Image-guided positioning and tracking

Super-resolution imaging in a multiple layer EPID

A new portal imager consisting of four vertically stacked conventional electronic portal imaging device (EPID) layers has been constructed in pursuit of improved detective quantum efficiency (DQE). We hypothesize that super-resolution (SR) imaging can also be achieved in such a system by shifting each layer laterally by half a pixel relative to the layer above. Super-resolution imaging will improve resolution and contrast-to-noise ratio (CNR) in megavoltage (MV) planar and cone beam computed tomography (MV-CBCT) applications. Simulations are carried out to test this hypothesis with digital phantoms. To assess planar resolution, 2 mm long iron rods with 0.3 × 0.3 mm² square cross-section are arranged in a grid pattern at the center of a 1 cm thick solid water. For measuring CNR in MV-CBCT, a 20 cm diameter digital phantom with 8 inserts of different electron densities is used. For measuring resolution in MV-CBCT, a digital phantom featuring a bar pattern similar to the Gammex™ phantom is used. A 6 MV beam is attenuated through each phantom and detected by each of the four detector layers. Fill factor of the detector is explicitly considered. Projections are blurred with an estimated point spread function (PSF) before super-resolution reconstruction. When projections from multiple shifted layers are used in SR reconstruction, even a simple shift-add fusion can significantly improve the resolution in reconstructed images. In the reconstructed planar image, the grid pattern becomes visually clearer. In MV-CBCT, combining projections from multiple layers results in increased CNR and resolution. The inclusion of two, three and four layers increases CNR by 40%, 70% and 99%, respectively. Shifting adjacent layers by half a pixel almost doubles resolution. In comparison, using four perfectly aligned layers does not improve resolution relative to a single layer.

Real-time auto-adaptive margin generation for MLC-tracked radiotherapy

In radiotherapy, abdominal and thoracic sites are candidates for performing motion tracking. With real-time control it is possible to adjust the multileaf collimator (MLC) position to the target position. However, positions are not perfectly matched and position errors arise from system delays and complicated response of the electromechanic MLC system. Although, it is possible to compensate parts of these errors by using predictors, residual errors remain and need to be compensated to retain target coverage. This work presents a method to statistically describe tracking errors and to automatically derive a patient-specific, per-segment margin to compensate the arising underdosage on-line, i.e. during plan delivery. The statistics of the geometric error between intended and actual machine position are derived using kernel density estimators. Subsequently a margin is calculated on-line according to a selected coverage parameter, which determines the amount of accepted underdosage. The margin is then applied onto the actual segment to accommodate the positioning errors in the enlarged segment. The proof-of-concept was tested in an on-line tracking experiment and showed the ability to recover underdosages for two test cases, increasing [Formula: see text] in the underdosed area about [Formula: see text] and [Formula: see text], respectively. The used dose model was able to predict the loss of dose due to tracking errors and could be used to infer the necessary margins. The implementation had a running time of 23 ms which is compatible with real-time requirements of MLC tracking systems. The auto-adaptivity to machine and patient characteristics makes the technique a generic yet intuitive candidate to avoid underdosages due to MLC tracking errors.

Radiation oncology: physics advances that minimize morbidity

ABSTRACT 

Radiation therapy has become an ever more successful treatment for many cancer patients. This is due in large part from advances in physics including the expanded use of imaging protocols combined with ever more precise therapy devices such as linear and particle beam accelerators, all contributing to treatments with far fewer side effects. This paper will review current state-of-the-art physics maneuvers that minimize morbidity, such as intensity-modulated radiation therapy, volummetric arc therapy, image-guided radiation, radiosurgery and particle beam treatment. We will also highlight future physics enhancements on the horizon such as MRI during treatment and intensity-modulated hadron therapy, all with the continued goal of improved clinical outcomes.

Radiation therapy in neurologic disease

Radiotherapy is a primary mode of treatment of many of the disease entities seen by the neurologist. Therefore knowledge of how ionizing radiation works and when it is indicated is a crucial part of the field of Neurology. The neurologist may also be confronted with some of the side effects and complications or radiotherapy treatment. This chapter attempts to serve as a review of the current day process of radiotherapy, a brief review of biology and physics of radiation, and how it is used in the treatment diseases which are common to the Neurologist. In addition we review the more commonly seen side effects and complications of treatment which may be seen by the neurologist.

Future radiation therapy: photons, protons and particles

Radiation therapy plays a critical role in the current management of cancer patients. The most common linear accelerator-based treatment device delivers photons of radiation. In an ever more precise fashion, state-of-the-art technology has recently allowed for both modulation of the radiation beam and imaging for this treatment delivery. This has resulted in better patient outcome with far fewer side effects than were achieved even a decade ago. Recently, a push has begun for proton therapy, which may have clinical advantage in select indications, although significant limitations for these devices have become apparent. In addition, currently, heavy particle therapy has been touted as a potential means to improve cancer patient outcomes. This article will highlight current benefits and drawbacks to modern radiation therapy and speculate on future tools that will likely dramatically improve radiation oncology.