Iterative prescription refinement in fully discretized inverse problems of radiation therapy planning

Iterative Prescription Refinement in Fully-Discretized Inverse Problems of Radiation Therapy Planning

We formulate the method of Iterative Prescription Refinement (IPR) for inverse planning in any fully-discretized model of radiation therapy. The method starts out from an ideal dose prescription and repeatedly refines it into a refined dose prescription. This is done computationally without human interaction until a prespecified stopping rule is met, at which point the refined dose vector and t...

Fast fully iterative Newton-type methods for inverse problems

We study nonlinear inverse problems of the form

Inverse Methods for Planning Radiation Therapy

A new era in cancer radiation therapy has been gradually emerging during the last decade with the rapid development of many new and powerful methods for plan­ ning treatment and for optimizing the delivery of radiation doses. This process parallels in many respects the rapid devel­ opment in medical imaging technology that took place during the last 20 years, where computed tomography (CT), mag...

Iterative Approach for Automatic Beam Angle Selection in Intensity Modulated Radiation Therapy Planning

Introduction: Beam-angle optimization (BAO) is a computationally intensive problem for a number of reasons. First, the search space of the solutions is huge, requiring enumeration of all possible beam orientation combinations. For example, when choosing 4 angles out of 36 candidate beam angles, C36 = 58905 possible combinations exist.  Second, any change in a   beam 4 config...

Iterative Algorithms in Inverse Problems