High-intensity focused ultrasound (HIFU) therapy is a noninvasive treatment for cancer. Prediction of the HIFU treatment region in advance by ultrasound imaging is important for the efficacy and safety of the treatment. Acoustic radiation force (ARF) imaging has been shown to be useful in estimating the heat distribution caused by HIFU. In this study, we investigated the effect of shielding on the estimated heat distribution when the propagation of HIFU is partially shielded by an object simulating ribs, imitating an actual clinical situation, and examined the correction method of HIFU output power using ARF imaging to obtain tissue coagulation equivalent to that without shielding. As a result, it was suggested that even with partial acoustic shielding, when the HIFU output power is adjusted so that the displacement induced by ARF is equal to that without shielding, almost the same coagulated region can be obtained as in the unshielded case.
The diagnosis of lung cancer is practically done by looking at a sample of lung cells in the lab. However, signs of lung cancer can be found by screening using X-Ray, CT, or Histopathological images. Each of these imaging modalities has its advantages and disadvantages. Chest X-ray is the first-line investigation for suspected lung cancer in primary care. However, the highest-quality studies suggest that the sensitivity of chest X-rays for symptomatic lung cancer is only 77–80%. On the other hand, a chest CT scan uses x-rays to make detailed cross-sectional images of the chest. Instead of taking 1 or 2 pictures, like a regular x-ray, a CT scanner takes many pictures and a computer then combines them to show a slice of the part of the chest under investigation. A CT scan is more likely to show lung tumors than traditional chest x-rays. In addition, the size, shape, and position of any lung tumors can be shown by a chest CT scan. More lung cancers were detected in the CT screening group compared with the control group with a 95% confidence interval. Moreover, Histopathological image analysis is widely used for cancer grading. Compared to mammography, CT and others, histopathology slides provide more comprehensive information for the diagnosis, and the diseases are analyzed by detecting tissue and cells in lesions. However, an invasive biopsy is necessary, which is often tried to be avoided. Therefore, chest CT is an optimal candidate for our study in sense of accuracy and availability. In this proposal, we deal with multi-class cancer detection from CT Lung images that is by detecting the cancer type rather than two classes (cancerous and normal images). The proposed method is based on a better representation of the image features by using Wavelet scattering Transform (WST). The classification is performed using three machine learning (ML) algorithms including support vector machine (SVM), kernel nearest neighbor (KNN), and random forest (RF). The WST coefficients are stable under signal deformations and globally invariant to signal translation and rotation. Based on the simulation results, the proposed method achieved an accuracy of 93.24%, 95.28%, and 99.90% for the case of WST + SVM, WST + KNN, and WST + RF networks, respectively.
One of the most significant indicators of heart and cardiovascular health is blood pressure (BP). Blood pressure (BP) has gained great attention in the last decade. Uncontrolled high blood pressure increases the risk of serious health problems, including heart attack and stroke. Recently, machine/deep learning is leveraged for learning the BP from Photoplethysmography (PPG) signals. Hence, continuous BP monitoring can be introduced based on simple wearable contact sensors or even remotely sensed from a proper camera away from the clinical setup. However, the available training dataset imposes many limitations besides the other difficulties related to the PPG time series as high-dimensional data. This work presents beat-by-beat continuous PPG-based BP monitoring while accounting for the aforementioned limitations. For a better exploration of beats’ features, we propose to use wavelet scattering transform as a better descriptive domain to cope with the limitation of the training dataset and to help the deep learning network accurately learn the relationship between the morphological shapes of PPG beats and the BP. Long Short-Term Memory (LSTM) network is utilized to demonstrate the superiority of the wavelet scattering transform over others domains. The learning scenarios are carried out on a beat basis where the input corresponding PPG beat is used for predicting BP in two scenarios; 1) Beat-by-beat arterial blood pressure (ABP) estimation, and 2) Beat-by-beat estimation of the systolic and diastolic blood pressure values. Different transformations are used to extract the features of the PPG beats in different domains including time, DCT, DWT, and wavelet scattering Domains. The simulation results show that using the wavelet scattering domain outperforms the other domains in the sense of root mean square error (RMSE) and mean absolute error (MAE) for both of the suggested two scenarios.
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