Physics in medicine and biology
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The main purpose was to compare average glandular dose (AGD) for same-compression digital mammography (DM) and digital breast tomosynthesis (DBT) acquisitions in a population based screening program, with and without breast density stratification, as determined by automatically calculated breast density (Quantra™). Secondary, to compare AGD estimates based on measured breast density, air kerma and half value layer (HVL) to DICOM metadata based estimates. AGD was estimated for 3819 women participating in the screening trial. ⋯ Mean AGD error between estimates based on measurements (air kerma and HVL) versus DICOM header data was 3.8%, but for one mammography unit as high as 7.9%. Mean error of using the AGD value reported in the DICOM header was 10.7 and 13.3%, respectively. Thus, measurement of breast density, radiation dose and beam quality can substantially affect AGD estimates.
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The integration of 1.5 T MRI functionality with a radiotherapy linear accelerator (linac) has been pursued since 1999 by the UMC Utrecht in close collaboration with Elekta and Philips. The idea behind this integrated device is to offer unrivalled, online and real-time, soft-tissue visualization of the tumour and the surroundings for more precise radiation delivery. The proof of concept of this device was given in 2009 by demonstrating simultaneous irradiation and MR imaging on phantoms, since then the device has been further developed and commercialized by Elekta. ⋯ Absolute doses were found to be highly accurate, with deviations ranging from 0.0% to 1.7% in the isocenter. The geometrical, MRI based targeting as confirmed using portal images was better than 0.5 mm, ranging from 0.2 mm to 0.4 mm. In conclusion, high precision, high-field, 1.5 T MRI guided radiotherapy is clinically feasible.
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Percutaneous pelvic screw placement is challenging due to narrow bone corridors surrounded by vulnerable structures and difficult visual interpretation of complex anatomical shapes in 2D x-ray projection images. To address these challenges, a system for planning, guidance, and quality assurance (QA) is presented, providing functionality analogous to surgical navigation, but based on robust 3D-2D image registration techniques using fluoroscopy images already acquired in routine workflow. Two novel aspects of the system are investigated: automatic planning of pelvic screw trajectories and the ability to account for deformation of surgical devices (K-wire deflection). ⋯ The dKC-Reg algorithm localized the K-wire tip within 1.1 mm and 1.4° and was consistently more accurate than rigid-body tracking (errors up to 9 mm). The system was shown to automatically compute reliable screw trajectories and accurately localize deformed surgical devices (K-wires). Such capability could improve guidance and QA in orthopaedic surgery, where workflow is impeded by manual planning, conventional tool trackers add complexity and cost, rigid tool assumptions are often inaccurate, and qualitative interpretation of complex anatomy from 2D projections is prone to trial-and-error with extended fluoroscopy time.
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Texture-based radiomic models constructed from medical images have the potential to support cancer treatment management via personalized assessment of tumour aggressiveness. While the identification of stable texture features under varying imaging settings is crucial for the translation of radiomics analysis into routine clinical practice, we hypothesize in this work that a complementary optimization of image acquisition parameters prior to texture feature extraction could enhance the predictive performance of texture-based radiomic models. As a proof of concept, we evaluated the possibility of enhancing a model constructed for the early prediction of lung metastases in soft-tissue sarcomas by optimizing PET and MR image acquisition protocols via computerized simulations of image acquisitions with varying parameters. ⋯ The model constructed with textures extracted from simulated images acquired with a standard clinical set of acquisition parameters reached an average AUC of [Formula: see text] in bootstrap testing experiments. In comparison, the model performance significantly increased using an optimal set of image acquisition parameters ([Formula: see text]), with an average AUC of [Formula: see text]. Ultimately, specific acquisition protocols optimized to generate superior radiomics measurements for a given clinical problem could be developed and standardized via dedicated computer simulations and thereafter validated using clinical scanners.
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Comparative Study
Comparison of region-of-interest-averaged and pixel-averaged analysis of DCE-MRI data based on simulations and pre-clinical experiments.
Differences between region-of-interest (ROI) and pixel-by-pixel analysis of dynamic contrast enhanced (DCE) MRI data were investigated in this study with computer simulations and pre-clinical experiments. ROIs were simulated with 10, 50, 100, 200, 400, and 800 different pixels. For each pixel, a contrast agent concentration as a function of time, C(t), was calculated using the Tofts DCE-MRI model with randomly generated physiological parameters (K trans and v e) and the Parker population arterial input function. ⋯ In pre-clinical experiments, the pixel-by-pixel averaged K trans was ~15% larger than the ROI-averaged K trans. Overall, with the Tofts model, the extracted physiological parameters from the pixel-by-pixel averages were larger than the ROI averages. These differences were dependent on the heterogeneity of the ROI.