Related papers: An analytic model to calculate Voxel S-Values for …
The distribution of absorbed dose in radionuclide therapy with Lu$^{177}$ can be approximated by convolving an image of the time-integrated activity distribution with a dose voxel kernel representing different tissue types. This fast but…
We present a spatially resolved spectroscopic analysis of the Helix Nebula (NGC 7293) using data from the SDSS-V Local Volume Mapper (LVM), by applying the recently developed LVM Data Analysis Pipeline (LVM-DAP). Covering the full optical…
Identifying, measuring and reporting lesions accurately and comprehensively from patient CT scans are important yet time-consuming procedures for physicians. Computer-aided lesion/significant-findings detection techniques are at the core of…
Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume and ejection fraction. In this work, we employ deep learning…
Optimized for pixel fidelity metrics, images compressed by existing image codec are facing systematic challenges when used for visual analysis tasks, especially under low-bitrate coding. This paper proposes a visual analysis-motivated…
Mesh reconstruction of the cardiac anatomy from medical images is useful for shape and motion measurements and biophysics simulations to facilitate the assessment of cardiac function and health. However, 3D medical images are often acquired…
Aims: Projected rotational velocities (\vsini) have been estimated for 334 targets in the VLT-FLAMES Tarantula survey that do not manifest significant radial velocity variations and are not supergiants. They have spectral types from…
Gaussian and discrete non-Gaussian spatial datasets are common across fields like public health, ecology, geosciences, and social sciences. Bayesian spatial generalized linear mixed models (SGLMMs) are a flexible class of models for…
Automating the radiotherapy treatment planning process is a technically challenging problem. The majority of automated approaches have focused on customizing and inferring dose volume objectives to used in plan optimization. In this work we…
We use the multi-epoch K$\rm _S$ band observations, covering a $\sim$5 years baseline to obtain milli and sub-milli arcsec precision astrometry for a sample of eighteen previously known high proper motion sources, including precise…
LiDAR-based fully sparse architecture has garnered increasing attention. FSDv1 stands out as a representative work, achieving impressive efficacy and efficiency, albeit with intricate structures and handcrafted designs. In this paper, we…
One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from…
Objective: Image reconstruction in high resolution PET scanners with depth of interaction (DOI) capability is computationally challenging due to the high sampling in detector and image space. This study evaluates the use of a virtual…
To synthesize diffusion MR measurements from Monte-Carlo simulation using tissue models with sizes comparable to those of scan voxels. Larger regions enable restricting structures to be modeled in greater detail and improve accuracy and…
Accurate and early diagnosis of pneumonia through X-ray imaging is essential for effective treatment and improved patient outcomes. Recent advancements in machine learning have enabled automated diagnostic tools that assist radiologists in…
Recent medical vision-language models (VLMs) have shown promise in 2D medical image interpretation. However extending them to 3D medical imaging has been challenging due to computational complexities and data scarcity. Although a few recent…
Super-Resolution Ultrasound (SRUS) through localizing spatially isolated microbubbles has been demonstrated to overcome the wave diffraction limit and reveal the microvascular structure and flow information at the microscopic scale.…
The availability of digital twins for the cardiovascular system will enable insightful computational tools both for research and clinical practice. This, however, demands robust and well defined models and methods for the different steps…
We introduce the Data Analysis Pipeline (DAP) for the Sloan Digital Sky Survey V (SDSS-V) Local Volume Mapper (LVM) project, referred to as the LVM-DAP. We outline our methods for recovering both stellar and emission line components from…
Recently, machine learning has been successfully applied to model-based left ventricle (LV) segmentation. The general framework involves two stages, which starts with LV localization and is followed by boundary delineation. Both are driven…