useful option [111,115].…”

Section: Planning Studiesmentioning

confidence: 99%

“…However, in some cases the optimisation of beam angles can eliminate significant differences between anatomical and functional plans obtained from fixed beam-only plans [98]. Interestingly, the largest functional V20 differences of 5-7% have been found when using fixed beams [96,98,109,113] in contrast to optimised beam angles which tend to produce 2-3% differences [17,60,99,107,97], although both methods have also produced a large number of cases with no significant difference due to additional functional data [5,98,100,104,105,111].…”

Section: Planning Studiesmentioning

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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“…1 Image guidance is essential to ensure accurate beam targeting to fully exploit the potential of modern RT. 2 Clinically, on-board cone beam computed tomography (CBCT) is commonly used for image-guided RT (IGRT) of prostate cancer. [3][4][5][6][7][8][9] In reality, prostate patient setup using CBCT is complicated by the inherent low image contrast of the prostate in addition to the substantial prostate motion.…”

Section: Introductionmentioning

confidence: 99%

A deep learning framework for prostate localization in cone beam CT‐guided radiotherapy

et al. 2020

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Purpose: To develop a deep learning-based model for prostate planning target volume (PTV) localization on cone beam computed tomography (CBCT) to improve the workflow of CBCT-guided patient setup. Methods: A two-step task-based residual network (T 2 RN) is proposed to automatically identify inherent landmarks in prostate PTV. The input to the T 2 RN is the pretreatment CBCT images of the patient, and the output is the deep learning-identified landmarks in the PTV. To ensure robust PTV localization, the T 2 RN model is trained by using over thousand sets of CT images with labeled landmarks, each of the CTs corresponds to a different scenario of patient position and/or anatomy distribution generated by synthetically changing the planning CT (pCT) image. The changes, including translation, rotation, and deformation, represent vast possible clinical situations of anatomy variations during a course of radiation therapy (RT). The trained patient-specific T 2 RN model is tested by using 240 CBCTs from six patients. The testing CBCTs consists of 120 original CBCTs and 120 synthetic CBCTs. The synthetic CBCTs are generated by applying rotation/translation transformations to each of the original CBCT. Results: The systematic/random setup errors between the model prediction and the reference are found to be <0.25/2.46 mm and 0.14/1.41°in translation and rotation dimensions, respectively. Pearson's correlation coefficient between model prediction and the reference is higher than 0.94 in translation and rotation dimensions. The Bland-Altman plots show good agreement between the two techniques. Conclusions: A novel T 2 RN deep learning technique is established to localize the prostate PTV for RT patient setup. Our results show that highly accurate marker-less prostate setup is achievable by leveraging the state-of-the-art deep learning strategy.

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Dosimetric impact of 4-dimensional computed tomography ventilation imaging-based functional treatment planning for stereotactic body radiation therapy with 3-dimensional conformal radiation therapy

Cited by 28 publications

References 34 publications

Evaluation of the ΔV 4D CT ventilation calculation method usingin vivoxenon CT ventilation data and comparison to other methods

Evaluation of the ΔV 4D CT ventilation calculation method usingin vivoxenon CT ventilation data and comparison to other methods

Functional Image-guided Radiotherapy Planning for Normal Lung Avoidance

A deep learning framework for prostate localization in cone beam CT‐guided radiotherapy

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