IMRT treatment planning on 4D geometries for the era of dynamic MLC tracking

Real-time control of respiratory motion: Beyond radiation therapy

Motion management in radiation oncology is an important aspect of modern treatment planning and delivery. Special attention has been paid to control respiratory motion in recent years. However, other medical procedures related to both diagnosis and treatment are likely to benefit from the explicit control of breathing motion. Quantitative imaging - including increasingly important tools in radiology and nuclear medicine - is among the fields where a rapid development of motion control is most likely, due to the need for quantification accuracy. Emerging treatment modalities like focussed-ultrasound tumor ablation are also likely to benefit from a significant evolution of motion control in the near future. In the present article an overview of available respiratory motion systems along with ongoing research in this area is provided. Furthermore, an attempt is made to envision some of the most expected developments in this field in the near future.

Multi-GPU configuration of 4D intensity modulated radiation therapy inverse planning using global optimization

We report on the design, implementation and characterization of a multi-graphic processing unit (GPU) computational platform for higher-order optimization in radiotherapy treatment planning. In collaboration with a commercial vendor (Varian Medical Systems, Palo Alto, CA), a research prototype GPU-enabled Eclipse (V13.6) workstation was configured. The hardware consisted of dual 8-core Xeon processors, 256 GB RAM and four NVIDIA Tesla K80 general purpose GPUs. We demonstrate the utility of this platform for large radiotherapy optimization problems through the development and characterization of a parallelized particle swarm optimization (PSO) four dimensional (4D) intensity modulated radiation therapy (IMRT) technique. The PSO engine was coupled to the Eclipse treatment planning system via a vendor-provided scripting interface. Specific challenges addressed in this implementation were (i) data management and (ii) non-uniform memory access (NUMA). For the former, we alternated between parameters over which the computation process was parallelized. For the latter, we reduced the amount of data required to be transferred over the NUMA bridge. The datasets examined in this study were approximately 300 GB in size, including 4D computed tomography images, anatomical structure contours and dose deposition matrices. For evaluation, we created a 4D-IMRT treatment plan for one lung cancer patient and analyzed computation speed while varying several parameters (number of respiratory phases, GPUs, PSO particles, and data matrix sizes). The optimized 4D-IMRT plan enhanced sparing of organs at risk by an average reduction of [Formula: see text] in maximum dose, compared to the clinical optimized IMRT plan, where the internal target volume was used. We validated our computation time analyses in two additional cases. The computation speed in our implementation did not monotonically increase with the number of GPUs. The optimal number of GPUs (five, in our study) is directly related to the hardware specifications. The optimization process took 35 min using 50 PSO particles, 25 iterations and 5 GPUs.

The development of a 4D treatment planning methodology to simulate the tracking of central lung tumors in an MRI-linac

PURPOSE

Targeting and tracking of central lung tumors may be feasible on the Elekta MRI-linac (MRL) due to the soft-tissue visualization capabilities of MRI. The purpose of this work is to develop a novel treatment planning methodology to simulate tracking of central lung tumors with the MRL and to quantify the benefits in OAR sparing compared with the ITV approach.

METHODS

Full 4D-CT datasets for five central lung cancer patients were selected to simulate the condition of having 4D-pseudo-CTs derived from 4D-MRI data available on the MRL with real-time tracking capabilities. We used the MRL treatment planning system to generate two plans: (a) with a set of MLC-defined apertures around the target at each phase of the breathing ("4D-MRL" method); (b) with a fixed set of fields encompassing the maximum inhale and exhale of the breathing cycle ("ITV" method). For both plans, dose accumulation was performed onto a reference phase. To further study the potential benefits of a 4D-MRL method, the results were stratified by tumor motion amplitude, OAR-to-tumor proximity, and the relative OAR motion (ROM).

RESULTS

With the 4D-MRL method, the reduction in mean doses was up to 3.0 Gy and 1.9 Gy for the heart and the lung. Moreover, the lung's V12.5 Gy was spared by a maximum of 300 cc. Maximum doses to serial organs were reduced by up to 6.1 Gy, 1.5 Gy, and 9.0 Gy for the esophagus, spinal cord, and the trachea, respectively. OAR dose reduction with our method depended on the tumor motion amplitude and the ROM. Some OARs with large ROMs and in close proximity to the tumor benefited from tracking despite small tumor amplitudes.

CONCLUSIONS

We developed a novel 4D tracking methodology for the MRL for central lung tumors and quantified the potential dosimetric benefits compared with our current ITV approach.

Inverse 4D conformal planning for lung SBRT using particle swarm optimization

A critical aspect of highly potent regimens such as lung stereotactic body radiation therapy (SBRT) is to avoid collateral toxicity while achieving planning target volume (PTV) coverage. In this work, we describe four dimensional conformal radiotherapy using a highly parallelizable swarm intelligence-based stochastic optimization technique. Conventional lung CRT-SBRT uses a 4DCT to create an internal target volume and then, using forward-planning, generates a 3D conformal plan. In contrast, we investigate an inverse-planning strategy that uses 4DCT data to create a 4D conformal plan, which is optimized across the three spatial dimensions (3D) as well as time, as represented by the respiratory phase. The key idea is to use respiratory motion as an additional degree of freedom. We iteratively adjust fluence weights for all beam apertures across all respiratory phases considering OAR sparing, PTV coverage and delivery efficiency. To demonstrate proof-of-concept, five non-small-cell lung cancer SBRT patients were retrospectively studied. The 4D optimized plans achieved PTV coverage comparable to the corresponding clinically delivered plans while showing significantly superior OAR sparing ranging from 26% to 83% for D max heart, 10%-41% for D max esophagus, 31%-68% for D max spinal cord and 7%-32% for V 13 lung.

Objective function to obtain multiple representative waveforms for a novel helical CT scan protocol

PURPOSE

To develop objective functions for selecting multiple representative respiratory waveforms. A specific application considered is to reduce the number of swiping scans in a novel helical CT scan protocol to harvest efficiency and dose reduction benefit.

METHODS

The authors consider a general class of potential objective functions consisting of weighted norms on pointwise profile differentials. The authors utilize the Lagrangian approach and derive proper conditions on the formulation based on first and second order optimality conditions. The derived objective functions are applied to clinically acquired respiratory trajectories for swipe subset selection to verify the validity and generality of the proposed rationale. An end-to-end 4DCT reconstruction comparison is performed using a swipe subset of data corresponding to 3 out of the full 25 waveforms to assess the consequence in image quality and dose.

RESULTS

Their results show that maximizing the proposed objective function with the suggested parameters yields maximal spread of trajectories among the selected subset. 4DCT Reconstruction using the chosen subset of data indicates the potential for further dose reduction by about 5 to 10 folds without significant sacrifice in image quality. Experimental results also support further generalization to include slice prioritization.

CONCLUSIONS

The authors have derived a formulation that is both simple and general as a metric to quantify the spread of a set of respiratory trajectories, which can be used for subset selection with potential computation and dose reduction benefit when applied to a newly developed helical 4DCT scan protocol.