Using Cyber-Infrastructure for Dynamic Data Driven Laser Treatment of Cancer

Abstract

Abstract. Hyperthermia based cancer treatments are used to increase the susceptibility of cancerous tissue to subsequent radiation or chemotherapy treatments, and in the case in which a tumor exists as a well-defined region, higher intensity heat sources may be used to ablate the tissue. Utilizing the guidance of real-time treatment data while applying a laser heat source has the potential to provide unprecedented control over the outcome of the treatment process [6,12]. The goals of this work are to provide a working snapshot of the current system architecture developed to provide a real-time finite element solution of the problems of calibration, optimal heat source control, and goal-oriented error estimation applied the equations of bioheat transfer and demonstrate that current finite element technology, parallel computer architecture, peer-to-peer data transfer infrastructure, and thermal imaging modalities are capable of inducing a precise computer controlled temperature field within the biological domain.

Dynamic Data Driven Applications Systems

“…Papers [12][13][14][15] discuss applications related to human health: from personalized pharmaceuticals, to advanced medical intervention and customized medical treatment. In [12] is discussed the onset of a paradigm shift in pharmaceutical and biotechnology research methods, leading towards a future world of personalized medicines, which will be enabled through a foundational framework that will be based on the DDDAS concept.…”

“…In [13] new frameworks and architectures for brain-machine interface communication and control capabilities are developed, using mixture models to enhance rehabilitation of disabled patients. In [14] and [15] are presented new modeling methods and distributed computational frameworks for advanced medical procedures. Paper [14] discusses an image-guided neurosurgery and grid-enabled software environment by integrating real-time acquisition of intra-operative Magnetic Resonance Imaging (I-MRI) with the preoperative MRI, fMRI, and DT-MRI data.…”

“…Paper [14] discusses an image-guided neurosurgery and grid-enabled software environment by integrating real-time acquisition of intra-operative Magnetic Resonance Imaging (I-MRI) with the preoperative MRI, fMRI, and DT-MRI data. Paper [15] discusses work on novel advances for cancer tumor treatment by utilizing real-time imaging data to provide dynamic control of a laser heat source targeted to ablate only the tumor-tissue, and at the same time imparting minimal damage to the tumor surrounding area.…”

Real-Time Predictive Surgical Control for Cancer Treatment Using Laser Ablation [Life Science]

“…The optimization problem with PDE constrain defined in each phase of the treatment protocol was accompanied by a short period of dead time to allow for computations to reach a solution. Obviously, the methodology inherent to the control system will rely critically on the precise real-time orchestration (see Figure 1) of dynamic imaging data acquisition, high-speed data transfer, finite element mesh generation, model coregistration, inverse analysis and optimization, visualization, optimal laser control, and large scale parallel computing algorithms [4]. Nonetheless, the first step is to generate a threedimensional (3-D) computational model based on MRI images.…”