Original paperAvailability of a simplified lung ventilation imaging algorithm based on four-dimensional computed tomography
Introduction
It is beneficial to utilize pulmonary function images, acquired by single photon emission tomography (SPECT) [1] or positron emission tomography (PET) [2] in radiotherapy. In recent years, four-dimensional computed tomography (4DCT)-based ventilation imaging has become a research hotpot. It can provide functional information in lung without additional dose or monetary cost to the patient, since 4DCT simulation is a clinical routine for thoracic cancer patient in most of radiation oncology clinics. In clinical practice, the 4DCT-based Ventilation Image (CTVI) has been used for assessing the regional function [3] or function changes [4], [5], [6], [7], [8] in lung tissue, predicting radiation pneumonitis [9], [10] and guiding radiotherapy treatment planning [2], [11], [12], [13], [14], [15].
Three ventilation imaging algorithms (VIA) to produce CTVI have been reported. The first VIA (VIAHU) is described by Guerrero et al. [16] which generates static 3D ventilation images based solely on the physical density-change between the exhalation breath-hold and inhalation breath-hold computed tomography (CT) images using deformable image registration (DIR) and the underlying CT density information. The second widely used VIA (VIAJAC) is introduced by Reinhardt et al. [17], which utilizes the Jacobian determinant of the DIR spatial transformation to quantify the regional volume-change of lung volume elements. The third VIA (VIAPRO) is proposed by Kipritidis et al. [18], which estimates the ventilation in terms of the 4D time-averaged regional product of air and tissue densities at each voxel without requiring DIR.
However, the correlations between the CTVIs based on any of above algorithms and the clinical golden standard (99mTc-SPECT [19] or 68Ga-Galligas PET [20]) are highly variable [21], [22], [23], [24]. Many causes have been reported, such as the image quality [25], breathing variations [26], the DIR inaccuracy [27], [28], and the 4DCT sorting method [29]. It still remains unclear which VIA is the most accurate and efficient for clinical use.
Inspired by the study of Kipritidis et al. [18], in this study we propose a simplified VIA (VIAAVG) , which requires only average CT (AVG CT) as input data. Its availability is quantitatively evaluated by comparing the accuracy and efficiency of VIAAVG with those VIAs proposed before (i.e. VIAHU, VIAJAC and VIAPRO) for 50 enrolled patients.
Section snippets
Patients
Fifty thoracic cancer patients (lung or esophageal cancer) were enrolled in this study and underwent radiotherapy in our hospital between 2015 and 2018. 4DCT images were acquired in 4DCT simulation as routine part of radiotherapy prior to the treatment. SPECT ventilation images (VI-SPECT) were also acquired 1–3 days before the first radiotherapy session with the same supine position used in 4DCT simulation. The time interval between 4DCT simulation and VI-SPECT acquisition was less than 3 days
Results
A visual comparison between different ventilation images is illustrates in Fig. 1 for a specific patient in transverse (first row) and coronal (second row) view. The correctness of segmentation algorithm used in this study can be confirmed by comparing the original VI-SPECT (first column) and the segmented VI-SPECT (second column). Generally, segmented CTVIPRO and CTVIAVG are very similar, and showing a high similarity with segmented VI-SPECT. However, the similarity between segmented CTVIJAC,
Discussion
In this study, we propose a simplified algorithm, VIAAVG, inspired by the method proposed by Kipritidis et al. with the aim to reduce input requirement and computation time. In VIAHU and VIAJAC, two phases of 4DCT images (T50 and T00) are required as input data. In VIAPRO, even all phases of 4DCT images are required. In contrast, the simplified algorithm requires only AVG CT to compute CTVIAVG. The availability of this algorithm is evaluated by comparing its accuracy and efficiency with other
Conclusion
The simplified algorithm proposed in this study, VIAAVG, shows a nearly substantial correlation with the commonly used clinic-gold-standard lung functional image (VI-SPECT). Compared with other proposed algorithms, it is a promising modality for the further application of 4DCT-based ventilation imaging in radiation therapy because of less input requirement, improved computation efficiency and independence on DIR.
Acknowledgements
The authors sincerely thank Dr. Li Mei and her team of the Nuclear Medicine Department of Peking TongRen Hospital for their great help in SPECT image acquisitions. This work was supported by the National Natural Science Foundation for Young Scholars of China (Grant No. 81502649) and National Key Projects of Research and Development (Grant No. 2016YFC0904600).
References (34)
- et al.
SPECT V/Q in lung cancer radiotherapy planning
Semin Nucl Med
(2019) - et al.
Functional image-guided radiotherapy planning for normal lung avoidance
Clin Oncol (R Coll Radiol)
(2016) - et al.
Analysis of long-term 4-dimensional computed tomography regional ventilation after radiation therapy
Int J Radiat Oncol Biol Phys
(2015) - et al.
The first patient treatment of computed tomography ventilation functional image-guided radiotherapy for lung cancer
Radiother Oncol
(2016) - et al.
Combined ventilation and perfusion imaging correlates with the dosimetric parameters of radiation pneumonitis in radiation therapy planning for lung cancer
Int J Radiat Oncol Biol Phys
(2015) - et al.
Use of 4-dimensional computed tomography-based ventilation imaging to correlate lung dose and function with clinical outcomes
Int J Radiat Oncol Biol Phys
(2013) - et al.
Reduction of normal lung irradiation in locally advanced non-small-cell lung cancer patients, using ventilation images for functional avoidance
Int J Radiat Oncol Biol Phys
(2007) - et al.
Impact of four-dimensional computed tomography pulmonary ventilation imaging-based functional avoidance for lung cancer radiotherapy
Int J Radiat Oncol Biol Phys
(2011) - et al.
Functional image-guided radiotherapy planning in respiratory-gated intensity-modulated radiotherapy for lung cancer patients with chronic obstructive pulmonary disease
Int J Radiat Oncol Biol Phys
(2012) - et al.
CT ventilation functional image-based IMRT treatment plans are comparable to SPECT ventilation functional image-based plans
Radiother Oncol
(2016)
Quantification of regional ventilation from treatment planning CT
Int J Radiat Oncol Biol Phys
Registration-based estimates of local lung tissue expansion compared to xenon CT measures of specific ventilation
Med Image Anal
Pulmonary ventilation imaging based on 4-dimensional computed tomography: comparison with pulmonary function tests and SPECT ventilation images
Int J Radiat Oncol Biol Phys
Ventilation measured on clinical 4D-CBCT: increased ventilation accuracy through improved image quality
Radiother Oncol
Reproducibility of four-dimensional computed tomography-based lung ventilation imaging
Acad Radiol
Investigation of four-dimensional computed tomography-based pulmonary ventilation imaging in patients with emphysematous lung regions
Phys Med Biol
4DCT-based measurement of changes in pulmonary function following a course of radiation therapy
Med Phys
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