Deshan Yang

Exploring feature-based approaches in PET images for predicting cancer treatment outcomes

Accumulating evidence suggests that characteristics of pre-treatment FDG-PET could be used as prognostic factors to predict outcomes in different cancer sites. Current risk analyses are limited to visual assessment or direct uptake value measurements. We are investigating intensity-volume histogram metrics and shape and texture features extracted from PET images to predict patients response to treatment. These approaches were demonstrated using datasets from cervix and head and neck cancers, where AUC of 0.76 and 1.0 were achieved, respectively. The preliminary results suggest that the proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.

Implementation and evaluation of various demons deformable image registration algorithms on a GPU

Online adaptive radiation therapy (ART) promises the ability to deliver an optimal treatment in response to daily patient anatomic variation. A major technical barrier for the clinical implementation of online ART is the requirement of rapid image segmentation. Deformable image registration (DIR) has been used as an automated segmentation method to transfer tumor/organ contours from the planning image to daily images. However, the current computational time of DIR is insufficient for online ART. In this work, this issue is addressed by using computer graphics processing units (GPUs). A gray-scale-based DIR algorithm called demons and five of its variants were implemented on GPUs using the compute unified device architecture (CUDA) programming environment. The spatial accuracy of these algorithms was evaluated over five sets of pulmonary 4D CT images with an average size of 256 x 256 x 100 and more than 1100 expert-determined landmark point pairs each. For all the testing scenarios presented in this paper, the GPU-based DIR computation required around 7 to 11 s to yield an average 3D error ranging from 1.5 to 1.8 mm. It is interesting to find out that the original passive force demons algorithms outperform subsequently proposed variants based on the combination of accuracy, efficiency and ease of implementation.

Expanding the Bioheat Equation to Include Tissue Internal Water Evaporation During Heating

We propose a new method to study high temperature tissue ablation using an expanded bioheat diffusion equation. An extra term added to the bioheat equation is combined with the specific heat into an effective (temperature dependent) specific heat. It replaces the normal specific heat term in the modified bioheat equation, which can then be used at temperatures where water evaporation is expected to occur. This new equation is used to numerically simulate the microwave ablation of bovine liver and is compared to experimental ex vivo results.

Measurement and Analysis of Tissue Temperature During Microwave Liver Ablation

We measured tissue temperature changes during ex vivo microwave ablation (MWA) procedures for bovine liver tissue. Tissue temperature increased rapidly at the beginning of the MW power application. It came to a plateau at 100 degC to 104 degC before it increased again. We split the changes of tissue temperature versus time into four phases. This suggests that tissue temperature changes may be directly related to tissue water related phenomena during MWA, including evaporation, diffusion, condensation and tissue water composition. An additional analysis indicated the lesion boundary at ~50 degC to 60 degC temperature. We also measured the water content of ablated tissue lesions and examined the relationship of tissue water content and tissue temperature by mapping temperature to remaining tissue water after ablation. The results demonstrate significant tissue water content changes and lead to a better understanding of tissue water movement

A floating sleeve antenna yields localized hepatic microwave ablation

We report a novel coaxial antenna for hepatic microwave ablation. This device uses a floating sleeve, that is, a metal conductor electrically isolated from the outer connector of the antenna coaxial body, to achieve a highly localized specific absorption rate pattern that is independent of insertion depth. This floating sleeve coaxial dipole antenna has low power reflection in the 2.4-GHz IMS band. Ex vivo experiments confirm our numerical simulation results.

Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning

Multimodality imaging information is regularly used now in radiotherapy treatment planning for cancer patients. The authors are investigating methods to take advantage of all the imaging information available for joint target registration and segmentation, including multimodality images or multiple image sets from the same modality. In particular, the authors have developed variational methods based on multivalued level set deformable models for simultaneous 2D or 3D segmentation of multimodality images consisting of combinations of coregistered PET, CT, or MR data sets. The combined information is integrated to define the overall biophysical structure volume. The authors demonstrate the methods on three patient data sets, including a nonsmall cell lung cancer case with PET/CT, a cervix cancer case with PET/CT, and a prostate patient case with CT and MRI. CT, PET, and MR phantom data were also used for quantitative validation of the proposed multimodality segmentation approach. The corresponding Dice similarity coefficient (DSC) was 0.90±0.02(p<0.0001) with an estimated target volume error of 1.28±1.23% volume. Preliminary results indicate that concurrent multimodality segmentation methods can provide a feasible and accurate framework for combining imaging data from different modalities and are potentially useful tools for the delineation of biophysical structure volumes in radiotherapy treatment planning.

4D‐CT motion estimation using deformable image registration and 5D respiratory motion modeling

Four-dimensional computed tomography (4D-CT) imaging technology has been developed for radiation therapy to provide tumor and organ images at the different breathing phases. In this work, a procedure is proposed for estimating and modeling the respiratory motion field from acquired 4D-CT imaging data and predicting tissue motion at the different breathing phases. The 4D-CT image data consist of series of multislice CT volume segments acquired in ciné mode. A modified optical flow deformable image registration algorithm is used to compute the image motion from the CT segments to a common full volume 3D-CT reference. This reference volume is reconstructed using the acquired 4D-CT data at the end-of-exhalation phase. The segments are optimally aligned to the reference volume according to a proposed a priori alignment procedure. The registration is applied using a multigrid approach and a feature-preserving image downsampling maxfilter to achieve better computational speed and higher registration accuracy. The registration accuracy is about 1.1 +/- 0.8 mm for the lung region according to our verification using manually selected landmarks and artificially deformed CT volumes. The estimated motion fields are fitted to two 5D (spatial 3D+tidal volume+airflow rate) motion models: forward model and inverse model. The forward model predicts tissue movements and the inverse model predicts CT density changes as a function of tidal volume and airflow rate. A leave-one-out procedure is used to validate these motion models. The estimated modeling prediction errors are about 0.3 mm for the forward model and 0.4 mm for the inverse model.

A fast inverse consistent deformable image registration method based on symmetric optical flow computation

Deformable image registration is widely used in various radiation therapy applications including 4D-CT and treatment planning adaptation. In this work, a simple and efficient inverse consistency deformable registration method is proposed with aims of higher registration accuracy and faster convergence speed. Instead of registering image I to the second image J, two images are symmetrically deformed toward one another in multiple passes, until both deformed images are registered. In every pass, a delta motion field is computed by minimizing a symmetric optical flow system cost function using the modified optical flow algorithms. The images are then further deformed with the delta motion field in positive and negative directions, respectively, and then used for the next pass. The magnitude of the delta motion field is forced to be less than 0.4 voxel for every pass in order to guarantee the smoothness and invertibility of the two overall motion fields which are accumulating the delta motion fields in positive and negative directions, respectively. The final motion fields to register the original images I and J, in either direction, are calculated by inverting one overall motion field and composing the inversion result with the other overall motion field. The final motion fields are inversely consistent and this is ensured by the symmetric way that registration is carried out. Results suggest that the method is able to improve the overall accuracy by 30% or more, reduce the inverse consistency error, and increase the convergence rate. The computation speed may slightly decrease, or increase in some cases because the new method converges faster. Comparing to previously published inverse consistency algorithms, the proposed method is simpler in theory, easier to implement, and faster.

Technical Note: DIRART- A software suite for deformable image registration and adaptive radiotherapy research

Purpose: Recent years have witnessed tremendous progress in image guide radiotherapy technology and a growing interest in the possibilities for adapting treatment planning and delivery over the course of treatment. One obstacle faced by the research community has been the lack of a comprehensive open-source software toolkit dedicated for adaptive radiotherapy (ART). To address this need, the authors have developed a software suite called the Deformable Image Registration and Adaptive Radiotherapy Toolkit (DIRART). Methods: DIRART is an open-source toolkit developed in MATLAB. It is designed in an object-oriented style with focus on user-friendliness, features, and flexibility. It contains four classes of DIR algorithms, including the newer inverse consistency algorithms to provide consistent displacement vector field in both directions. It also contains common ART functions, an integrated graphical user interface, a variety of visualization and image-processing features, dose metric analysis functions, and interface routines. These interface routines make DIRART a powerful complement to the Computational Environment for Radiotherapy Research (CERR) and popular image-processing toolkits such as ITK. Results :DIRART provides a set of image processing/registration algorithms and postprocessing functions to facilitate the development and testing of DIR algorithms. It also offers a good amount of options for DIR results visualization, evaluation, and validation. Conclusions : By exchanging data with treatment planning systems via DICOM-RT files andCERR, and by bringing image registration algorithms closer to radiotherapy applications, DIRART is potentially a convenient and flexible platform that may facilitate ART and DIR research.

Contribution of direct heating, thermal conduction and perfusion during radiofrequency and microwave ablation.

Heat based tumor ablation methods such as radiofrequency (RF) and microwave (MW) ablation are increasingly accepted treatment methods for tumors not treatable by traditional surgery. Typically, an interstitial applicator is introduced under imaging guidance into the tumor, and tissue is destroyed by heating to above -50degC, with maximum tissue temperatures over 100degC. Since high thermal gradients occur during the procedure, thermal conduction contributes significantly towards tissue heating. We created finite element method (FEM) computer models of RF and MW applicators, and determined the thermal conduction term, the resistive (for RF) or dielectric (for MW) loss term, and perfusion term. We integrated these terms over the heating period to obtain relative contribution towards tissue temperature rise (indegC) as a function of distance from the applicator. We performed simulations without and with perfusion, where perfusion was assumed to stop above 50degC. During the first 6 minutes, direct heating by RF and MW were dominating throughout the tissue. Over the treatment period (12 min for RF, and 6 min for MW), thermal conduction was dominating at distances between than 12 and 19 mm from the RF electrode, while for MW ablation direct heating dominated everywhere. Even though thermal conduction significantly contributes towards tissue heating during ablative therapies, direct heating by RF or MW is dominating throughout most of the tissue volume. Tissue cooling due to perfusion is more significant during RF heating, in part due to the longer treatment times