Physics in medicine and biology
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This work looks at the feasibility of an online adaptive radiation therapy concept that would detect the daily position and shape of the patient, and would then correct the daily treatment to account for any changes compared with planning position. In particular, it looks at the possibility of developing algorithms to correct for large complicated shape change. For co-planar beams, the dose in an axial plane is approximately associated with the positions of a single multi-leaf collimator (MLC) pair. ⋯ This type of skew-like motion is not accounted for by the proposed ART technique. In conclusion, this technique has potential to correct for fairly extreme daily changes in patient setup, but some control of the daily position would still be necessary. Importantly, it was possible to combine treatments from different plans (MLC sequences) to correct for position and shape change.
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We couple standardized low-resolution electromagnetic tomography, an inverse solution for electroencephalography (EEG) and the common spatial pattern, which is here conceived as a data-driven beamformer, to classify the benchmark BCI (brain-computer interface) competition 2003, data set IV. The data set is from an experiment where a subject performed a self-paced left and right finger tapping task. Available for analysis are 314 training trials whereas 100 unlabelled test trials have to be classified. ⋯ Despite our use of an untrained classifier, and our extraction of only one attribute per class, our method yields accuracy similar to the winners of the competition for this data set. The distinct advantages of the approach presented here are the use of an untrained classifier and the processing speed, which make the method suitable for actual BCI applications. The proposed method is favourable over existing classification methods based on an EEG inverse solution, which rely either on iterative algorithms for single-trial independent component analysis or on trained classifiers.
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MR imaging based treatment planning for radiotherapy of prostate cancer is limited due to MR imaging system related geometrical distortions, especially for patients with large body sizes. On our 0.23 T open scanner equipped with the gradient distortion correction (GDC) software, the residual image distortions after the GDC were <5 mm within the central 36 cm x 36 cm area for a standard 48 cm field of view (FOV). In order to use MR imaging alone for treatment planning the effect of residual MR distortions on external patient contour determination, especially for the peripheral regions outside the 36 cm x 36 cm area, must be investigated and corrected. ⋯ Our results show that for patients with larger lateral dimensions (>36 cm), the differences in patient external contours between distortion-free CT images and GDC-corrected MR images were 1-2 cm because of the combination of greater gradient distortion and loss of field homogeneity away from the isocentre and the uncertainties in patient setup during CT and MRI scans. The measured distortion maps were used to perform point-by-point corrections for patients with large dimensions inside the effective FOV. Using the point-by-point method, the geometrical distortion after the GDC were reduced to <3 mm for external contour determination and the effective FOV was expanded from 36 cm to 42 cm.
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A novel method for dynamic ventilation imaging of the full respiratory cycle from four-dimensional computed tomography (4D CT) acquired without added contrast is presented. Three cases with 4D CT images obtained with respiratory gated acquisition for radiotherapy treatment planning were selected. Each of the 4D CT data sets was acquired during resting tidal breathing. ⋯ A linear regression resulted in a slope of 1.01 and a correlation coefficient of 0.984 for the ventilation images. The spatial distribution of ventilation was found to be case specific and a 30% difference in mass-specific ventilation between the lower and upper lung halves was found. These images may be useful in radiotherapy planning.
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A newly developed simulation toolkit, GATE (Geant4 Application for Tomographic Emission), was used to develop a Monte Carlo simulation of a fully three-dimensional (3D) clinical PET scanner. The Philips Allegro/GEMINI PET systems were simulated in order to (a) allow a detailed study of the parameters affecting the system's performance under various imaging conditions, (b) study the optimization and quantitative accuracy of emission acquisition protocols for dynamic and static imaging, and (c) further validate the potential of GATE for the simulation of clinical PET systems. A model of the detection system and its geometry was developed. ⋯ An agreement of <3% was obtained in scatter fraction, with a difference between 4% and 10% in the true and random coincidence count rates respectively, throughout a range of activity concentrations and under various imaging conditions, resulting in <8% differences between simulated and measured noise equivalent count rates performance. Finally, the image quality validation study revealed a good agreement in signal-to-noise ratio and contrast recovery coefficients for a number of different volume spheres and two different (clinical level based) tumour-to-background ratios. In conclusion, these results support the accurate modelling of the Philips Allegro/GEMINI PET systems using GATE in combination with a dead-time model for the signal flow description, which leads to an agreement of <10% in coincidence count rates under different imaging conditions and clinically relevant activity concentration levels.