Biomedical subjects

Todd R McNutt

Publications and source records attributed to Todd R McNutt.

6 recordsLinked to original sources

PubMed2006

The impact of dental metal artifacts on head and neck IMRT dose distributions.

BACKGROUND AND PURPOSES: To quantify the cold or hot spot induced in IMRT treatment plans due to the presence of metal artifact in CT image data sets stemming from dental work. PATIENTS AND METHODS: Metal artifact corrected image data sets of five patients have been analyzed. IMRT plans were generated using five different planning image data sets: (a) uncorrected (UC) (b) homogeneous uncorrected (HUC), (c) sinogram completion corrected (SCC), (d) minimum value corrected (MVC), and (e) image set (d) subsequently corrected with a streak artifacts reduction algorithm (SAR-MVC). The SAR-MVC data set is assumed to be the closest approximation to the absence of metal artifacts and has therefore been taken as the reference image data set. An IMRT plan was generated for each of the image datasets (a)-(e). The resulting IMRT treatment plans for data sets (a)-(d) were then projected onto the reference data set (e) and recalculated. The reference dose distribution (e) was then subtracted from these recalculated dose distributions. Using dose difference analysis, the cold and hot spots in organs at risk (OARs) and the target volumes (TVs) were quantified. RESULTS: When compared to the reference dose distribution, the UC, HUC, and SCC plans exhibited hot spots showing on average more than 1.0 Gy hot dose in the left and right parotids. For the UC, HUC, and SCC recalculated plans, subvolumes of the clinical target volumes (CTV) were under dosed on average by more than 0.9 Gy. On the other hand, the MVC plan showed less than 0.3 Gy hot dose in both parotids, and the cold dose in the CTVs were reduced by up to 0.8 Gy. CONCLUSIONS: The presence of dental metal artifacts in head and neck planning CT data sets can lead to relative hot spots in OARs and relative cold spots in regions of the TVs when compared to the reference data set that more closely approximates the patient anatomy. This effect can be reduced if a simple minimum value correction (MVC) method for the dental metal artifacts is employed.

Artifacts↗

PubMed2004

Automated model-based organ delineation for radiotherapy planning in prostatic region.

PURPOSE: Organ delineation is one of the most tedious and time-consuming parts of radiotherapy planning. It is usually performed by manual contouring in two-dimensional slices using simple drawing tools, and it may take several hours to delineate all structures of interest in a three-dimensional (3D) data set used for planning. In this paper, a 3D model-based approach to automated organ delineation is introduced that allows for a significant reduction of the time required for contouring. METHODS AND MATERIALS: The presented method is based on an adaptation of 3D deformable surface models to the boundaries of the anatomic structures of interest. The adaptation is based on a tradeoff between deformations of the model induced by its attraction to certain image features and the shape integrity of the model. To make the concept clinically feasible, interactive tools are introduced that allow quick correction in problematic areas in which the automated model adaptation may fail. A feasibility study with 40 clinical data sets was done for the male pelvic area, in which the risk organs (bladder, rectum, and femoral heads) were segmented by automatically adapting the corresponding organ models. RESULTS: In several cases of the validation study, minor user interaction was required. Nevertheless, a statistically significant reduction in the time required compared with manual organ contouring was achieved. The results of the validation study showed that the presented model-based approach is accurate (1.0-1.7 mm mean error) for the tested anatomic structures. CONCLUSION: A framework for organ delineation in radiotherapy planning is presented, including automated 3D model-based segmentation, as well as tools for interactive corrections. We demonstrated that the proposed approach is significantly more efficient than manual contouring in two-dimensional slices.

Algorithms↗

PubMed2004

Treatment plan optimization incorporating respiratory motion.

Similar to conventional conformal radiotherapy, during lung tomotherapy, a motion margin has to be set for respiratory motion. Consequently, large volume of normal tissue is irradiated by intensive radiation. To solve this problem, we have developed a new motion mitigation method by incorporating target motion into treatment optimization. In this method, the delivery-breathing correlation is determined prior to treatment plan optimization. Beamlets are calculated by using the CT images at the corresponding breathing phases from a dynamic (four-dimensional) image sequence. With the displacement vector fields at different breathing phases, a set of deformed beamlets is obtained by mapping the dose to the primary phase. Optimization incorporating motion is then performed by using the deformed beamlets obtained by dose mapping. During treatment delivery, the same breathing-delivery correlation can be reproduced by instructing the patient to breathe following a visually displayed guiding cycle. This method was tested using a computer-simulated deformable phantom and a real lung case. Results show that treatment optimization incorporating motion achieved similar high dose conformality on a mobile target compared with static delivery. The residual motion effects due to imperfect breathing tracking were also analyzed.

Biophysical Phenomena↗

PubMed2003

IMRT delivery verification using a spiral phantom.

In this paper we report on the testing and verification of a system for IMRT delivery quality assurance that uses a cylindrical solid water phantom with a spiral trajectory for radiographic film placement. This spiral film technique provides more complete dosimetric verification of the entire IMRT treatment than perpendicular film methods, since it samples a three-dimensional dose subspace rather than using measurements at only one or two depths. As an example, the complete analysis of the predicted and measured spiral films is described for an intracranial IMRT treatment case. The results of this analysis are compared to those of a single field perpendicular film technique that is typically used for IMRT QA. The comparison demonstrates that both methods result in a dosimetric error within a clinical tolerance of 5%, however the spiral phantom QA technique provides a more complete dosimetric verification while being less time consuming. To independently verify the dosimetry obtained with the spiral film, the same IMRT treatment was delivered to a similar phantom in which LiF thermoluminescent dosimeters were arranged along the spiral trajectory. The maximum difference between the predicted and measured TLD data for the 1.8 Gy fraction was 0.06 Gy for a TLD located in a high dose gradient region. This further validates the ability of the spiral phantom QA process to accurately verify delivery of an IMRT plan.

Film Dosimetry↗

PubMed2003

On the incorporation of multi-modality image registration into the radiotherapy treatment planning process.

A technique is presented that allows the direct use of physiological image sets in the radiation therapy treatment planning process. When fused to the treatment planning CT, physiological image studies may allow one to define physiological tumor subvolumes consisting of areas of possible chronic hypoxia, areas of high perfusion, areas of high diffusion, and areas containing high choline concentrations. These physiological tumor subvolumes could be selectively boosted to increase local control of malignant brain tumors once one has determined which of these physiological tumor subvolumes predicts for local tumor recurrence after conventional radiotherapy. In this technique a user assisted automatic registration technique is used that is based on an analytical estimate for the transformation matrix needed to register two rigid bodies. The only user input needed is three non-collinear points selected based on landmarks in the primary image and the corresponding three points in the secondary image. Since this registration technique uses two sets of at least three user-defined landmark points each of which has some selection error associated with it, the final registration will have an error that depends only on the selection error associated with the point sets. Since physiological image studies are acquired at the same setting as the T1- w MRI their spatial orientation with respect to the T1- w MRI is known. Therefore, the registration of multiple physiological image studies to the treatment planning CT can be accomplished by first correlating them to the T1- w MRI, and in a second step the T1- w MRI is then registered to the treatment planning CT. The desired registration of the physiological image studies to the treatment planning CT is then accomplished by simply composing the appropriate transformation matrices.

Brain Neoplasms↗

PubMed2002

A method of scaling the 3D electron pencil-beam dose calculation to obtain accurate monitor units for irregularly-shaped electron beams.

A method for determining monitor units (MU) for electron beams using a 3D pencil beam dose algorithm is described. A set of correction factors (OF(c)) to the pencil beam dose is generated that transforms the output into dose per MU. The OF(c)s depend on cone selection, field size, and source-to-surface distance (SSD) for a given electron energy. The dose per MU is determined by scaling the dose by the OF(c). The OF(c) value is determined using a measured set of relative output factors (OF(rel)) for various field sizes and SSD. The pencil-beam algorithm is used to compute the "raw" value (OF(p)) to each of the OF(rel) measurement points. The OF(c) is the ratio of the measured OF(rel) to the calculated OF(p). The OF(c) value for irregularly-shaped electron fields or for electron fields at extended SSD may be interpolated from the OF(c) table. The interpolation over SSD is performed linearly using the central axis SSD. The interpolation over field size is more complicated and uses the minimum area-circumscribing rectangle around the field shape. Due to the relative flatness of the OF(c), the interpolation is less error prone than the more common direct interpolation of the output factors. Computations were performed in the ADAC Pinnacle3 Planning System. Measurements were obtained using a Varian 2300c for 6 and 15 MeV. The results show that this method can predict the dose per MU within 1% to 2% for clinical fields and within 3% to 4% for extreme field shapes.

Algorithms↗