Dual-Modality PET-CT Visualization using Real-Time Volume Rendering and Image Fusion with Interactive 3D Segmentation of Anatomical Structures

SummaryAim: To retrospectively evaluate the feasibility and value of CT-CT image fusion to assess the shift of peripheral lung cancers with/-out chest wall infiltration, comparing computed tomography acquisitions in shallow-breathing (SB-CT) and deep-inspiration breath-hold (DIBH-CT) in patients undergoing FDG-PET/ CT for lung cancer staging. Methods: Image fusion of SB-CT and DIBH-CT was performed with a multimodal workstation used for nuclear medicine fusion imaging. The distance of intrathoracic landmarks and the positional shift of tumours were measured using semitransparent overlay of both CT series. Statistical analyses were adjusted for confounders of tumour infiltration. Cutoff levels were calculated for prediction of no-/infiltration. Results: Lateral pleural recessus and diaphragm showed the largest respiratory excursions. Infiltrating lung cancers showed more limited respiratory shifts than non-infiltrating tumours. A large respiratory tumour-motility accurately predicted non-infiltration. However, the tumour shifts were limited and variable, limiting the accuracy of prediction. Conclusion: This pilot fusion study proved feasible and allowed a simple analysis of the respiratory shifts of peripheral lung tumours using CT-CT image fusion in a PET/CT setting. The calculated cutoffs were useful in predicting the exclusion of chest wall infiltration but did not accurately predict tumour infiltration. This method can provide additional qualitative information in patients with lung cancers with contact to the chest wall but unclear CT evidence of infiltration undergoing PET/CT without the need of additional investigations. Considering the small sample size investigated, further studies are necessary to verify the obtained results.

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A Novel Adaptive PET/CT Image Fusion Algorithm

Current Bioinformatics ◽

10.2174/1574893613666180704153946 ◽

2019 ◽

Vol 14 (7) ◽

pp. 658-666

Author(s):

Kai-jian Xia ◽

Jian-qiang Wang ◽

Jian Cai

Keyword(s):

Lung Cancer ◽

Image Fusion ◽

High Frequency ◽

Malignant Tumors ◽

Low Frequency ◽

Combination Method ◽

Ct Image ◽

Fusion Algorithm ◽

Pet Ct ◽

Frequency Components

Background: Lung cancer is one of the common malignant tumors. The successful diagnosis of lung cancer depends on the accuracy of the image obtained from medical imaging modalities. Objective: The fusion of CT and PET is combining the complimentary and redundant information both images and can increase the ease of perception. Since the existing fusion method sare not perfect enough, and the fusion effect remains to be improved, the paper proposes a novel method called adaptive PET/CT fusion for lung cancer in Piella framework. Methods: This algorithm firstly adopted the DTCWT to decompose the PET and CT images into different components, respectively. In accordance with the characteristics of low-frequency and high-frequency components and the features of PET and CT image, 5 membership functions are used as a combination method so as to determine the fusion weight for low-frequency components. In order to fuse different high-frequency components, we select the energy difference of decomposition coefficients as the match measure, and the local energy as the activity measure; in addition, the decision factor is also determined for the high-frequency components. Results: The proposed method is compared with some of the pixel-level spatial domain image fusion algorithms. The experimental results show that our proposed algorithm is feasible and effective. Conclusion: Our proposed algorithm can better retain and protrude the lesions edge information and the texture information of lesions in the image fusion.

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