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Time-resolved high-resolution X-ray Computed Tomography (4D $\mu$CT) is an imaging technique that offers insight into the evolution of dynamic processes inside materials that are opaque to visible light. Conventional tomographic…
A human body digital twin (DT) is a virtual representation of an individual's physiological state, created using real-time data from sensors and medical test devices, with the purpose of simulating, predicting, and optimizing health…
We are releasing a dataset of diagram drawings with dynamic drawing information. The dataset aims to foster research in interactive graphical symbolic understanding. The dataset was obtained using a prompted data collection effort.
This article is about an image transform called 3D-DCT, or three-dimensional discrete cosine transform. This is an extension of the well-known 1D and 2D-DCT, which is extensively used, mostly in multimedia coding. A modification of 1D-DCT…
Digitisation of natural history collections not only preserves precious information about biological diversity, it also enables us to share, analyse, annotate and compare specimens to gain new insights. High-resolution, full-colour 3D…
We developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye tracking system while a radiologist reviewed and reported on 1,083 CXR images. The dataset…
Chaotic microcavities play a crucial role in several research areas, including the study of unidirectional microlasers, nonlinear optics, sensing, quantum chaos, and non-Hermitian physics. To date, most theoretical and experimental…
Medical imaging modalities have revolutionized health-care approaches by offering a better understanding of the human anatomy. Discovery of x-rays allowed the exploiting of the micro-scaled information of human anatomy. Computed tomography…
Deep learning based Computer Aided Diagnosis (CAD) systems have been developed to treat breast ultrasound. Most of them focus on a single ultrasound imaging modality, either using representative static images or the dynamic video of a…
Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However,…
We review the available atomic data used for interpreting and modeling X-ray observations. The applications for these data can be divided into several levels of detail, ranging from compilations which can be used with direct inspection of…
Computed Tomography (CT) has been widely adopted in medicine and it is increasingly being used in scientific and industrial applications. Parallelly, research in different mathematical areas concerning discrete inverse problems has led to…
Computed tomography (CT) is a beneficial imaging tool for diagnostic purposes. CT scans provide detailed information concerning the internal anatomic structures of a patient, but present higher radiation dose and costs compared to X-ray…
Publicly available, large-scale medical imaging datasets are crucial for developing and validating artificial intelligence models and conducting retrospective clinical research. However, datasets that combine functional and anatomical…
Here we describe a system for personal and professional management and analysis of bio-medical images captured using off-the-shelf, consumer-grade imaging devices such as scanners, digital cameras, cellphones, webcams and tablet PCs.…
Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoising and pure end-to-end reconstruction…
Medical imaging is an essential tool in many areas of medical applications, used for both diagnosis and treatment. However, reading medical images and making diagnosis or treatment recommendations require specially trained medical…
This study integrates PET metabolic information with CT anatomical structures to establish a 3D multimodal segmentation dataset for lymphoma based on whole-body FDG PET/CT examinations, which bridges the gap of the lack of standardised…
In this article, we provide the appendix for Lifelong Generative Modelling Using Dynamic Expansion Graph Model. This appendix includes additional visual results as well as the numerical results on the challenging datasets. In addition, we…
One of the key impediments for developing and assessing robust medical imaging algorithms is limited access to large-scale datasets with suitable annotations. Synthetic data generated with plausible physical and biological constraints may…