Stack Of 2D Slices Research Articles

ARmedViewer, an augmented-reality-based fast 3D reslicer for medical image data on mobile devices: A feasibility study

Background and objective: Medical images obtained by methods such as magnetic resonance imaging (MRI) or computed tomography (CT) are typically displayed as a stack of 2D slices, comprising a 3D volume. Often, the anatomy of interest does not fall neatly into the slice plane but rather extends obliquely through several slices. Reformatting the data to show the anatomy in one slice in conventional medical imaging software can require expertise and time. In this work, we present ARmedViewer, a medical image viewing app designed for mobile devices that uses augmented reality technology to display medical image data. An arbitrary plane for displaying the data can be chosen quickly and intuitively by moving the mobile device.Methods: The app ARmedViewer, compiled for an iOS device, was designed to allow a user to easily select from a list of 3D image datasets consisting of header information and image data. The user decides where to place the data, which can be overlaid on actual human anatomy. After loading the dataset, the user can move and rotate the data as desired. 15 users compared the user experience of the app to a common image viewer by answering two user surveys each, one custom and one standardized. The utility of the app was also tested by having two users find a plane through a 3D dataset that displayed 3 randomly placed lesions. This operation was timed and compared between the app and a standard medical image viewer.Results: ARmedViewer was successfully developed and run on an iPhone XS. User interfaces for selecting, placing, moving, reslicing, and displaying the data were operated with ease, even by naïve users. The custom user survey indicated that freely selecting a slice through the data was significantly more intuitive and easier using the app than using a conventional image viewer on a computer workstation, and changing the viewing angle was also significantly more intuitive. The standardized survey indicated a significantly better user experience for the app in several categories, and never significantly worse. The timed reslicing experiments demonstrated the app being faster than the standard image viewer by an average factor of 9.Conclusions: The newly developed ARmedViewer is a portable software tool for easily displaying 3D medical image data overlaid on human anatomy, allowing for easy choice of the viewing plane by intuitively moving the mobile device.

AUTOMATIC EXTRACTION AND SIGN DETERMINATION OF RESPIRATORY SIGNAL IN REAL-TIME CARDIAC MAGNETIC RESONANCE IMAGING.

In real-time (RT) cardiac cine imaging, a stack of 2D slices is collected sequentially under free-breathing conditions. A complete heartbeat from each slice is then used for cardiac function quantification. The inter-slice respiratory mismatch can compromise accurate quantification of cardiac function. Methods based on principal components analysis (PCA) have been proposed to extract the respiratory signal from RT cardiac cine, but these methods cannot resolve the inter-slice sign ambiguity of the respiratory signal. In this work, we propose a fully automatic sign correction procedure based on the similarity of neighboring slices and correlation to the center-of-mass curve. The proposed method is evaluated in eleven volunteers, with ten slices per volunteer. The motion in a manually selected region-of-interest (ROI) is used as a reference. The results show that the extracted respiratory signal has a high, positive correlation with the reference in all cases. The qualitative assessment of images also shows that the proposed approach can accurately identify heartbeats, one from each slice, belonging to the same respiratory phase. This approach can improve cardiac function quantification for RT cine without manual intervention.

Histograms of Oriented 3D Gradients for Fully Automated Fetal Brain Localization and Robust Motion Correction in 3 T Magnetic Resonance Images.

Fetal brain magnetic resonance imaging (MRI) is a rapidly emerging diagnostic imaging tool. However, automated fetal brain localization is one of the biggest obstacles in expediting and fully automating large-scale fetal MRI processing. We propose a method for automatic localization of fetal brain in 3 T MRI when the images are acquired as a stack of 2D slices that are misaligned due to fetal motion. First, the Histogram of Oriented Gradients (HOG) feature descriptor is extended from 2D to 3D images. Then, a sliding window is used to assign a score to all possible windows in an image, depending on the likelihood of it containing a brain, and the window with the highest score is selected. In our evaluation experiments using a leave-one-out cross-validation strategy, we achieved 96% of complete brain localization using a database of 104 MRI scans at gestational ages between 34 and 38 weeks. We carried out comparisons against template matching and random forest based regression methods and the proposed method showed superior performance. We also showed the application of the proposed method in the optimization of fetal motion correction and how it is essential for the reconstruction process. The method is robust and does not rely on any prior knowledge of fetal brain development.

Automated fetal brain segmentation from 2D MRI slices for motion correction

Motion correction is a key element for imaging the fetal brain in-utero using Magnetic Resonance Imaging (MRI). Maternal breathing can introduce motion, but a larger effect is frequently due to fetal movement within the womb. Consequently, imaging is frequently performed slice-by-slice using single shot techniques, which are then combined into volumetric images using slice-to-volume reconstruction methods (SVR). For successful SVR, a key preprocessing step is to isolate fetal brain tissues from maternal anatomy before correcting for the motion of the fetal head. This has hitherto been a manual or semi-automatic procedure. We propose an automatic method to localize and segment the brain of the fetus when the image data is acquired as stacks of 2D slices with anatomy misaligned due to fetal motion. We combine this segmentation process with a robust motion correction method, enabling the segmentation to be refined as the reconstruction proceeds. The fetal brain localization process uses Maximally Stable Extremal Regions (MSER), which are classified using a Bag-of-Words model with Scale-Invariant Feature Transform (SIFT) features. The segmentation process is a patch-based propagation of the MSER regions selected during detection, combined with a Conditional Random Field (CRF). The gestational age (GA) is used to incorporate prior knowledge about the size and volume of the fetal brain into the detection and segmentation process. The method was tested in a ten-fold cross-validation experiment on 66 datasets of healthy fetuses whose GA ranged from 22 to 39weeks. In 85% of the tested cases, our proposed method produced a motion corrected volume of a relevant quality for clinical diagnosis, thus removing the need for manually delineating the contours of the brain before motion correction. Our method automatically generated as a side-product a segmentation of the reconstructed fetal brain with a mean Dice score of 93%, which can be used for further processing.

Glyph-Based SPECT Visualization for the Diagnosis of Coronary Artery Disease

Myocardial perfusion imaging with single photon emission computed tomography (SPECT) is an established method for the detection and evaluation of coronary artery disease (CAD). State-of-the-art SPECT scanners yield a large number of regional parameters of the left-ventricular myocardium (e.g., blood supply at rest and during stress, wall thickness, and wall thickening during heart contraction) that all need to be assessed by the physician. Today, the individual parameters of this multivariate data set are displayed as stacks of 2D slices, bull's eye plots, or, more recently, surfaces in 3D, which depict the left-ventricular wall. In all these visualizations, the data sets are displayed side-by-side rather than in an integrated manner, such that the multivariate data have to be examined sequentially and need to be fused mentally. This is time consuming and error-prone. In this paper we present an interactive 3D glyph visualization, which enables an effective integrated visualization of the multivariate data. Results from semiotic theory are used to optimize the mapping of different variables to glyph properties. This facilitates an improved perception of important information and thus an accelerated diagnosis. The 3D glyphs are linked to the established 2D views, which permit a more detailed inspection, and to relevant meta-information such as known stenoses of coronary vessels supplying the myocardial region. Our method has demonstrated its potential for clinical routine use in real application scenarios assessed by nuclear physicians.

Use of 2d histograms for volume rendering of multidetector CT data: Development of a graphical user interface 1

Rationale and Objectives Direct volume rendering reveals 3D information on anatomic structures without preprocessing the data. This increases the interest in this technique as a diagnostic tool. A fast and simple method for setting transfer functions is crucial for clinical routine work. However, this is still a complex task. Present commercial workstations are usually limited to design galleries and window/level functionality. Materials and Methods We present a graphical user interface for volume rendering of multidetector row CT data that permits a much more flexible specification of rendering parameters. A 2D histogram of CT density versus gradient magnitude facilitates the understanding of the spatial connections of different tissues. The incorporation of gradient magnitude into the transfer function domain allows discrimination of features of interest that are not distinguishable on CT density alone. Penetration length, color, and gradient magnitude are depicted on a stack of 2D slices according to the settings of the opacity transfer function and the viewing direction. A gallery of thumbnails with presets of transfer functions is interactively adapted if the volume is rotated or cropped. Results This allows for fast evaluation of numerous rendering protocols at once. The interface was evaluated with CT data covering skeletal trauma, pathologies of the thorax/abdomen, and CT angiography. Conclusion We observed that high-quality visualizations could be obtained with reasonable interaction times. The 2D histogram and penetration length displays provided valuable insight into the dataset that made the specification of transfer functions a goal-oriented process.