CT-PET weighted image fusion for separately scanned whole body rat

Suh, Jung Wook; Kwon, Oh-Kyu; Scheinost, Dustin; Sinusas, Albert J.; Cline, Gary W.; Papademetris, Xenophon

doi:10.1118/1.3672167

Cited by 20 publications

(14 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Analysis of Hausdorff Distance (HD) percentile values indicates that for Case I and Case II, the average HD (as presented in Suh et al (2012)) between the edges in the registered and target images was less than the original (i.e. before resampling) CT image voxel size of 5 mm (Table 1).…”

Section: Resultsmentioning

confidence: 99%

“…For all the analysed image sets, the average Hausdorff distance between the pairs edges in registered and target images was within two times the voxel size, which compares well with the studies on non-rigid registration of whole-body CTs relying solely on image-processing algorithms. For instance, Suh et al (2012) reported the maximum-likelihood HD (M-HD) of an order of 4 image voxels in their study for rat whole-body CT non-rigid registration using a weighted demons algorithm. Similarly, Toews and Wells (2013) observed errors of an order of 6 image voxel when applying their feature-based alignment (FBA) method for inter-subject registration of human whole-body CT images.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Patient-specific biomechanical model as whole-body CT image registration tool

Miller

Joldes

et al. 2015

Medical Image Analysis

View full text Add to dashboard Cite

Whole-body computed tomography (CT) image registration is important for cancer diagnosis, therapy planning and treatment. Such registration requires accounting for large differences between source and target images caused by deformations of soft organs/tissues and articulated motion of skeletal structures. The registration algorithms relying solely on image processing methods exhibit deficiencies in accounting for such deformations and motion. We propose to predict the deformations and movements of body organs/tissues and skeletal structures for whole-body CT image registration using patient-specific non-linear biomechanical modelling. Unlike the conventional biomechanical modelling, our approach for building the biomechanical models does not require time-consuming segmentation of CT scans to divide the whole body into non-overlapping constituents with different material properties. Instead, a Fuzzy C-Means (FCM) algorithm is used for tissue classification to assign the constitutive properties automatically at integration points of the computation grid. We use only very simple segmentation of the spine when determining vertebrae displacements to define loading for biomechanical models. We demonstrate the feasibility and accuracy of our approach on CT images of seven patients suffering from cancer and aortic disease. The results confirm that accurate whole-body CT image registration can be achieved using a patient-specific non-linear biomechanical model constructed without time-consuming segmentation of the whole-body images.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Patient-specific biomechanical model as whole-body CT image registration tool

Miller

Joldes

et al. 2015

Medical Image Analysis

View full text Add to dashboard Cite

show abstract

“…An accurate deformable registration framework for PET and CT alignment can be used for accurate positioning tumor target in radiotherapy treatment planning. Precise definition of tumor target can largely reduce toxicity to surrounding organs and tissues and ensure sufficient dosage to the tumor target in treatment planning therapy (14) . Currently, in 3D image‐guide radiation therapy (IGRT) GTVs are usually manually delineated by oncologists in treatment planning system on the planning day.…”

Section: Discussionmentioning

confidence: 99%

“…In this paper, we used a three‐dimensional version of modified Hausdorff distance using Canny edge‐detector with a Gaussian smoothing kernel with

σ = 2

. This automated edge‐detection algorithm has been successfully used by Suh et al, (14) who indicated that the modified Hausdorff distance similarity measure was robust in the presence of outliers and occlusions, and can be used to compare the similarity of PET‐CT images, even though the edges are not fully generated from the PET images. So no extra human observer was enrolled to check their consistency.…”

Section: Methodsmentioning

confidence: 99%

“…Castillo et al (13) presented a new 3D elastic transformation algorithm based on normalized mutual information (NMI) to automatically align PET and CT images, which showed better performance compared with rigid transformation. Suh et al (14) proposed a novel deformable registration method based on a weighted demons algorithm to register whole‐body rat CT and PET images, and the maximum likelihood Hausdorff distance was used as a similarity measure. Preliminary results demonstrated the high efficiency of this algorithm compared with traditional demons algorithm and NMI‐based nonrigid free‐form deformation (FFD) method.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of GMI and PMI diffeomorphic‐based demons algorithms for aligning PET and CT Images

Yang

Wang

You

et al. 2015

J Applied Clin Med Phys

View full text Add to dashboard Cite

Fusion of anatomic information in computed tomography (CT) and functional information in normalF18‐FDG positron emission tomography (PET) is crucial for accurate differentiation of tumor from benign masses, designing radiotherapy treatment plan and staging of cancer. Although current PET and CT images can be acquired from combined normalF18‐FDG PET/CT scanner, the two acquisitions are scanned separately and take a long time, which may induce potential positional errors in global and local caused by respiratory motion or organ peristalsis. So registration (alignment) of whole‐body PET and CT images is a prerequisite for their meaningful fusion. The purpose of this study was to assess the performance of two multimodal registration algorithms for aligning PET and CT images. The proposed gradient of mutual information (GMI)‐based demons algorithm, which incorporated the GMI between two images as an external force to facilitate the alignment, was compared with the point‐wise mutual information (PMI) diffeomorphic‐based demons algorithm whose external force was modified by replacing the image intensity difference in diffeomorphic demons algorithm with the PMI to make it appropriate for multimodal image registration. Eight patients with esophageal cancer(s) were enrolled in this IRB‐approved study. Whole‐body PET and CT images were acquired from a combined normalF18‐FDG PET/CT scanner for each patient. The modified Hausdorff distance (dMH) was used to evaluate the registration accuracy of the two algorithms. Of all patients, the mean values and standard deviations (SDs) of dMH were 6.65 false(± 1.90false) voxels and 6.01 false(± 1.90false) after the GMI‐based demons and the PMI diffeomorphic‐based demons registration algorithms respectively. Preliminary results on oncological patients showed that the respiratory motion and organ peristalsis in PET/CT esophageal images could not be neglected, although a combined normalF18‐FDG PET/CT scanner was used for image acquisition. The PMI diffeomorphic‐based demons algorithm was more accurate than the GMI‐based demons algorithm in registering PET/CT esophageal images.PACS numbers: 87.57.nj, 87.57. Q‐, 87.57.uk

show abstract

Biomechanical model for computing deformations for whole‐body image registration: A meshless approach

Mao

Miller

Joldes

et al. 2016

Numer Methods Biomed Eng

View full text Add to dashboard Cite

Patient-specific biomechanical models have been advocated as a tool for predicting deformations of soft body organs/tissue for medical image registration (aligning two sets of images) when differences between the images are large. However, complex and irregular geometry of the body organs makes generation of patient-specific biomechanical models very time consuming. Meshless discretisation has been proposed to solve this challenge. However, applications so far have been limited to 2-D models and computing single organ deformations. In this study, 3-D comprehensive patient-specific non-linear biomechanical models implemented using Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithms are applied to predict a 3-D deformation field for whole-body image registration. Unlike a conventional approach which requires dividing (segmenting) the image into non-overlapping constituents representing different organs/tissues, the mechanical properties are assigned using the Fuzzy C-Means (FCM) algorithm without the image segmentation. Verification indicates that the deformations predicted using the proposed meshless approach are for practical purposes the same as those obtained using the previously validated finite element models. To quantitatively evaluate the accuracy of the predicted deformations, we determined the spatial misalignment between the registered (i.e. source images warped using the predicted deformations) and target images by computing the edge-based Hausdorff distance. The Hausdorff distance-based evaluation determines that our meshless models led to successful registration of the vast majority of the image features.

show abstract

CT-PET weighted image fusion for separately scanned whole body rat

Cited by 20 publications

References 26 publications

Patient-specific biomechanical model as whole-body CT image registration tool

Patient-specific biomechanical model as whole-body CT image registration tool

Evaluation of GMI and PMI diffeomorphic‐based demons algorithms for aligning PET and CT Images

Biomechanical model for computing deformations for whole‐body image registration: A meshless approach

Contact Info

Product

Resources

About