Biomedical subjects

C Sibata

Publications and source records attributed to C Sibata.

5 recordsLinked to original sources

PubMed2001

Optimization of Gamma knife treatment planning via guided evolutionary simulated annealing.

We present a method for generating optimized Gamma Knife (Elekta, Stockholm, Sweden) radiosurgery treatment plans. This semiautomatic method produces a highly conformal shot packing plan for the irradiation of an intracranial tumor. We simulate optimal treatment planning criteria with a probability function that is linked to every voxel in a volumetric (MR or CT) region of interest. This sigmoidal P+ parameter models the requirement of conformality (i.e., tumor ablation and normal tissue sparing). After determination of initial radiosurgery treatment parameters, a guided evolutionary simulated annealing (GESA) algorithm is used to find the optimal size, position, and weight for each shot. The three-dimensional GESA algorithm searches the shot parameter space more thoroughly than is possible during manual shot packing and provides one plan that is suitable to the treatment criteria of the attending neurosurgeon and radiation oncologist. The result is a more conformal plan, which also reduces redundancy, and saves treatment administration time.

Algorithms↗

PubMed2000

TBI treatment planning using the ADAC pinnacle treatment planning system.

The use of total-body irradiation (TBI) for the purpose of bone marrow transplant is an established procedure at many institutions. In our institution, the TBI monitor unit (MU) calculation starts with the calibration done at the same conditions of the treatment source-axis distance (SAD) = 350 cm for the field size of 40 x 40 cm at a depth of 10 cm). The dose rate in the central axis of the beam at this distance is measured in cGy/MU. A tissue phantom ratio table obtained in the condition of treatment together with off-axis factors is used in the MU calculation for each particular patient. The treatment is done with the patient lying on his/her back and the beam is delivered using right-to-left lateral beams. Due to different thickness' of the patient, a lead compensator is built to compensate for the different parts of the body. Eighteen or 10-MV x-ray photons are used in the TBI treatment, and a 1-cm-thick lucite plate is placed near the patient to increase the dose to the surface. In vivo dosimetry using diodes is done to verify the calculations. The Rando-Phantom was computed-tomography scanned from the head to the abdomen with 1-cm-thick slices covering 70 cm of the phantom. This simulated the TBI treatment and correlated the calculations done by the ADAC treatment planning system to film measurements at the pelvis and lung levels. These results agreed within 5% of the measured dose. The use of the upper arms to reduce the dose to the lungs and optimization of dose using special compensators has been studied using the treatment planning system. Use of the multileaf collimator to compensate the dose received by the patient has been explored in this paper.

Humans↗

PubMed2000

Fast verification of Gamma Knifetrade mark treatment plans.

The Leksell stereotactic Gamma Knifetrade mark uses radiation from 201 (60)Co sources that are focused to the center of a collimator helmet to deliver a high dose of radiation with minimal irradiation of proximal structures. This paper presents a method for fast verification of the irradiation time as calculated by the Leksell Gamma Knifetrade mark treatment planning software GammaPlan((R)). To obtain the irradiation time for each shot in the treatment plan, one must first accurately calculate the tissue maximum ratio (TMR) for each of the individual 201 beams. The algorithm presented in this paper begins with the determination of the geometrical relationship between the Gamma Knifetrade mark collimator helmet and the stereotactic frame. A group of reference points is measured to build a head model simulating the patient skull geometry. During radiosurgery, the isocenter of the collimator helmet is moved to the shot center. A group of spatial vectors describing the reference points at the skull surface is obtained by converting the Cartesian coordinates to Polar coordinates. For each individual beam, the three nearest reference vectors are found by ranking the relative angles. The depth that each beam penetrates the patient's skull to the isocenter is obtained via linear interpolation. The TMR for each beam then is compared with the TMR for the calibration setup, which is done using a spherical 8 cm radius phantom. This algorithm is applied to verify the treatment time calculated in GammaPlan((R)) Version 5.2. The results are shown to agree with GammaPlan((R)) within 3%.

Algorithms↗

PubMed1988

Posterior spinal cord block: a dosimetric study.

To determine the optimal width of a midline posterior spinal block (MPSB) (to avoid delivering too great a dose to the cord and too small a dose to adjacent tissue), the authors determined with magnetic resonance (MR) imaging normal ranges of cord depth and width and correlated them with film dosimetric data. In 59 randomly selected patients there was a wide range for both depth and width. The average depths of the anterior and posterior surfaces of the cord were 6.7 cm +/- 1.4 and 5.4 cm +/- 1.3, respectively. The average cord width was 1.6 cm +/- 0.4. Optimal cord block width as a function of cord width was determined for a 6-MV photon beam. The optimal cord block width at the surface (half-value layer [HVL] thickness = 6) varied from 1.5 to 3.0 cm for cord widths of 0.8-2.4 cm, which correspond to two standard deviations from the average. There was no significant dependence on depth of the cord. For optimal treatment outcome, the MPSB width may have to be determined for each patient individually.

Film Dosimetry↗

PubMedDate not supplied

Surface matching of multimodality image volumes by a fuzzy elastic registration technique.

Multimodality image registration is useful in diagnostic imaging and treatment planning for radiation therapy. In this paper, we present a technique which registers the surfaces of two volumes acquired by different medical imaging modalities. We represent the image volumes in terms of their surface elements known as tiles. We identify the fuzzy variables, assign fuzzy membership functions to them and generate a fuzzy rule database. The fuzzy algorithm reduces the discrepancy between the two set of tiles until the surfaces are matched. In order to study the efficacy of our approach, we severely warp a simulated image and register it with its original. We register CT and MR volumes of humanoid phantom images. Finally, we present the results at the end of the article.

Algorithms↗