Bladder cancer is the fourth most common cancer in men and is considered to have the highest rate of recurrence of all cancers at ~70%, and transitional cell carcinoma (TCC) is the most common form of intrabladder malignancy. Current standard-of-care for Stages 2 or higher is radical cystectomy, which involves removal of the urinary bladder and nearby lymph nodes. Alternative, organ-sparing treatments such as chemo- or radiotherapy are relatively ineffective against these cancers. The latter is effective when precisely targeted, but suffers from accuracy issues due to low contrast from computed tomography guidance. These motivate an innovative approach to more precisely visualize and spatially pinpoint TCC. This manuscript presents a novel non-invasive computer vision pipeline that can extract 3D structural information from 2D images obtained during routine flexible cystoscopy. The pipeline utilized camera calibration, adaptive thresholding, Scale Invariant Feature Transform (SIFT), and a Structure from Motion (SFM) implementation to reconstruct 3D point clouds of the inner surface of organ phantoms and an ex vivo porcine bladder. 3D point clouds were processed by Poisson reconstruction to generate a textured, triangle meshed 3D surface. The reconstruction pipeline generated a visually recognizable, qualitative 3D representation of the bladder from 2D video captured via flexible cystoscopy. Once further developed, this approach will enhance the targeting precision of external beam radiotherapy, providing clinicians with better organ-sparing methods to treat TCC.
This paper describes a new approach for reconstructing images from a finite number of projections. The rayintegrals
of the image f(x, y) are transformed uniquely into the ray-sums of the discrete image fn,m on the
Cartesian lattice. This transformation allows for calculating the tensor representation of the discrete image, when
the image is considered as the sum of direction images, or splitting-signals carrying the spectral information of
the image at frequency-points of different subsets that cover the Cartesian lattice. These subsets are intersected
and this property of redundancy is used to reduce the angular range of projections. The proposed approach is
presented for parallel projections and the continuous model. Preliminary results show very good results of image reconstruction when the angular range scanned is 27° and down to 10°.
The reconstruction of the image f(x, y) is from a finite number of projections on the discrete Cartesian lattice N × N is described. The reconstruction is exact in the framework of the model, when image is considered as the set of N2 cells, or image elements with constant intensity each. Such reconstruction is achieved because of the following two facts. Each basis function of the tensor transformation is determined by the set of parallel rays, and, therefore, the components of the tensor transform can be calculated by ray-sums. These sums can be determined from the ray-integrals, and we introduce here the concept of geometrical, or G-rays to solve this problem. The examples of image reconstruction by the proposed method are given, and the reconstruction on the Cartesian lattice 7 × 7 is described in detail.
In this paper, we present a model-based predictive control system that is capable of capturing physical and biological variations of laser-tissue interaction as well as heterogeneity in real-time during laser induced thermal therapy (LITT). Using a three-dimensional predictive bioheat transfer model, which is built based on regular
magnetic resonance imaging (MRI) anatomic scan and driven by imaging data produced by real-time magnetic resonance temperature imaging (MRTI), the computational system provides a regirous real-time predictive control during surgical operation process. The unique feature of the this system is its ability for predictive control
based on validated model with high precision in real-time, which is made possible by implementation of efficient
parallel algorithms. The major components of the current computational systems involves real-time finite element
solution of the bioheat transfer induced by laser-tissue interaction, solution module of real-time calibration
problem, optimal laser source control, goal-oriented error estimation applied to the bioheat transfer equation,
and state-of-the-art imaging process module to characterize the heterogeneous biological domain. The system
was tested in vivo in a canine animal model in which an interstitial laser probe was placed in the prostate region
and the desired treatment outcome in terms of ablation temperature and damage zone were achieved. Using the
guidance of the predictive model driven by real-time MRTI data while applying the optimized laser heat source
has the potential to provide unprecedented control over the treatment outcome for laser ablation.
Thermal therapy efficacy can be diminished due to heat shock protein (HSP) induction in regions of a tumor where temperatures are insufficient to coagulate proteins. HSP expression enhances tumor cell viability and imparts resistance to chemotherapy and radiation treatments, which are generally employed in conjunction with hyperthermia. Therefore, an understanding of the thermally induced HSP expression within the targeted tumor must be incorporated into the treatment plan to optimize the thermal dose delivery and permit prediction of the overall tissue response. A treatment planning computational model capable of predicting the temperature, HSP27 and HSP70 expression, and damage fraction distributions associated with laser heating in healthy prostate tissue and tumors is presented. Measured thermally induced HSP27 and HSP70 expression kinetics and injury data for normal and cancerous prostate cells and prostate tumors are employed to create the first HSP expression predictive model and formulate an Arrhenius damage model. The correlation coefficients between measured and model predicted temperature, HSP27, and HSP70 were 0.98, 0.99, and 0.99, respectively, confirming the accuracy of the model. Utilization of the treatment planning model in the design of prostate cancer thermal therapies can enable optimization of the treatment outcome by controlling HSP expression and injury.
Heat shock proteins (HSP) are critical components of a complex defense mechanism essential for preserving cell survival under adverse environmental conditions. It is inevitable that hyperthermia will enhance tumor tissue viability, due to HSP expression in regions where temperatures are insufficient to coagulate proteins, and would likely increase the probability of cancer recurrence. Although hyperthermia therapy is commonly used in conjunction with radiotherapy, chemotherapy, and gene therapy to increase therapeutic effectiveness, the efficacy of these therapies can be substantially hindered due to HSP expression when hyperthermia is applied prior to these procedures. Therefore, in planning hyperthermia protocols, prediction of the HSP response of the tumor must be incorporated into the treatment plan to optimize the thermal dose delivery and permit prediction of overall tissue response. In this paper, we present a highly accurate, adaptive, finite element tumor model capable of predicting the HSP expression distribution and tissue damage region based on measured cellular data when hyperthermia protocols are specified. Cubic spline representations of HSP27 and HSP70, and Arrhenius damage models were integrated into the finite element model to enable prediction of the HSP expression and damage distribution in the tissue following laser heating. Application of the model can enable optimized treatment planning by controlling of the tissue response to therapy based on accurate prediction of the HSP expression and cell damage distribution.
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