11 September 2023
CT scans are currently the most common imaging modality used for suspected stroke patients due to their short acquisition time and wide availability. However, MRI offers superior tissue contrast and image quality. In this study, eight deep learning m...
Background: Eliminating the need for a preoperative spine CT scan prior to spine surgery offers significant financial advantages, reduced radiation exposure, and a more streamlined workflow. This can be achieved by converting a standard-protocol MRI...
Due to major artifacts and uncalibrated Hounsfield units (HU), cone-beam computed tomography (CBCT) cannot be used readily for diagnostics and therapy planning purposes. This study addresses image-to-image translation by convolutional neural networks...
The generation of synthetic CT for carbon ion radiotherapy (CIRT) applications is challenging, since high accuracy is required in treatment planning and delivery, especially in an anatomical site as complex as the abdomen. Thirty-nine abdominal MRI-C...
Creating synthetic images of trabecular tissue provides an alternative for researchers to validate algorithms designed to study trabecular bone. Developing synthetic images requires baseline data, such as datasets of digital biological samples or tem...
Background/Objectives: Paediatric PET/CT imaging is crucial in oncology but poses significant radiation risks due to children’s higher radiosensitivity and longer post-exposure life expectancy. This study aims to minimize radiation exposure by...
Background: The clinical imperative to reduce patient ionizing radiation exposure during diagnosis and treatment planning necessitates robust, high-fidelity synthetic imaging solutions. Current cross-modal synthesis techniques, primarily based on GAN...
Deep convolutional neural network (CNN) helped enhance image quality of cone-beam computed tomography (CBCT) by generating synthetic CT. Most of the previous works, however, trained network by intensity-based loss functions, possibly undermining to p...
Image-guided treatment adaptation is a game changer in oncological particle therapy (PT), especially for younger patients. The purpose of this study is to present a cycle generative adversarial network (CycleGAN)-based method for synthetic computed t...
Objectives: Identifying patients’ advantageous radiotherapy modalities prior to CT simulation is challenging. This study aimed to develop a workflow using deep learning (DL)-predicted synthetic CT (sCT) for treatment modality comparison based s...
This study investigates the generation of synthetic CT (sCT) images from zero echo time (ZTE) MRI to support MR-only radiotherapy, which can reduce image registration errors and lower treatment planning costs. Since MRI lacks the electron density dat...
This study evaluated the bone regeneration effect and mechanical properties of “Sticky bone”, a mixture of platelet-rich fibrin (PRF) and synthetic bone grafts (SBGs), in the repair of large femoral bone defects in rabbits. Eighteen New Z...
Human CtIP is best known for its role in DNA end resection to initiate DNA double-strand break repair by homologous recombination. Recently, CtIP has also been shown to protect reversed replication forks from nucleolytic degradation upon DNA replicat...
Background and Purpose: Interfractional anatomical changes might affect the outcome of proton therapy (PT). We aimed to prospectively evaluate the role of Magnetic Resonance Imaging (MRI) based adaptive PT for children with tumors of the head and nec...
This study aimed to generate synthetic MR images from real CT images. CT# mean and standard deviation of a moving window across every pixel in the reconstructed CT images were mapped to their corresponding tissue-mimicking types. Identification of th...
The use of multimodal contrast agents can potentially overcome the intrinsic limitations of individual imaging methods. We have validated synthetic antiferromagnetic nanoparticles (SAF-NPs) as bimodal contrast agents for in vitro cell labeling and in...
Purpose: To investigate the feasibility of using cone-beam computed tomography (CBCT)-derived synthetic CTs to monitor the daily dose and trigger a plan review for adaptive proton therapy (APT) in head and neck cancer (HNC) patients. Methods: For 84...
(1) Background: Synthetic CT images of the pelvis were generated from daily CBCT images to monitor changes in water equivalent path length (WEPL) and determine the dosimetric impact of anatomy changes along the proton beam’s path; (2) Methods:...
In recent years, synthetic Computed Tomography (CT) images generated from Magnetic Resonance (MR) or Cone Beam Computed Tomography (CBCT) acquisitions have been shown to be comparable to real CT images in terms of dose computation for radiotherapy si...
Purpose: To develop a deep learning framework based on a hybrid dataset to enhance the quality of CBCT images and obtain accurate HU values. Materials and Methods: A total of 228 cervical cancer patients treated in different LINACs were enrolled. We...
We map single energy CT (SECT) scans to synthetic dual-energy CT (synth-DECT) material density iodine (MDI) scans using deep learning (DL) and demonstrate their value for liver segmentation. A 2D pix2pix (P2P) network was trained on 100 abdominal DEC...
With the rapid development of artificial intelligence (AI), various deep learning (DL) methods have been introduced into radiation oncology. Among them, the generation of synthetic Computed Tomography (sCT) images has attracted increasing attention,...
To improve synthetic CT (sCT) generation from cone-beam CT (CBCT) in radiotherapy, we propose a multiscale segmentation-guided diffusion framework. The proposed model integrates anatomical priors across multiple spatial resolutions through a segmenta...
Research exploring CycleGAN-based synthetic image generation has recently accelerated in the medical community due to its ability to leverage unpaired images effectively. However, a commonly established drawback of the CycleGAN, the introduction of a...
Purpose: The study evaluates the efficacy of cone-beam computed tomography (CBCT)-based synthetic CTs (sCT) as a potential alternative to verification CT (vCT) for enhanced treatment monitoring and early adaptation in proton therapy. Methods: Seven c...
Artificial Intelligence (AI) is revolutionising many aspects of radiotherapy (RT), opening scenarios that were unimaginable just a few years ago. The aim of this study is to propose a Deep Leaning (DL) approach able to quickly generate synthetic Comp...
Deep coal seams in the Junggar Basin, China, have demonstrated high gas yields due to enhanced pore structures resulting from hydraulic fracturing. However, raw coal samples inadequately represent these stimulated reservoirs, and acquiring fractured...
Background/Objectives: Early detection of hepatocellular carcinoma (HCC), particularly small lesions (<2 cm), which is crucial for curative treatment, remains challenging with conventional liver dynamic computed tomography (LDCT). We aimed to deve...
Egyptian Blue was the first synthetic pigment by humankind. It contains of cuprorivaite, which is a calcium-copper-silicate (CaCuSi4O10). This study reports the results of a mineralogical and computer tomographic study of Egyptian Blue finds from Agu...
Computed Tomography (CT) is commonly used for cancer screening as it utilizes low radiation for the scan. One problem with low-dose scans is the noise artifacts associated with low photon count that can lead to a reduced success rate of cancer detect...
Lung cancer is a highly prevalent pathology and a leading cause of cancer-related deaths. Most patients are diagnosed when the disease has manifested itself, which usually is a sign of lung cancer in an advanced stage and, as a consequence, the 5-yea...
The oral cavity is a frequent site for head and neck cancers, which rank as the sixth most common cancer globally, with a 5-year survival rate slightly over 50%. Current treatments are limited, and resistance to therapy remains a significant clinical...
Bioactive alloplastic materials, like beta-tricalcium phosphate (β-TCP) and calcium sulfate (CS), have been extensively researched and are currently used in orthopedic and dental bone regenerative procedures. The purpose of this study was to com...
Head and neck squamous cell carcinoma (SCC), particularly in the oral cavity, is among the most prevalent and lethal forms of cancer globally. Current therapeutic strategies, predominantly involving cisplatin, face challenges like chemoresistance and...
Multi-modal three-dimensional (3-D) image segmentation is used in many medical applications, such as disease diagnosis, treatment planning, and image-guided surgery. Although multi-modal images provide information that no single image modality alone...
Contrast media are widely diffused in biomedical imaging, due to their relevance in the diagnosis of numerous disorders. However, the risk of adverse reactions, the concern of potential damage to sensitive organs, and the recently described brain dep...
The purpose of this work is to develop a reliable deep-learning-based method that is capable of synthesizing needed CT from MRI for radiotherapy treatment planning. Simultaneously, we try to enhance the resolution of synthetic CT. We adopted pix2pix...
Background: The utility of synthetic ECV, which does not require hematocrit values, has been reported; however, high-quality CT images are essential for accurate quantification. Second-generation Deep Learning Reconstruction (DLR) enables low-noise a...
(1) Introduction: Computed tomography (CT) and magnetic resonance imaging (MRI) play an important role in the diagnosis and evaluation of spinal diseases, especially degenerative spinal diseases. MRI is mainly used to diagnose most spinal diseases be...
Purpose: Stereotactic radiotherapy (SRT) has become widely accepted as a treatment of choice for patients with a small number of brain metastases that are of an acceptable size, allowing for better target dose conformity, resulting in high local cont...
Computed tomography (CT) offers detailed insights into the internal anatomy of patients, particularly for spinal vertebrae examination. However, CT scans are associated with higher radiation exposure and cost compared to conventional X-ray imaging. I...
The use of computed tomography (CT) images to correct for photon attenuation in positron emission tomography (PET) produces unbiased patient images, but it is not optimal for synthetic materials. For test objects made from epoxy, image bias and artif...
Nanoparticle drug delivery (NDDS) is a novel system in which the drugs are delivered to the site of action by small particles in the nanometer range. Natural or synthetic polymers are used as vectors in NDDS, as they provide targeted, sustained relea...
Healthcare is one of the crucial aspects of the Internet of things. Connected machine learning-based systems provide faster healthcare services. Doctors and radiologists can also use these systems for collaboration to provide better help to patients....
Machine learning models are renowned for their high dependency on a large corpus of data in solving real-world problems, including the recent COVID-19 pandemic. In practice, data acquisition is an onerous process, especially in medical applications,...
Red blood cells (RBCs) have been found to synthesize and release both nitric oxide (NO) and cyclic guanosine monophosphate (cGMP), contributing to systemic NO bioavailability. These RBC functions resulted impaired in chronic kidney disease (CKD). Thi...
We aimed to evaluate and compare the qualities of synthetic computed tomography (sCT) generated by various deep-learning methods in volumetric modulated arc therapy (VMAT) planning for prostate cancer. Simulation computed tomography (CT) and T2-weigh...
The World Health Organization indicates the top cause of death is heart disease. These diseases can be detected using several imaging modalities, especially cardiac computed tomography (CT), whose images have imperfections associated with noise and c...
Retrograde intrarenal surgery (RIRS) is a minimally invasive endoscopic procedure for the treatment of kidney stones. Traditionally, RIRS is usually performed by reconstructing a 3D model of the kidney from preoperative CT images in order to locate t...
The interpretability of gait analysis studies in people with rare diseases, such as those with primary hereditary cerebellar ataxia (pwCA), is frequently limited by the small sample sizes and unbalanced datasets. The purpose of this study was to asse...
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