PET Image Reconstruction Using Kernel Method

Wang, Guobao; Qi, Jinyi

doi:10.1109/tmi.2014.2343916

Cited by 193 publications

(182 citation statements)

References 47 publications

(56 reference statements)

Supporting

Mentioning

174

Contrasting

Order By: Relevance

“…The first parameter is the Gaussian kernel coefficient . According to previous studies, 38,49 =1 yields best results. The second parameter is the number of nearest neighbor's k. However, for the numerical simulations with the false target size in anatomical images, the kernel method with the smaller number of voxels performed better than the kernel method with the larger number of voxels.…”

Section: Reconstruction With Inhomogeneous Background In Ct Imagesmentioning

confidence: 59%

“…(13) to zero in this study, 38 and solved by the MM approach. 39,40 Once is obtained we can easily obtain the final fluorophore distribution image by the linear transformation = .…”

Section: 47mentioning

confidence: 99%

“…In this paper, inspired by the kernel method in PET image reconstruction, 38 we introduce the kernel-based image reconstruction as a new approach to incorporating anatomical guidance into FMT. Compared with the Laplacian-type regularization methods, the proposed kernel method does not require the target region segmentation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method

et al. 2017

View full text Add to dashboard Cite

Abstract. Fluorescence molecular tomography (FMT) is an important in vivo imaging modality to visualize physiological and pathological processes in small animals. However, FMT reconstruction is ill-posed and illconditioned due to strong optical scattering in deep tissues, which results in poor spatial resolution. It is well known that FMT image quality can be improved substantially by applying the structural guidance in the FMT reconstruction. In this paper, a new approach to introducing anatomical information into the FMT reconstruction is presented using the kernel method. In contrast to conventional methods that incorporate anatomical information with a Laplacian-type regularization matrix, the proposed method introduces the anatomical guidance into the projection model of FMT. The primary advantage of the proposed method is that it does not require segmentation of targets in the anatomical images. Numerical simulations and phantom experiments have been performed to demonstrate the proposed approach's feasibility. Numerical simulation results indicate that the proposed kernel method can separate two FMT targets with an edge-to-edge distance of 1 mm and is robust to false positive guidance and inhomogeneity in the anatomical image. For the phantom experiments with two FMT targets, the kernel method has reconstructed both targets successfully, which further validates the proposed kernel method. We have compared the proposed kernel method with the soft prior method thoroughly and found that the kernel method without target segmentation is able to achieve similar anatomical guided results as the soft prior method.

show abstract

Section: Reconstruction With Inhomogeneous Background In Ct Imagesmentioning

confidence: 59%

“…(13) to zero in this study, 38 and solved by the MM approach. 39,40 Once is obtained we can easily obtain the final fluorophore distribution image by the linear transformation = .…”

Section: 47mentioning

confidence: 99%

See 1 more Smart Citation

Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method

et al. 2017

View full text Add to dashboard Cite

show abstract

“…This can be accomplished by defining a kernel function for each finite element node. The optical absorption coefficient at a node i can then be written as a linear combination of kernels in a way similar to for PET [49,51,52],…”

Section: Kernel-based Anatomically-aided Reconstruction Algorithmmentioning

confidence: 99%

“…In this paper, inspired by the kernel method of PET image reconstruction [49], we introduce the kernel method based image reconstruction as a new approach to include anatomical guidance into DOT reconstruction. Compared with the conventional hard and soft prior approaches, the proposed kernel method does not require target region segmentation.…”

Section: Introductionmentioning

confidence: 99%

Kernel-based anatomically-aided diffuse optical tomography reconstruction

Baikejiang¹,

Zhang²,

Zhu³

et al. 2017

Biomed. Phys. Eng. Express

Self Cite

View full text Add to dashboard Cite

Image reconstruction in diffuse optical tomography (DOT) is a challenging task because its inverse problem is nonlinear, ill-posed and ill-conditioned. Anatomical guidance from imaging modalities with high spatial resolution can substantially improve the quality of reconstructed DOT images. In this paper, inspired by the kernel methods in machine learning, we proposed a kernel method to introduce anatomical guidance into the DOT image reconstruction. In this kernel method, the optical absorption coefficient at each finite element node is represented as a function of a set of features obtained from anatomical images such as computed tomography (CT) images. Compared with Laplacian approaches that include structural priors, the proposed method does not require image segmentation. The proposed kernel method is validated with numerical simulations of 3D DOT reconstruction using synthetic CT data. 5% Gaussian noise was added to both the numerical DOT measurements and the simulated CT image. The proposed method was also validated by an agar phantom experiment with the anatomical guidance from a cone beam CT scan. The effects of voxel size and number of nearest neighborhood size in the kernel method on the reconstructed DOT images were studied. The results indicate that the spatial resolution and the accuracy of the reconstructed DOT images have been improved substantially after applying the anatomical guidance with the proposed kernel method comparing to the case without guidance. Furthermore, we demonstrated that the kernel method was able to utilize clinical breast CT images as anatomical guidance without segmentation. In addition, we found that the proposed kernel method was robust to the false positive guidance in the anatomical image.

show abstract

Two‐scale image decomposition based image fusion using structure tensor

2019

Int J Imaging Syst Tech

View full text Add to dashboard Cite

The PET/SPECT image generates functional information of the human brain. In the field of multimodal medical image fusion, the decomposition scheme of PET/SPECT image in pseudo-color is thought to be negligible. In this article, a two-step model for PET/SPECT image decomposition is proposed to achieve contrast enhancement that inherently captures gradient to distinguish individual edge information from detail information. First, sharp structure is preserved using structure tensor on the intensity component of PET/SPECT image. Second, sharp structure in gray is transformed back into the image in pseudo-color by generalized intensity-hue-saturation, one of the color space methods. The combination of structure tensor and generalized intensity-hue-saturation based fusion technique can preserve not only more intensity information but also functional information content. Finally, we demonstrate the effectiveness of our proposed decomposition method in the context of PET/SPECT image and then make a comparison of fusion result by two-scale image fusion method in terms of the full-reference objective metric structural similarity and no-reference objective metric natural image quality evaluator. K E Y W O R D Sfunctional medical image in pseudo-color decomposition scheme, generalized intensity-huesaturation, multimodal medical image fusion, structure tensor

show abstract

PET Image Reconstruction Using Kernel Method

Cited by 193 publications

References 47 publications

Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method

Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method

Kernel-based anatomically-aided diffuse optical tomography reconstruction

Two‐scale image decomposition based image fusion using structure tensor

Contact Info

Product

Resources

About