The Feasibly of Functionally Guided Intensity Modulated Radiation Therapy Treatment Planning Using Perfluoropropane-Enhanced MRI for Lung Cancer

Radiotherapy is commonly used to treat lung cancer. However, radiation induced damage to lung tissue is a major limiting factor to its use. To minimize normal tissue lung toxicity from conformal radiotherapy treatment planning, we investigated the use of Perfluoropropane(PFP)-enhanced MR imaging to assess and guide the sparing of functioning lung. Fluorine Enhanced MRI using Perfluoropropane(PFP) is a dynamic multi-breath steady state technique enabling quantitative and qualitative assessments of lung function(1).

Imaging data was obtained from studies previously acquired in the Duke Image Analysis Laboratory. All studies were approved by the Duke IRB. The data was de-identified for this project, which was also approved by the Duke IRB. Subjects performed several breath-holds at total lung capacity(TLC) interspersed with multiple tidal breaths(TB) of Perfluoropropane(PFP)/oxygen mixture. Additive wash-in intensity images were created through the summation of the wash-in phase breath-holds. Additionally, model based fitting was utilized to create parametric images of lung function(1).

Varian Eclipse treatment planning software was used for putative treatment planning. For each subject two plans were made, a standard plan, with no regional functional lung information considered other than current standard models. Another was created using functional information to spare functional lung while maintaining dose to the target lesion. Plans were optimized to a prescription dose of 60 Gy to the target over the course of 30 fractions.

A decrease in dose to functioning lung was observed when utilizing this functional information compared to the standard plan for all five subjects. PFP-enhanced MR imaging is a feasible method to assess ventilatory lung function and we have shown how this can be incorporated into treatment planning to potentially decrease the dose to normal tissue.

Task-driven adaptive statistical compressive sensing of gaussian mixture models

A framework for adaptive and non-adaptive statistical compressive sensing is developed, where a statistical model replaces the standard sparsity model of classical compressive sensing. We propose within this framework optimal task-specific sensing protocols specifically and jointly designed for classification and reconstruction. A two-step adaptive sensing paradigm is developed, where online sensing is applied to detect the signal class in the first step, followed by a reconstruction step adapted to the detected class and the observed samples. The approach is based on information theory, here tailored for Gaussian mixture models (GMMs), where an information-theoretic objective relationship between the sensed signals and a representation of the specific task of interest is maximized. Experimental results using synthetic signals, Landsat satellite attributes, and natural images of different sizes and with different noise levels show the improvements achieved using the proposed framework when compared to more standard sensing protocols. The underlying formulation can be applied beyond GMMs, at the price of higher mathematical and computational complexity. © 1991-2012 IEEE.

Pre-Clinical Models of Diffuse Intrinsic Pontine Glioma.

Diffuse intrinsic pontine glioma (DIPG) is a rare and incurable brain tumor that arises in the brainstem of children predominantly between the ages of 6 and 8. Its intricate morphology and involvement of normal pons tissue precludes surgical resection, and the standard of care today remains fractionated radiation alone. In the past 30 years, there have been no significant advances made in the treatment of DIPG. This is largely because we lack good models of DIPG and therefore have little biological basis for treatment. In recent years, however, due to increased biopsy and acquisition of autopsy specimens, research is beginning to unravel the genetic and epigenetic drivers of DIPG. Insight gleaned from these studies has led to improvements in approaches to both model these tumors in the lab and to potentially treat them in the clinic. This review will detail the initial strides toward modeling DIPG in animals, which included allograft and xenograft rodent models using non-DIPG glioma cells. Important advances in the field came with the development of in vitro cell and in vivo xenograft models derived directly from autopsy material of DIPG patients or from human embryonic stem cells. Finally, we will summarize the progress made in the development of genetically engineered mouse models of DIPG. Cooperation of studies incorporating all of these modeling systems to both investigate the unique mechanisms of gliomagenesis in the brainstem and to test potential novel therapeutic agents in a preclinical setting will result in improvement in treatments for DIPG patients.