Biomedical subjects

K Ruchala

Publications and source records attributed to K Ruchala.

4 recordsLinked to original sources

PubMed2002

Monte Carlo study of a highly efficient gas ionization detector for megavoltage imaging and image-guided radiotherapy.

The imaging characteristics of an arc-shaped xenon gas ionization chamber for the purpose of megavoltage CT imaging were investigated. The detector consists of several hundred 320 microm thick gas cavities separated by thin tungsten plates of the same thickness. Dose response, efficiency and resolution parameters were calculated using Monte Carlo simulations. The calculations were compared to measurements taken in a 4 MV photon beam, assuming that the measured signal in the chambers corresponds to the therein absorbed dose. The measured response profiles for narrow and broad incident photon beams could be well reproduced with the Monte Carlo calculations. They show, that the quantum efficiency is 29.2% and the detective quantum efficiency at zero frequency DQE(0) is 20.4% for the detector arc placed in focus with the photon source. For a detector placed out of focus, these numbers even increase. The efficiency of this kind of radiation detector for megavoltage radiation therefore surpasses the reported efficiency of existing detector technologies. The resolution of the detector is quantified with calculated and measured line spread functions. The corresponding modulation transfer functions were determined for different thicknesses of the tungsten plates. They show that the resolution is only slightly dependent on the plate thickness but is predominantly determined by the cell size of the detector. The optimal plate thickness is determined by a tradeoff between quantum efficiency, total signal generation and resolution. Thicker plates are more efficient but the total signal and the resolution decrease with plate thickness. In conclusion, a gas ionization chamber of the described type is a highly efficient megavoltage radiation detector, allowing to obtain CT images with very little dose for a sufficient image quality for anatomy verification. This kind of detector might serve as a model for a future generation of highly efficient radiation detectors.

Animals↗

PubMed1999

Tomotherapy.

Tomotherapy is delivery of intensity-modulated, rotational radiation therapy using a fan-beam delivery. The NOMOS (Sewickley, PA) Peacock system is an example of sequential (or serial) tomotherapy that uses a fast-moving, actuator-driven multileaf collimator attached to a conventional C-arm gantry to modulate the beam intensity. In helical tomotherapy, the patient is continuously translated through a ring gantry as the fan beam rotates. The beam delivery geometry is similar to that of helical computed tomography (CT) and requires the use of slip rings to transmit power and data. A ring gantry provides a stable and accurate platform to perform tomographic verification using an unmodulated megavoltage beam. Moreover, megavoltage tomograms have adequate tissue contrast and resolution to provide setup verification. Assuming only translational and rotational offset errors, it is also possible to determine the offsets directly from tomographic projections, avoiding the time-consuming image reconstruction operation. The offsets can be used to modify the leaf delivery pattern to match the beam to the patient's anatomy on each day of a course of treatment. If tomographic representations of the patient are generated, this information can also be used to perform dose reconstruction. In this way, the actual dose distribution delivered can be superimposed onto the tomographic representation of the patient obtained at the time of treatment. The results can be compared with the planned isodose on the planning CT. This comparison may be used as an accurate basis for adaptive radiotherapy whereby the optimized delivery is modified before subsequent fractions. The verification afforded tomotherapy allows more precise conformal therapy. It also enables conformal avoidance radiotherapy, the complement to conformal therapy, for cases in which the tumor volume is ill-defined, but the locations of sensitive structures are adequately determined. A clinical tomotherapy unit is under construction at the University of Wisconsin.

Dose Fractionation, Radiation↗

PubMed1999

Registration using tomographic projection files.

An algorithm has been developed and experimentally verified for tomographic registration--a patient positioning method using internal anatomy and standard external fiducial marks. This algorithm improves patient set-up and verification to an accuracy sufficient for tomotherapy. By implementation of this technique, the time-consuming reconstruction process is avoided. Instead, offsets in the x, y and z directions are determined directly from sinogram data by an algorithm that utilizes cross-correlations and Fourier transforms. To verify the efficiency and stability of the algorithm, data were collected on the University of Wisconsin's dedicated tomotherapy research workbench. The experiment indicates offset statistical errors of less than +/-0.8 mm for offsets up to 30 mm. With standard clinical techniques, initial patient offsets are expected to be less than 5 mm, so the 30 mm limitation is of no consequence. The angular resolution for the direction of patient translation is within the +/-2 degrees needed for tomotherapy.

Algorithms↗

PubMed1998

Maximum likelihood as a common computational framework in tomotherapy.

Tomotherapy is a dose delivery technique using helical or axial intensity modulated beams. One of the strengths of the tomotherapy concept is that it can incorporate a number of processes into a single piece of equipment. These processes include treatment optimization planning, dose reconstruction and kilovoltage/megavoltage image reconstruction. A common computational technique that could be used for all of these processes would be very appealing. The maximum likelihood estimator, originally developed for emission tomography, can serve as a useful tool in imaging and radiotherapy. We believe that this approach can play an important role in the processes of optimization planning, dose reconstruction and kilovoltage and/or megavoltage image reconstruction. These processes involve computations that require comparable physical methods. They are also based on equivalent assumptions, and they have similar mathematical solutions. As a result, the maximum likelihood approach is able to provide a common framework for all three of these computational problems. We will demonstrate how maximum likelihood methods can be applied to optimization planning, dose reconstruction and megavoltage image reconstruction in tomotherapy. Results for planning optimization, dose reconstruction and megavoltage image reconstruction will be presented. Strengths and weaknesses of the methodology are analysed. Future directions for this work are also suggested.

Algorithms↗