Biomedical Physics & Engineering Express 

About: Biomedical Physics & Engineering Express is an academic journal published by IOP Publishing. The journal publishes majorly in the area(s): Medicine & Physics. It has an ISSN identifier of 2057-1976. Over the lifetime, 1430 publications have been published receiving 7959 citations. The journal is also known as: Biomedical physics and engineering express.

TIGRE: A MATLAB-GPU toolbox for CBCT image reconstruction

Abstract: In this article the Tomographic Iterative GPU-based Reconstruction (TIGRE) Toolbox, a MATLAB/CUDA toolbox for fast and accurate 3D x-ray image reconstruction, is presented. One of the key features is the implementation of a wide variety of iterative algorithms as well as FDK, including a range of algorithms in the SART family, the Krylov subspace family and a range of methods using total variation regularization. Additionally, the toolbox has GPU-accelerated projection and back projection using the latest techniques and it has a modular design that facilitates the implementation of new algorithms. We present an overview of the structure and techniques used in the creation of the toolbox, together with two usage examples. The TIGRE Toolbox is released under an open source licence, encouraging people to contribute.

A mechanistic investigation of the oxygen fixation hypothesis and oxygen enhancement ratio.

Abstract: The presence of oxygen in tumours has substantial impact on treatment outcome; relative to anoxic regions, well-oxygenated cells respond better to radiotherapy by a factor 2.5–3. This increased radio-response is known as the oxygen enhancement ratio. The oxygen effect is most commonly explained by the oxygen fixation hypothesis, which postulates that radical-induced DNA damage can be permanently ‘fixed’ by molecular oxygen, rendering DNA damage irreparable. While this oxygen effect is important in both existing therapy and for future modalities such a radiation dose-painting, the majority of existing mathematical models for oxygen enhancement are empirical rather than based on the underlying physics and radiochemistry. Here we propose a model of oxygen-enhanced damage from physical first principles, investigating factors that might influence the cell kill. This is fitted to a range of experimental oxygen curves from literature and shown to describe them well, yielding a single robust term for oxygen interaction obtained. The model also reveals a small thermal dependency exists but that this is unlikely to be exploitable.

Segmentation of the prostate and organs at risk in male pelvic ct images using deep learning

Abstract: Inter-and intra-observer variation in delineating regions of interest (ROIs) occurs because of differences in expertise level and preferences of the radiation oncologists. We evaluated the accuracy of a segmentation model using the U-Net structure to delineate the prostate, bladder, and rectum in male pelvic CT images. The dataset used for training and testing the model consisted of raw CT scan images of 85 prostate cancer patients. We designed a 2D U-Net model to directly learn a mapping function that converts a 2D CT grayscale image to its corresponding 2D OAR segmented image. Our network contains blocks of convolution 2D layers with variable kernel sizes, channel number, and activation functions. On the left side of the U-Net model, we used three 3x3 convolutions, each followed by a rectified linear unit (ReLu) (activation function), and one max pooling operation. On the right side of the U-Net model, we used a 2x2 transposed convolution and two 3x3 convolution networks followed by a ReLu activation function. The automatic segmentation using the U-Net generated an average dice similarity coefficient (DC) and standard deviation (SD) of the following: DC +- SD (0.88 +- 0.12), (0.95 +- 0.04), and (0.92 +- 0.06) for the prostate, bladder, and rectum, respectively. Furthermore, the mean of average surface Hausdorff distance (ASHD) and SD were 1.2 +- 0.9 mm, 1.08 +- 0.8 mm, and 0.8 +- 0.6 mm for the prostate, bladder, and rectum, respectively. Our proposed method, which employs the U-Net structure, is highly accurate and reproducible for automated ROI segmentation. This provides a foundation to improve automatic delineation of the boundaries between the target and surrounding normal soft tissues on a standard radiation therapy planning CT scan.

Two-dimensional sample entropy: assessing image texture through irregularity

Abstract: Image texture analysis is a key task in computer vision. Although various methods have been applied to extract texture information, none of them are based on the principles of sample entropy, which is a measurement of entropy rate. This paper proposes a two-dimensional sample entropy method, namely SampEn2D, in order to measure irregularity in pixel patterns. We evaluated the proposed method in three different situations: a set of simulated images generated by a deterministic function corrupted with different levels of a stochastic influence; the Brodatz public texture database; and a real biological image set of rat sural nerve. Evaluation with simulations showed SampEn2D as a robust irregularity measure, closely following sample entropy properties. Results with Brodatz dataset testified superiority of SampEn2D to separate different image categories compared to conventional Haralick and wavelet descriptors. SampEn2D was also capable of discriminating rat sural nerve images by age groups with high accuracy (AUROC = 0.844). No significant difference was found between SampEn2D AUROC and those obtained with the best performed Haralick descriptors, i.e. entropy (AUROC = 0.828), uniformity (AUROC = 0.833), homogeneity (AUROC = 0.938) and Wavelet descriptors, i.e. Haar energy/entropy (AUROC = 0.932) and Daubechies energy/entropy (AUROC = 0.859). In addition, it was shown that SampEn2D computation time increases with image size, being around 1400 s for a 600 × 600 pixels image. In conclusion, SampEn2D showed to be stable and robust enough to be applied as texture feature quantifier and irregularity properties, as measured by SampEn2D, seem to be an important feature for image characterization in biomedical image analysis.