Pure Vision Transformer (CT-ViT) with Noise2Neighbors Interpolation for Low-Dose CT Image Denoising.

Convolutional neural networks (CNN) have been used for a wide variety of deep learning applications, especially in computer vision. For medical image processing, researchers have identified certain challenges associated with CNNs. These challenges encompass the generation of less informative features, limitations in capturing both high and low-frequency information within feature maps, and the computational cost incurred when enhancing receptive fields by deepening the network.

Dilated Convolution ResNet with Boosting Attention Modules and Combined Loss Functions for LDCT Image Denoising.

With the increasing concern regarding the radiation exposure of patients undergoing computed tomography (CT) scans, researchers have been using deep learning techniques to improve the quality of denoised low-dose CT (LDCT) images. In this paper, a cascaded dilated residual network (ResNet) with integrated attention modules, specifically spatial- and channel- attention modules, is proposed. This experiment demonstrated how these attention modules improved the denoised CT image by testing a simple ResNet with and without the modules.

A Hybrid Capsule Network for Automatic 3D Mandible Segmentation applied in Virtual Surgical Planning.

Automatic mandible segmentation of CT images is an essential step to achieve an accurate preoperative prediction of an intended target in three-dimensional (3D) virtual surgical planning. Segmentation of the mandible is a challenging task due to the complexity of the mandible structure, imaging artifacts, and metal implants or dental filling materials. In recent years, utilizing convolutional neural networks (CNNs) have made significant improvements in mandible segmentation.

Gradient-based Optimization Algorithm for Hybrid Loss Function in Low-dose CT Denoising.

Deep learning techniques have emerged in de-noising low-dose computed tomography (CT) images to avoid the potential health risks of high ionizing radiation dose on patients. Although these post-processing methods display high quality denoised images, the denoising performance still has the potential to improve. The primary purpose of this work was to determine and analyze the most effective and efficient hybrid loss function in deep learning (DL)-based denoising network.

Low Dose CT Image Denoising Using Boosting Attention Fusion GAN with Perceptual Loss.

Image denoising of Low-dose computed tomography (LDCT) images has continues to receive attention in the research community due to ongoing concerns about high-dose radiation exposure of patients for diagnosis. The use of low radiation CT image, however, could lead to inaccurate diagnosis due to the presence of noise. Deep learning techniques are being integrated into denoising methods to address this problem.

Cascaded Learning with Generative Adversarial Networks for Low Dose CT Denoising.

CT machines can be tuned in order to reduce the radiation dose used for imaging, yet reducing the radiation dose results in noisy images which are not suitable in clinical practice. In order for low dose CT to be used effectively in practice this issue must be addressed. Generative Adversarial Networks (GAN) have been used widely in computer vision research and have proven themselves as a powerful tool for producing images with high perceptual quality.

Empirical Mode Decomposition Based Hyperspectral Data Analysis for Brain Tumor Classification.

The capability of Hyperspectral Imaging (HSI) in rapidly acquiring abundant reflectance data in a non-invasive manner, makes it an ideal tool for obtaining diagnostic information about tissue pathology. Identifying wavelengths that provide the most discriminatory clues for specific pathologies will greatly assist in understanding their underlying biochemical characteristics. In this paper, we propose an efficient and computationally inexpensive method for determining the most relevant spectral bands for brain tumor classification.

Low-dose CT Denoising Using Edge Detection Layer and Perceptual Loss.

Low-dose CT imaging is a valid approach to reduce patients' exposure to X-ray radiation. However, reducing X-ray current increases noise and artifacts in the reconstructed CT images. Deep neural networks have been successfully employed to remove noise from low-dose CT images.

Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer.

Low-dose CT denoising is a challenging task that has been studied by many researchers. Some studies have used deep neural networks to improve the quality of low-dose CT images and achieved fruitful results. In this paper, we propose a deep neural network that uses dilated convolutions with different dilation rates instead of standard convolution helping to capture more contextual information in fewer layers.

Low-dose CT Denoising with Dilated Residual Network.

Low-dose Computed Tomography (CT) is considered a solution for reducing the risk of X-ray radiation; however, lowering the X-ray current results in a degraded reconstructed image. To improve the quality of the image, different noise removal techniques have been proposed. Con- volutional neural networks also have shown promising results in denoising the low-dose CT images.

Co-Sparse Analysis Model Based Image Registration to Compensate Brain Shift by Using Intra-Operative Ultrasound Imaging.

Notwithstanding the widespread use of image guided neurosurgery systems in recent years, the accuracy of these systems is strongly limited by the intra-operative deformation of the brain tissue, the so-called brain shift. Intra-operative ultrasound (iUS) imaging as an effective solution to compensate complex brain shift phenomena update patients coordinate during surgery by registration of the intra-operative ultrasound and the pre-operative MRI data that is a challenging problem.In this work a non-rigid multimodal image registration technique based on co-sparse analysis model is proposed.

Transesophageal 2D ultrasound to 3D computed tomography registration for the guidance of a cardiac arrhythmia therapy.

The work aims to develop a new image-processing method to improve the guidance of transesophageal high intensity focused ultrasound (HIFU) atrial fibrillation therapy. Our proposal is a novel registration approach that aligns intraoperative 2D ultrasound with preoperative 3D-CT information. This approach takes advantage of the anatomical constraints imposed at the transesophageal HIFU probe to simplify the registration process.

Nonrigid motion compensation in compressed sensing reconstruction of cardiac cine MRI.

In this work, a robust nonrigid motion compensation approach, is applied to the compressed sensing reconstruction of dynamic cardiac cine MRI sequences. Respiratory and cardiac motion separation coupled with a registration algorithm is used to provide accurate reconstruction of dynamic cardiac images. The proposed scheme employs a variable splitting based optimization strategy to enable joint motion estimation along with reconstruction.

A dictionary learning approach for human sperm heads classification.

Background And Objective: To diagnose infertility in men, semen analysis is conducted in which sperm morphology is one of the factors that are evaluated. Since manual assessment of sperm morphology is time-consuming and subjective, automatic classification methods are being developed. Automatic classification of sperm heads is a complicated task due to the intra-class differences and inter-class similarities of class objects.

Motion-compensated data decomposition algorithm to accelerate dynamic cardiac MRI.

Objectives: In dynamic cardiac magnetic resonance imaging (MRI), the spatiotemporal resolution is often limited by low imaging speed. Compressed sensing (CS) theory can be applied to improve imaging speed and spatiotemporal resolution. The combination of compressed sensing and low-rank matrix completion represents an attractive means to further increase imaging speed.

Shear wave elastography using Wigner-Ville distribution: a simulated multilayer media study.

Shear Wave Elastography (SWE) is a quantitative ultrasound-based imaging modality for distinguishing normal and abnormal tissue types by estimating the local viscoelastic properties of the tissue. These properties have been estimated in many studies by propagating ultrasound shear wave within the tissue and estimating parameters such as speed of wave. Vast majority of the proposed techniques are based on the cross-correlation of consecutive ultrasound images.

Curvelet based residual complexity objective function for non-rigid registration of pre-operative MRI with intra-operative ultrasound images.

Intra-operative ultrasound as an imaging based method has been recognized as an effective solution to compensate non rigid brain shift problem in recent years. Measuring brain shift requires registration of the pre-operative MRI images with the intra-operative ultrasound images which is a challenging task. In this study a novel hybrid method based on the matching echogenic structures such as sulci and tumor boundary in MRI with ultrasound images is proposed.

Planning screw insertion trajectory in lumbar spinal fusion using pre-operative CT images.

Spinal fusion permanently connects two or more vertebrae in spine to improve stability, correct a deformity or reduce pain by immobilizing the vertebrae through pedicle screw fixation. Pedicle screws should be inserted very carefully to prevent possible irrecoverable damages to the spinal cord. Surgeons use CT/fluoroscopic images to find how to insert the screws safely.

Compressed sensing reconstruction of cardiac cine MRI using golden angle spiral trajectories.

In dynamic cardiac cine Magnetic Resonance Imaging (MRI), the spatiotemporal resolution is limited by the low imaging speed. Compressed sensing (CS) theory has been applied to improve the imaging speed and thus the spatiotemporal resolution. The purpose of this paper is to improve CS reconstruction of under sampled data by exploiting spatiotemporal sparsity and efficient spiral trajectories.

Estimation of intraoperative brain shift by combination of stereovision and doppler ultrasound: phantom and animal model study.

Purpose: Combination of various intraoperative imaging modalities potentially can reduce error of brain shift estimation during neurosurgical operations. In the present work, a new combination of surface imaging and Doppler US images is proposed to calculate the displacements of cortical surface and deformation of internal vessels in order to estimate the targeted brain shift using a Finite Element Model (FEM). Registration error in each step and the overall performance of the method are evaluated.

A shape based rotation invariant method for ultrasound-MR image registration: A phantom study.

In this work, a new shape based method to improve the accuracy of Brain Ultrasound-MRI image registration is proposed. The method is based on modified Shape Context (SC) descriptor in combination with CPD algorithm. An extensive experiment was carried out to evaluate the robustness of this method against different initialization conditions.

A hybrid method for non-rigid registration of intra-operative ultrasound images with pre-operative MR images.

In recent years intra-operative ultrasound images have been used for many procedures in neurosurgery. The registration of intra-operative ultrasound images with preoperative magnetic resonance images is still a challenging problem. In this study a new hybrid method based on residual complexity is proposed for this problem.

Adaptation and evaluation of the multiple organs OSD for T2 MRI prostate segmentation.

This paper deals with the adaptation, the tuning and the evaluation of the multiple organs Optimal Surface Detection (OSD) algorithm for the T2 MRI prostate segmentation. This algorithm is initialized by first surface approximations of the prostate (obtained after a model adjustment), the bladder (obtained automatically) and the rectum (interactive geometrical model). These three organs are then segmented together in a multiple organs OSD scheme which proposes a competition between the gray level characteristics and some topological and anatomical information of these three organs.