A Survey of Perceptually Motivated 3D Visualization of Medical Image Data

Multi‐Light Image Collections (MLICs), i.e., stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination, provide large amounts of visual and geometric information. In this survey, we provide an up‐to‐date integrative view of MLICs as a mean to gain insight on objects through the analysis and visualization of the acquired data. After a general overview of MLICs capturing and storage, we focus on the main approaches to produce representations usable for visualization and analysis. In this context, we first discuss methods for direct exploration of the raw data. We then summarize approaches that strive to emphasize shape and material details by fusing all acquisitions in a single enhanced image. Subsequently, we focus on approaches that produce relightable images through intermediate representations. This can be done both by fitting various analytic forms of the light transform function, or by locally estimating the parameters of physically plausible models of shape and reflectance and using them for visualization and analysis. We finally review techniques that improve object understanding by using illustrative approaches to enhance relightable models, or by extracting features and derived maps. We also review how these methods are applied in several, main application domains, and what are the available tools to perform MLIC visualization and analysis. We finally point out relevant research issues, analyze research trends, and offer guidelines for practical applications.

Section: Discussionmentioning

confidence: 99%

State‐of‐the‐art in Multi‐Light Image Collections for Surface Visualization and Analysis

Pintus

Dulecha

Ciortan

et al. 2019

“…Brain tumors may additionally require Diffusion Tensor imaging (DTI) [MBC15]. Details on each modality can be found in recent surveys [LSBP18, PBC*16]. During this step, the planning CT is acquired, a high quality CT which plays an important role in the planning.…”

Section: Radiation Therapy Planning: Workflow Data and Usersmentioning

confidence: 99%

“…Single modality may not provide enough information with respect to tumor tissues, as well as the tissues that surround target organs. Combining different modality images can be a necessary tool in cancer treatment [LSBP18, PBC*16]. The specifics of the integration and combination of various channels of data information is done through multi‐modality image fusion.…”

Section: Taxonomy and Presentation Of Previous Work In Vc For Rtmentioning

confidence: 99%

State‐of‐the‐Art Report: Visual Computing in Radiation Therapy Planning

Schlachter

Raidou

Muren

et al. 2019

Radiation therapy (RT) is one of the major curative approaches for cancer. It is a complex and risky treatment approach, which requires precise planning, prior to the administration of the treatment. Visual Computing (VC) is a fundamental component of RT planning, providing solutions in all parts of the process—from imaging to delivery. Despite the significant technological advancements of RT over the last decades, there are still many challenges to address. This survey provides an overview of the compound planning process of RT, and of the ways that VC has supported RT in all its facets. The RT planning process is described to enable a basic understanding in the involved data, users and workflow steps. A systematic categorization and an extensive analysis of existing literature in the joint VC/RT research is presented, covering the entire planning process. The survey concludes with a discussion on lessons learnt, current status, open challenges, and future directions in VC/RT research.

“…Various methods have been presented during the last decade for providing high‐quality illustrative and non‐photorealistic rendering of volume data in order to highlight structures and features of interest. We refer the reader to classic surveys for a coverage of illumination methods applied to volume rendering [JSYR12, DRN*17] and of perceptually motivated methods for visualization of medical volume data [PBC*16]. With respect to methods for highlighting structures and functional activity in volume data, Nguyen et al [NOE*10] proposed a method for concurrent volume visualization of functional Magnetic Resonance Imaging (fMRI), in which the fMRI signal is treated as light emission when rendering a patient‐specific high resolution reference MRI volume, and rendered as light glowing from active regions.…”

Section: Related Workmentioning

confidence: 99%

Interactive Volumetric Visual Analysis of Glycogen‐derived Energy Absorption in Nanometric Brain Structures

Agus

Calì

Al-Awami

et al. 2019

Digital acquisition and processing techniques are changing the way neuroscience investigation is carried out. Emerging applications range from statistical analysis on image stacks to complex connectomics visual analysis tools targeted to develop and test hypotheses of brain development and activity. In this work, we focus on neuroenergetics, a field where neuroscientists analyze nanoscale brain morphology and relate energy consumption to glucose storage in form of glycogen granules. In order to facilitate the understanding of neuroenergetic mechanisms, we propose a novel customized pipeline for the visual analysis of nanometric‐level reconstructions based on electron microscopy image data. Our framework supports analysis tasks by combining i) a scalable volume visualization architecture able to selectively render image stacks and corresponding labelled data, ii) a method for highlighting distance‐based energy absorption probabilities in form of glow maps, and iii) a hybrid connectivitybased and absorption‐based interactive layout representation able to support queries for selective analysis of areas of interest and potential activity within the segmented datasets. This working pipeline is currently used in a variety of studies in the neuroenergetics domain. Here, we discuss a test case in which the framework was successfully used by domain scientists for the analysis of aging effects on glycogen metabolism, extracting knowledge from a series of nanoscale brain stacks of rodents somatosensory cortex.