Physics in medicine and biology
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Knowledge of accurate parameter estimates is essential for incorporating normal tissue complication probability (NTCP) models into biologically based treatment planning. The purpose of this work is to derive parameter estimates for the Lyman-Kutcher-Burman (LKB) NTCP model using a combined analysis of multi-institutional toxicity data for the lung (radiation pneumonitis) and parotid gland (xerostomia). A series of published clinical datasets describing dose response for radiation pneumonitis (RP) and xerostomia were identified for this analysis. ⋯ For xerostomia expressed as reduction in stimulated salivary flow below 25% within six months after radiotherapy, the following values were obtained: m = 0.53 (95% CI 0.45, 0.65) and TD(50) = 31.4 Gy (95% CI 29.1, 34.0). Although a large number of parameter estimates for different NTCP models and critical structures exist and continue to appear in the literature, it is hard to justify the use of any single parameter set obtained at a selected institution for the purposes of biologically based treatment planning. Our expectation is that the proposed model parameters based on cumulative experience at various institutions are more representative of the overall practice of radiation therapy than any single-institution data, and could be more readily incorporated into clinical use.
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Parametric imaging using the Patlak graphical method has been widely used to analyze dynamic PET data. Conventionally a Patlak parametric image is generated by reconstructing a sequence of dynamic images first and then performing Patlak graphical analysis on the time-activity curves pixel-by-pixel. However, because it is rather difficult to model the noise distribution in reconstructed images, the spatially variant noise correlation is simply ignored in the Patlak analysis, which leads to sub-optimal results. ⋯ We conduct computer simulations to validate the proposed direct method. Comparisons with the conventional indirect approach show that the proposed method results in a more accurate estimate of the parametric image. The proposed method has been applied to dynamic fully 3D PET data from a microPET scanner.
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MR elastography (MRE) enables the noninvasive determination of the viscoelastic behavior of human internal organs based on their response to oscillatory shear stress. An experiment was developed that combines multifrequency shear wave actuation with broad-band motion sensitization to extend the dynamic range of a single MRE examination. With this strategy, multiple wave images corresponding to different driving frequencies are simultaneously received and can be analyzed by evaluating the dispersion of the complex modulus over frequency. ⋯ Five standard rheological models (Maxwell, Voigt, Zener, Jeffreys and fractional Zener model) were assessed for their ability to reproduce the observed dispersion curves. The three-parameter Zener model was found to yield the most consistent results with two shear moduli mu(1) = 0.84 +/- 0.22 (1.36 +/- 0.31) kPa, mu(2) = 2.03 +/- 0.19 (1.86 +/- 0.34) kPa and one shear viscosity of eta = 6.7 +/- 1.3 (5.5 +/- 1.6) Pa s (interindividual mean +/- SD) in brain (liver) experiments. Significant differences between the rheological parameters of brain and liver were found for mu(1) and eta (P < 0.05), indicating that human brain is softer and possesses a higher viscosity than liver.
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We have investigated improvements to PET-MR image registration offered by PET-CT scanning. Ten subjects with suspected soft-tissue sarcomas were scanned with an in-line PET-CT and a clinical MR scanner. PET to CT, CT to MR and PET to MR image registrations were performed using a rigid-body external marker technique and rigid and non-rigid voxel-similarity algorithms. ⋯ The application of rigid and non-rigid CT to MR transformations to accompanying PET data gives significantly reduced PET-MR errors of 10.0 mm and 8.5 mm, respectively. Visual comparison by two independent observers confirmed the improvement over direct PET-MR registration. We conclude that PET-MR registration can be more accurately and reliably achieved using the hybrid technique described than through direct rigid-body registration of PET to MR.
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We previously developed a noninvasive technique for the quantification of fluorodeoxyglucose (FDG) positron emission tomography (PET) images using an image-derived input function obtained from a manually drawn carotid artery region. Here, we investigate the use of independent component analysis (ICA) for more objective identification of the carotid artery and surrounding tissue regions. Using FDG PET data from 22 subjects, ICA was applied to an easily defined cubical region including the carotid artery and neighboring tissue. ⋯ In fact, the ICA-derived input functions and CMRgl measurements were not only highly correlated (correlation coefficients >0.99) to, but also highly comparable (regression slopes between 0.92 and 1.09), with those generated using arterial blood sampling. Moreover, the reliability of the ICA-derived input function remained high despite variations in the location and size of the cubical region. The ICA procedure makes it possible to quantify FDG PET images in an objective and reproducible manner.