Medical Physics

Our medical research library holds 6 studies published in Medical Physics (2010–2017). Each entry links to the study's record on PubMed and, where there is one, to the publisher's page and a free full text. 6 have an abstract, the authors' own summary as PubMed records it: open "Abstract" to read it.

About the journal

• Publisher: John Wiley and Sons, Inc
• Published: 1974 to the present, United States
• Language: English
• ISSN: 0094-2405 (print), 2473-4209 (electronic)
• Its record in the NLM Catalog

2010s

• Three-dimensional finite-element based deformable image registration for evaluation of pleural cavity irradiation during photodynamic therapy. R. Penjweini, M.M. Kim, T.C. Zhu. 2017;44(7):3767-3775. PubMed · Publisher · Free full text

  Abstract

  Purpose: Photodynamic therapy (PDT) is used after surgical resection to treat the microscopic disease for malignant pleural mesothelioma and to increase survival rates. As accurate light delivery is imperative to PDT efficacy, the deformation of the pleural volume during the surgery is studied on its impact on the delivered light fluence. In this study, a three-dimensional finite element-based (3D FEM) deformable image registration is proposed to directly match the volume of lung to the volume of pleural cavity obtained during PDT to have accurate representation of the light fluence accumulated in the lung, heart and liver (organs-at-risk) during treatment.

  Methods: A wand, comprised of a modified endotrachial tube filled with Intralipid and an optical fiber inside the tube, is used to deliver the treatment light. The position of the treatment is tracked using an optical tracking system with an attachment comprised of nine reflective passive markers that are seen by an infrared camera-based navigation system. This information is used to obtain the surface contours of the plural cavity and the cumulative light fluence on every point of the cavity surface that is being treated. The lung, heart, and liver geometry are also reconstructed from a series of computed tomography (CT) scans of the organs acquired in the same patient before and after the surgery. The contours obtained with the optical tracking system and CTs are imported into COMSOL Multiphysics, where the 3D FEM-based deformable image registration is obtained. The delivered fluence values are assigned to the respective positions (x, y, and z) on the optical tracking contour. The optical tracking contour is considered as the reference, and the CT contours are used as the target, which will be deformed. The data from three patients formed the basis for this study.

  Results: The physical correspondence between the CT and optical tracking geometries, taken at different times, from different imaging devices was established using the 3D FEM-based image deformable registration. The volume of lung was matched to the volume of pleural cavity and the distribution of light fluence on the surface of the heart, liver and deformed lung volumes was obtained.

  Conclusion: The method used is appropriate for analyzing problems over complicated domains, such as when the domain changes (as in a solid-state reaction with a moving boundary), when the desired precision varies over the entire domain, or when the solution lacks smoothness. Implementing this method in real-time for clinical applications and in situ monitoring of the under- or over- exposed regions to light during PDT can significantly improve the treatment for mesothelioma.

  © 2017 American Association of Physicists in Medicine.

• Variability of tumor area measurements for response assessment in malignant pleural mesothelioma. Z.E. Labby, C. Straus, P. Caligiuri, et al. 2013;40(8):081916. PubMed · Publisher · Free full text

  Abstract

  Purpose: The measurement of malignant pleural mesothelioma is critical to the assessment of tumor response to therapy. Current response assessment standards utilize summed linear measurements acquired on three computed tomography (CT) sections. The purpose of this study was to evaluate manual area measurements as an alternate response assessment metric, specifically through the study of measurement interobserver variability.

  Methods: Two CT scans from each of 31 patients were collected. Using a computer interface, five observers contoured tumor on three selected CT sections from each baseline scan. Four observers also constructed matched follow-up scan tumor contours for the same 31 patients. Area measurements extracted from these contours were compared using a random effects analysis of variance model to assess relative interobserver variability. The sums of section area measurements were also analyzed, since these area sums are more clinically relevant for response assessment.

  Results: When each observer's measurements were compared with those of the other four observers, strong correlation was observed. The 95% confidence interval for relative interobserver variability of baseline scan summed area measurements was [-71%, +240%], spanning 311%. For the follow-up scan summed area measurements, the 95% confidence interval for relative interobserver variability was [-41%, +70%], spanning 111%. At both baseline and follow-up, the variability among observers was a significant component of the total variability in both per-section and summed area measurements (p<0.0001).

  Conclusions: Despite the ability of tumor area measurements to capture tumor burden with greater fidelity than linear tumor thickness measurements, manual area measurements may not be a robust means of response assessment in mesothelioma patients.

• Is there a single spot size and grid for intensity modulated proton therapy? Simulation of head and neck, prostate and mesothelioma cases. L. Widesott, A.J. Lomax, M. Schwarz. 2012;39(3):1298-308. PubMed · Publisher

  Abstract

  Purpose: To assess the quality of dose distributions in real clinical cases for different dimensions of scanned proton pencil beams. The distance between spots (i.e., the grid of delivery) is optimized for each dimension of the pencil beam.

  Methods: The authors vary the σ of the initial Gaussian size of the spot, from σ(x) = σ(y) = 3 mm to σ(x) = σ(y) = 8 mm, to evaluate the impact of the proton beam size on the quality of intensity modulated proton therapy (IMPT) plans. The distance between spots, Δx and Δy, is optimized on the spot plane, ranging from 4 to 12 mm (i.e., each spot size is coupled with the best spot grid resolution). In our Hyperion treatment planning system (TPS), constrained optimization is applied with respect to the organs at risk (OARs), i.e., the optimization tries to satisfy the dose objectives in the planning target volume (PTV) as long as all planning objectives for the OARs are met. Three-field plans for a nasopharynx case, two-field plans for a prostate case, and two-field plans for a malignant pleural mesothelioma case are considered in our analysis.

  Results: For the head and neck tumor, the best grids (i.e., distance between spots) are 5, 4, 6, 6, and 8 mm for σ = 3, 4, 5, 6, and 8 mm, respectively. σ ≤ 5 mm is required for tumor volumes with low dose and σ ≤ 4 mm for tumor volumes with high dose. For the prostate patient, the best grid is 4, 4, 5, 5, and 5 mm for σ = 3, 4, 5, 6, and 8 mm, respectively. Beams with σ > 3 mm did not satisfy our first clinical requirement that 95% of the prescribed dose is delivered to more than 95% of prostate and proximal seminal vesicles PTV. Our second clinical requirement, to cover the distal seminal vesicles PTV, is satisfied for beams as wide as σ = 6 mm. For the mesothelioma case, the low dose PTV prescription is well respected for all values of σ, while there is loss of high dose PTV coverage for σ > 5 mm. The best grids have a spacing of 6, 7, 8, 9, and 12 mm for σ = 3, 4, 5, 6, and 8 mm, respectively.

  Conclusions: The maximum acceptable proton pencil beam σ depends on the volume treated, the protocol of delivery, and optimization of the plan. For the clinical cases, protocol and optimization used in this analysis, acceptable σs are ≤ 4 mm for the head and neck tumor, ≤ 3 mm for the prostate tumor and ≤ 6 mm for the malignant pleural mesothelioma. One can apply the same procedure used in this analysis when given a "class" of patients, a σ and a clinical protocol to determine the optimal grid spacing.

• Characterization of mesothelioma and tissues present in contrast-enhanced thoracic CT scans. N. Corson, W.F. Sensakovic, C. Straus, et al. 2011;38(2):942-7. PubMed · Publisher · Free full text

  Abstract

  Purpose: The purpose of this study was to characterize the Hounsfield unit (HU) distributions of mesothelioma and other tissues present in contrast-enhanced thoracic CT scans, to compare the HU distributions of mesothelioma, muscle, and liver by scanner and reconstruction filter/kernel combination, and to assess interpatient HU distribution variability.

  Methods: The database consisted of 28 contrast-enhanced thoracic CT scans from different patients. For each scan, regions of interest were manually outlined within each of 13 tissues, including mesothelioma. For each tissue, the empirical percentiles in HU values were calculated along with the interpatient variability. The HU distributions of select tissues were compared across three different scanner and reconstruction filter/kernel combinations.

  Results: The HU distributions of blood-containing tissues demonstrated substantial overlap, as did the HU distributions of pleural effusion, mesothelioma, muscle, and liver. The HU distribution of fat had the least overlap with the other tissues. Fat and muscle had the lowest interpatient HU variability and the narrowest HU distributions, while blood-containing tissues had the highest interpatient HU variability. A soft-tissue reconstruction filter/kernel yielded the narrowest HU distribution, and fat with artifact had the widest HU distribution.

  Conclusions: Characterization of tissues in CT scans enhances the understanding of those tissues' HU distributions. Due to their overlapping HU distributions and close spatial proximity to one another, separating pleural effusion, mesothelioma, muscle, and liver from one another is a difficult task based on HU value thresholding alone. The results illustrate the wide distributions and large variability that exist for tissues present in clinical thoracic CT scans.

• Computerized segmentation and measurement of malignant pleural mesothelioma. W.F. Sensakovic, S.G. Armato, C. Straus, et al. 2011;38(1):238-44. PubMed · Publisher · Free full text

  Abstract

  Purpose: The current linear method to track tumor progression and evaluate treatment efficacy is insufficient for malignant pleural mesothelioma (MPM). A volumetric method for tumor measurement could improve the evaluation of novel treatments, but a fully manual implementation of volume measurement is too tedious and time-consuming. This manuscript presents a computerized method for the three-dimensional segmentation and volumetric analysis of MPM.

  Methods: The computerized MPM segmentation method segments the lung parenchyma and hemithoracic cavities to define the pleural space. Nonlinear diffusion and a k-means classifier are then implemented to identify MPM in the pleural space. A database of 31 computed tomography scans from 31 patients with pathologically confirmed MPM was retrospectively collected. Three observers independently outlined five randomly selected sections in each scan. The Jaccard similarity coefficient (J) between each of the observers and between the observer-defined and computer-defined segmentations was calculated. The computer-defined and the observer-defined segmentation areas (averaged over all observers) were both calculated for each axial section and compared using Bland-Altman plots.

  Results: The median J value among observers averaged over all sections was 0.517. The median J between the computer-defined and manual segmentations was 0.484. The difference between these values was not statistically significant. The area delineated by the computerized method demonstrated variability and bias comparable to the tumor area calculated from manual delineations.

  Conclusions: A computerized method for segmentation and measurement of MPM was developed. This method requires minimal initialization by the user and demonstrated good agreement with manually drawn outlines and area measurements. This method will allow volumetric tracking of tumor progression and may improve the evaluation of novel MPM treatments.

• The influence of initial outlines on manual segmentation. W.F. Sensakovic, A. Starkey, R. Roberts, et al. 2010;37(5):2153-8. PubMed · Publisher · Free full text

  Abstract

  Purpose: Initial outlines are often presented as an aid to reduce the time-cost associated with manual segmentation and measurement of structures in medical images. This study evaluated the influence of initial outlines on manual segmentation intraobserver and interobserver precision.

  Methods: Three observers independently outlined all pleural mesothelioma tumors present in five computed tomography (CT) sections in each of 30 patient scans. After a lapse of time, each observer was presented with the same series of CT sections with the outlines of each observer superimposed as initial outlines. Each observer created altered outlines by altering the initial outlines to reflect their perception of the tumor boundary. Altered outlines were compared to original outlines using the Jaccard similarity coefficient (J). Intraobserver and interobserver precision of observer outlines were calculated by applying linear mixed effects analysis of variance models to the J values. The percent of minor alterations (alterations that resulted in only slight changes in the initial outline) was also recorded.

  Results: The average J value between pairs of observer original outlines was 0.371. The average J value between pairs of observer outlines when altered from an identical initial outline was 0.796, indicating increased interobserver precision. The average difference between J values of an observer's segmentation created by altering their own initial outline and when altering a different observer's initial outline was 0.476, indicating initial outlines strongly influence intraobserver precision. Observers made minor alterations on 74.5% of initial outlines with which they were presented.

  Conclusions: Intraobserver and interobserver precision were strongly dependent on the initial outline. These effects are likely due to the tendency of observers to make only minor corrections to initial outlines. This finding could impact observer study design, tumor growth assessment, computer-aided diagnosis system validation, and radiation therapy target volume definition when initial outlines are used as an observer aid.

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