Diffuse optical tomography to monitor the photocoagulation front during interstitial photothermal therapy: Numerical simulations and measurements in tissue-simulating phantoms

He, Jie; Wilson, Brian C.; Piao, Daqing; Weersink, Robert A.

doi:10.1515/plm-2014-0011

Cited by 6 publications

(12 citation statements)

References 37 publications

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“…We previously used the transrectal probe DOT system of Piao et al 17 to measure tissue-simulating phantoms containing different sizes of highly scattering cylindrical inclusions, as well as numerical simulations using finite element calculations based on the diffusion equation. 16 This study demonstrated the sensitivity of the DOT signals to the diameter of the inclusion and, hence, of the distance between the nearest inclusion boundary and the DOT source-detector array. Multiple DOT source-detector configurations were simulated to determine the geometries with the highest location sensitivity.…”

Section: Introductionmentioning

confidence: 72%

“…We are developing a transrectal diffuse optical tomography (DOT) system as a method for assessing the location and size of the photothermal lesion(s) in near-real time, based primarily on changes in light scattering that are associated with coagulation. 15,16 The clinical objective is to track the location of the boundary of the growing coagulation zone, which is typically sharply delineated. The posterior border of the coagulation zone is particularly important because of the risk of damage to the rectal wall; thus, we have set an accuracy of AE1 mm for localization of this border, based on clinical relevance.…”

Section: Introductionmentioning

confidence: 99%

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Shape-based reconstruction for transrectal diffuse optical tomography monitoring of photothermal focal therapy of prostate cancer: simulation studies

et al. 2017

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We develop and demonstrate a simple shape-based approach for diffuse optical tomographic reconstruction of coagulative lesions generated during interstitial photothermal therapy (PTT) of the prostate. The shape-based reconstruction assumes a simple ellipsoid shape, matching the general dimensions of a cylindrical diffusing fiber used for light delivery in current clinical studies of PTT in focal prostate cancer. The specific requirement is to accurately define the border between the photothermal lesion and native tissue as the photothermal lesion grows, with an accuracy of ? 1 ?? mm , so treatment can be terminated before there is damage to the rectal wall. To demonstrate the feasibility of the shape-based diffuse optical tomography reconstruction, simulated data were generated based on forward calculations in known geometries that include the prostate, rectum, and lesions of varying dimensions. The only source of optical contrast between the lesion and prostate was increased scattering in the lesion, as is typically observed with coagulation. With noise added to these forward calculations, lesion dimensions were reconstructed using the shape-based method. This approach for reconstruction is shown to be feasible and sufficiently accurate for lesions that are within 4 mm from the rectal wall. The method was also robust for irregularly shaped lesions.

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Section: Introductionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Shape-based reconstruction for transrectal diffuse optical tomography monitoring of photothermal focal therapy of prostate cancer: simulation studies

et al. 2017

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“…3(b) shows a cross section of the phantom CT image obtained from the dedicated breast cancer CT scanner. As described in the numerical simulation, we have performed the DOT reconstruction of this phantom experiment without the structural prior, with the structural prior through the method of soft prior, and with the structural guidance by the proposed kernel method of 12 different cases with 3 different nearest neighbor k (16,32,64) and 4 different voxel numbers (3×3×3, 5×5×5, 7×7×7, and 9×9×9). The reconstructed optical absorption coefficient images are plotted in Fig.…”

Section: Phantom Experimental Resultsmentioning

confidence: 99%

“…The lengths of the feature vector were 27, 125, 343, and 729, respectively. For knnsearch, three different values of k (16,32,64), the number of nearest neighbors, were also studied. To evaluate and compare the quality of the reconstructed DOT images quantitatively, we used a combination of 4 metrics listed below.…”

Section: Optimization Of the Kernel Methodsmentioning

confidence: 99%

“…To find k similar neighbors for each feature vector, a knn search was carried out using the knnsearch function in MATLAB according to Euclidean distance between the feature vectors, not the physical distance among the finite element nodes in the Cartesian coordinates. In this study, three different values of k (16,32,64), the number of nearest neighbors, were studied. Only those elements corresponding to the k-nearest neighbors were kept in the kernel matrix and the rest of them were set to be 0.…”

Section: Kernel-based Anatomically-aided Reconstruction Algorithmmentioning

confidence: 99%

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Kernel-based anatomically-aided diffuse optical tomography reconstruction

Baikejiang¹,

Zhang²,

Zhu³

et al. 2017

Biomed. Phys. Eng. Express

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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.

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