Evaluation of 4D-CT Lung Registration

Kabus, Sven; Klinder, Tobias; Murphy, Keelin; Ginneken, Bram van; Lorenz, Cristian; Pluim, Josien P. W.

doi:10.1007/978-3-642-04268-3_92

Cited by 56 publications

(45 citation statements)

References 11 publications

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“…For reconstruction, the CT images (GE) or projections (Siemens) were sorted into ten respiratory bins by the phase-based method using GE Advantage 4D or Siemens Biograph 40 software. The second step was deformable image registration (DIR) for spatial mapping of the peak-inhale 4D-CT image data set (moving) to the peak-exhale image data set (fixed) using a volumetric elastic DIR method, which was found to have sub-voxel accuracy in the previous studies [23][24][25]. The same level of accuracy was assumed in this study.…”

Section: Ct Ventilation Imagingmentioning

confidence: 95%

CT ventilation functional image-based IMRT treatment plans are comparable to SPECT ventilation functional image-based plans

Kida

Bal

Kabus

et al. 2016

Radiotherapy and Oncology

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Radiation-induced lung injury has detrimental effects on lung function and is associated with radiation pneumonitis, which is a potentially fatal toxicity and occurs in up to 30% of lung cancer patients treated with radiotherapy [1,2]. Radiotherapy that selectively avoids irradiating highly-functional lung regions may reduce pulmonary toxicity [3][4][5]. This hypothesis is supported by several reports in the literature. Lung dose-function metrics were found to improve predictive power for pulmonary toxicity compared to dose-volume metrics [6,7]. The mean functional V 20 (fV 20 ) (percent lung function receiving P20 Gy) for patients who developed Grade P3 pneumonitis was 4.3% greater (p = 0.09) than those who did not, and it was closer to statistical significance than the V 20 (percent lung volume receiving P20 Gy) (p = 0.33) [7].Several modalities exist for pulmonary ventilation imaging [8][9][10][11]. Ventilation images can also be acquired by a method based on four-dimensional (4D) computed tomography (CT) and image processing/analysis [12,13], henceforth referred to as CT ventilation imaging. CT ventilation imaging has the potential for widespread clinical implementation, as 4D-CT is routinely acquired for treatment planning at many centers [14] and ventilation computation only involves image processing/analysis without extra scans to patients. Additionally, CT ventilation imaging has a shorter scan time, higher spatial resolution (the exact resolution is unknown), lower cost, and/or greater availability than other modalities.Validation studies for CT ventilation imaging have been focused on cross-modality image comparisons [15][16][17][18][19][20][21][22]. For example, studies with mechanically ventilated sheep have demonstrated strong correlations between CT ventilation and xenon-CT ventilation [15,16]. Human studies have also reported reasonable correlations with ventilation scintigraphy [21], single-photon emission CT (SPECT) ventilation [22] and other modalities [19,20]. Previously we have demonstrated that CT ventilation in SPECT ventilationdefined defect regions of interest (ROIs) is significantly lower than http://dx

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Section: Ct Ventilation Imagingmentioning

confidence: 95%

CT ventilation functional image-based IMRT treatment plans are comparable to SPECT ventilation functional image-based plans

Kida

Bal

Kabus

et al. 2016

Radiotherapy and Oncology

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“…The state-of-the art registration methods for lung CT images are mainly intensity-based approaches [2] because the feature-based methods generally produce less accurate results [3].…”

Section: Introductionmentioning

confidence: 99%

“…A feature-based method establishes deformations based on lowdimensional features, derived from the original images, while intensity-based method considers intensity information over complete image. The state-of-the art registration methods for lung CT images are mainly intensity-based approaches [2] because the feature-based methods generally produce less accurate results [3].…”

Section: Introductionmentioning

confidence: 99%

Lung CT registration combining intensity, curves and surfaces

Gorbunova

Durrleman

et al. 2010

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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In this paper we propose a registration method that combines intensity information with geometrical information in the form of curves and surfaces derived from lung CT images. Vessel tree centerlines and lung surfaces were extracted from segmented structures. First, a current-based registration was applied to align the pulmonary vessel tree and the lung surfaces. Subsequently, the resulting deformation field was used to constrain an intensity-based registration method. We applied the combined registration on a set of image pairs, extracted at the end exhale and the end inhale phases of 4D-CT scans. The proposed combined registration was compared to intensity-based registration, using a set of manually selected landmarks. The proposed registration decreases the mean and the standard deviation of the target registration errors for all 5 cases to on average 1.47±1.05 mm, compared to the intensity-based registration without constraint 1.74 ± 1.31 mm.Index Terms-Image registration, BSplines, currents, lung CT. INTRODUCTIONThe ultimate goal of any registration algorithm is to establish dense point-to-point correspondence between two images. Generally, registration of lung CT images is a difficult problem due to the possible large variation between the scans. Scans of the same patient taken at maximum inspiration, can have more than 0.5 liter difference in lung volume. The registration of end exhale and end inhale phases of 4D-CT lung images is an even more difficult problem due to the large and non-uniform deformations during the breathing cycle [1].Image registration methods can be divided into two groups of methods: intensity-based and feature-based. A feature-based method establishes deformations based on lowdimensional features, derived from the original images, while * This work is financially supported by the Danish Council for Strategic Research under the Programme Commission for Nanoscience and Technology, Biotechnology and IT (NABIIT), the Netherlands Organization for Scientific Research (NWO), and AstraZeneca, Lund, Sweden. Authors would like to thank Jon Sporring, Copenhagen University, Department of Computer Science, eScience Center, for fruitful discussions.intensity-based method considers intensity information over complete image. The state-of-the art registration methods for lung CT images are mainly intensity-based approaches [2] because the feature-based methods generally produce less accurate results [3].Recently, Li et al. [4] developed an image registration algorithm where the intensity-based registration was improved with the subsequent bio-mechanical simulation of lung inflation. Results showed an improvement in both accuracy of registration and physical plausibility of the deformation field for the combined approach. We previously developed a feature-based algorithm for registering lung CT images and compared it to intensity-based registration [5]. The overall accuracy of the feature-based algorithm was slightly worse than that of the intensity-based algorithm, but in 35 % of landmarks t...

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“…The target registration errors were found to be less than the voxel dimension on average. [31][32][33] The same algorithm parameters were employed in this study. To quantify regional air volume change, we employed the Jacobian-based ventilation metric.…”

Section: C 4d Ct Ventilation Imagingmentioning

confidence: 99%

4D CT lung ventilation images are affected by the 4D CT sorting method

et al. 2013

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Purpose: Four-dimensional (4D) computed tomography (CT) ventilation imaging is a novel promising technique for lung functional imaging. The current standard 4D CT technique using phase-based sorting frequently results in artifacts, which may deteriorate the accuracy of ventilation imaging. The purpose of this study was to quantify the variability of 4D CT ventilation imaging due to 4D CT sorting. Methods: 4D CT image sets from nine lung cancer patients were each sorted by the phase-based method and anatomic similarity-based method, designed to reduce artifacts, with corresponding ventilation images created for each method. Artifacts in the resulting 4D CT images were quantified with the artifact score which was defined based on the difference between the normalized cross correlation for CT slices within a CT data segment and that for CT slices bordering the interface between adjacent CT data segments. The ventilation variation was quantified using voxel-based Spearman rank correlation coefficients for all lung voxels, and Dice similarity coefficients (DSC) for the spatial overlap of low-functional lung volumes. Furthermore, the correlations with matching single-photon emission CT (SPECT) ventilation images (assumed ground truth) were evaluated for three patients to investigate which sorting method provides higher physiologic accuracy. Results: Anatomic similarity-based sorting reduced 4D CT artifacts compared to phase-based sorting (artifact score, 0.45 ± 0.14 vs 0.58 ± 0.24, p = 0.10 at peak-exhale; 0.63 ± 0.19 vs 0.71 ± 0.31, p = 0.25 at peak-inhale). The voxel-based correlation between the two ventilation images was 0.69 ± 0.26 on average, ranging from 0.03 to 0.85. The DSC was 0.71 ± 0.13 on average. Anatomic similarity-based sorting yielded significantly fewer lung voxels with paradoxical negative ventilation values than phase-based sorting (5.0 ± 2.6% vs 9.7 ± 8.4%, p = 0.05), and improved the correlation with SPECT ventilation regionally. Conclusions: The variability of 4D CT ventilation imaging due to 4D CT sorting was moderate overall and substantial in some cases, suggesting that 4D CT artifacts are an important source of variations in 4D CT ventilation imaging. Reduction of 4D CT artifacts provided more physiologically convincing and accurate ventilation estimates. Further studies are needed to confirm this result.

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Evaluation of 4D-CT Lung Registration

Cited by 56 publications

References 11 publications

CT ventilation functional image-based IMRT treatment plans are comparable to SPECT ventilation functional image-based plans

CT ventilation functional image-based IMRT treatment plans are comparable to SPECT ventilation functional image-based plans

Lung CT registration combining intensity, curves and surfaces

4D CT lung ventilation images are affected by the 4D CT sorting method

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