Physics in medicine and biology
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Four-dimensional (4D) Ultrafast ultrasound imaging was recently proposed to image and quantify blood flow with high sensitivity in 3D as well as anatomical, mechanical or functional information. In 4D Ultrafast imaging, coherent compounding of tilted planes waves emitted by a 2D matrix array were used to image the medium at high volume rate. 4D ultrafast imaging, however, requires a high channel count (>1000) to drive those probes. Alternative approaches have been proposed and investigated to efficiently reduce the density of elements, such as sparse or under-sampled arrays while maintaining a decent image quality and high volume rate. ⋯ In this study, we investigate the row and column addressed (RCA) approach with the orthogonal plane wave (OPW) compounding strategy using real hardware limitations. We designed and built a large 7 MHz 128 + 128 probe dedicated to vascular imaging and connected to a 256-channel scanner to implement the OPW imaging scheme. Using this strategy, we demonstrate that 4D ultrafast Power Doppler imaging of a large volume of [Formula: see text] up to [Formula: see text] depth, both in vitro on flow phantoms and in vivo on the carotid artery of a healthy volunteer at a volume rate of 834 Hz.
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Convolutional neural networks (CNNs) have become the state-of-the-art method for medical segmentation. However, repeated pooling and striding operations reduce the feature resolution, causing loss of detailed information. Additionally, tumors of different patients are of different sizes. ⋯ The Dice similarity coefficient values of CAC-SPP were 0.78 ± 0.08 and 0.85 ± 0.03, respectively, which were higher than those of U-Net (0.70 ± 0.11 and 0.82 ± 0.04) and ResNet-101 (0.76 ± 0.10 and 0.84 ± 0.03). The segmentation speed of CAC-SPP was comparable with ResNet-101, but about 36% faster than U-Net. In conclusion, the proposed CAC-SPP, which could extract high-resolution features with large receptive fields and capture multi-scale context yields, improves the accuracy of segmentation performance for rectal tumors.
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The relative biological effectiveness (RBE) of protons varies with multiple physical and biological factors. Phenomenological RBE models have been developed to include such factors in the estimation of a variable RBE, in contrast to the clinically applied constant RBE of 1.1. In this study, eleven published phenomenological RBE models and two plan-based models were explored and applied to simulated patient cases. ⋯ There were considerable variations between the estimations of RBE and RBE-weighted doses from the different models. These variations were a consequence of fundamental differences in experimental databases, model assumptions and regression techniques. The results from the implementation of RBE models in dose planning studies should be evaluated in light of these deviations.
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Respiratory motion reduces both the qualitative and quantitative accuracy of PET images in oncology. This impact is more significant for quantitative applications based on kinetic modeling, where dynamic acquisitions are associated with limited statistics due to the necessity of enhanced temporal resolution. The aim of this study is to address these drawbacks, by combining a respiratory motion correction approach with temporal regularization in a unique reconstruction algorithm for dynamic PET imaging. ⋯ Patlak parameter estimations on reconstructed images with the proposed reconstruction methods resulted in 30% and 40% bias reduction in the tumor and lung region respectively for the Patlak slope, and a 30% bias reduction for the intercept in the tumor region (a similar Patlak intercept was achieved in the lung area). Incorporation of the respiratory motion correction using an elastic model along with a temporal regularization in the reconstruction process of the PET dynamic series led to substantial quantitative improvements and motion artifact reduction. Future work will include the integration of a linear FDG kinetic model, in order to directly reconstruct parametric images.
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This study aims to develop a computer-aided diagnosis (CADx) scheme for classification between malignant and benign lung nodules, and also assess whether CADx performance changes in detecting nodules associated with early and advanced stage lung cancer. The study involves 243 biopsy-confirmed pulmonary nodules. Among them, 76 are benign, 81 are stage I and 86 are stage III malignant nodules. ⋯ AUC values computed from three classifiers trained using the same data set are consistent without statistically significant difference (p > 0.05). This study demonstrates (1) the feasibility of applying a CADx scheme to accurately distinguish between benign and malignant lung nodules, and (2) a positive trend between CADx performance and cancer progression stage. Thus, in order to increase CADx performance in detecting subtle and early cancer, training data sets should include more diverse early stage cancer cases.