ArticleMedical physics2012
CT-PET weighted image fusion for separately scanned whole body rat.
Article in Medical physics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 22 citations in OpenAlex.
- Biomechanical model for computing deformations for whole-body image registration: A meshless approach.International journal for numerical methods in biomedical engineering · 2016Article
- Evaluation of GMI and PMI diffeomorphic-based demons algorithms for aligning PET and CT Images.Journal of applied clinical medical physics · 2015Article
- Patient-specific biomechanical model as whole-body CT image registration tool.Medical image analysis · 2015Article
- Registration of PET and CT images based on multiresolution gradient of mutual information demons algorithm for positioning esophageal cancer patients.Journal of applied clinical medical physics · 2013Article
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6 authors at 3 institutions in 1 country.
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Abstract
purposeThe limited resolution and lack of spatial information in positron emission tomography (PET) images require the complementary anatomic information from the computed tomography (CT) and/or magnetic resonance imaging (MRI). Therefore, multimodality image fusion techniques such as PET/CT are critical in mapping the functional images to structural images and thus facilitate the interpretation of PET studies. In our experimental situation, the CT and PET images are acquired in separate scanners at different times and the inherent differences in the imaging protocols produce significant nonrigid changes between the two acquisitions in addition to dissimilar image characteristics. The registration conditions are also poor because CT images have artifacts due to the limitation of current scanning settings, while PET images are very blurry (in transmission-PET) and have vague anatomical structure boundaries (in emission-PET).
methodsThe authors present a new method for whole body small animal multimodal registration. In particular, the authors register whole body rat CT image and PET images using a weighted demons algorithm. The authors use both the transmission-PET and the emission-PET images in the registration process emphasizing particular regions of the moving transmission-PET image using the emission-PET image. After a rigid transformation and a histogram matching between the CT and the transmission-PET images, the authors deformably register the transmission-PET image to the CT image with weights based on the intensity-normalized emission-PET image. For the deformable registration process, the authors develop a weighted demons registration method that can give preferences to particular regions of the input image using a weight image.
resultsThe authors validate the results with nine rat image sets using the M-Hausdorff distance (M-HD) similarity measure with different outlier-suppression parameters (OSP). In comparison with standard methods such as the regular demons and the normalized mutual information (NMI)-based nonrigid free-form deformation (FFD) registration, the proposed weighted demons registration method shows average M-HD errors: 3.99 ± 1.37 (OSP = 10), 5.04 ± 1.59 (OSP = 20) and 5.92 ± 1.61 (OSP = ∞) with statistical significance (p < 0.0003) respectively, while NMI-based nonrigid FFD has average M-HD errors: 5.74 ± 1.73 (OSP = 10), 7.40 ± 7.84 (OSP = 20) and 9.83 ± 4.13 (OSP = ∞), and the regular demons has average M-HD errors: 6.79 ± 0.83 (OSP = 10), 9.19 ± 2.39 (OSP = 20) and 11.63 ± 3.99 (OSP = ∞), respectively. In addition to M-HD comparisons, the visual comparisons on the faint-edged region between the CT and the aligned PET images also show the encouraging improvements over the other methods.
conclusionsIn the whole body multimodal registration between CT and PET images, the utilization of both the transmission-PET and the emission-PET images in the registration process by emphasizing particular regions of the transmission-PET image using an emission-PET image is effective. This method holds promise for other image fusion applications where multiple (more than two) input images should be registered into a single informative image.
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