Natural interaction on 3D medical image viewer software

Reconstructing and resizing 3D images from DICOM files

Three-dimensional reconstruction of medical images plays a vital role in clinical medicine and medical research. It serves as a powerful tool to help doctors analyze multiple 2D medical images in a 3D perspective. The developed 3D model offers a faster and accurate approach in clinical diagnosis and therapy. Before, the development of 3D reconstruction programs it has always been a challenge to visualize in a 3D perspective but with the advent of powerful computing and graphics processing, medical imaging equipment development is on the rise. The advancement of 3D image reconstruction is not only due to the innovative hardware, but also because of the increase in open-source platforms or libraries, such as visualization toolkit (VTK), that assist on its development to provide an information visualization framework and advanced 3D modeling techniques. VTK is an object-oriented 3D graphics software program that is free to obtain and used both for open- and closed-source applications such as 3D computer graphics, image processing, and visualization. In medical imaging, data from ultrasound systems, magnetic resonance imaging (MRI), and computed tomography (CT) scans can be managed in VTK. Different algorithms of image processing such as color mapping, image re-slicing or resampling, iso-contouring, thresholding, and other algorithms can be utilized to make volume images from the data. Subsequently, data presentation is simplified with the data reconstruction through the use of supplemental software for analysis. VTK library contains a large number of filters, which are multi-threaded to facilitate parallel processing and interpretation. In this paper, recent technologies on the application of VTK for 3D reconstruction of biomedical images will be presented. Moreover, current methodologies will be discussed to clearly understand the different integral steps needed in image data conversion.

BI&T Tell me about the Medical Imaging and Technology Alliance. David Fisher The Medical Imaging & Technology Alliance (MITA), a division of the National Electrical Manufacturers Association (NEMA), is the leading organization and collective voice of medical imaging equipment manufacturers, innovators, and product developers. It represents companies whose sales comprise more than 90 percent of the global market for medical imaging technology. The goals of MITA are to: • Increase the awareness and understanding of the value of medical imaging • Achieve efficient and reasonable regulation of medical imaging technologies • Interact with appropriate government agencies on reimbursement and technology assessment policies • Expand the global acceptance of the digital communications standard (DICOM) that allows digital imaging technologies to interact seamlessly • Improve regulatory harmonization of the global market for medical imaging products • Develop and represent industry positions in technical, trade and other issues • Provide market data unique to this industry

Over the last decade, the specification of a standard format for digital (medical) images has received a great deal of attention. The yearly meetings of the ACC (95 and 96) and ESC (95) were used to promote the DICOM (Digital Imaging and Communications in Medicine) format, the result of a joined effort by the ACC, ACR, NEMA and ESC. At the moment, one of the most interesting questions is: how will the introduction of the DICOM standard affect the daily practice in the catheterization laboratory (cath lab) in the near future?

Three-dimensional reconstruction of medical images can provide the direct, real sense for doctors and play an important role in clinical medicine. The medical images can be reconstructed by using the VTK (visualization toolkit). In this paper, we proposed a new method for medical image reconstruction platform which is developed based on ITK (Insight Segmentation and Registration Toolkit) and VTK. The itkBinaryMask3DMeshSource class is used to extract the surface of DICOM (Digital Imaging and Communications in Medicine) series, and then transform the mesh to the polygon data by the class of itkMeshTovtkPolyData, finally the 3D-visualization outcome will be visual displayed by the VTK. The experiment results demonstrate that the 3D-visualization system based on ITK Surface Extraction is efficiency and satisfy practical applications.

Three-dimensional (3D) medical images are prone to overlap, and there are some problems, such as low detection efficiency and inconsistent with the actual situation. Therefore, a 3D medical image surface reconstruction method based on data mining and machine learning is proposed. The 3D medical images were classified according to different ways, the information frame of 3D medical images was established and the surface overlapping information model of 3D images was given. Based on this information framework, the nonlinear function of overlapping area information of 3D medical images was constructed. The weight of the nonlinear function was used to calculate the input and output results of overlapping area information. Combined with the input mode of 3D medical image information, the error between the information output and the expected output was set. The nonlinear function weight of the overlapping area information of 3D medical images was modified by using the learning rate and the use time of the overlapping area information, and the influence factors of the overlapping information detection were obtained by increasing the situation terms, so as to complete the detection of the surface reconstruction information of 3D medical images. The experimental results show that the information detection results of the proposed method fit well with the actual situation, and the information detection efficiency is high.

Conventional magnetic resonance imaging (MRI) studies depict disease of the human brain in 2D but the reconstruction of a patient’s brain stricken with multiple sclerosis (MS) in 3D using 2D images has not been attempted. Using 3D reconstruction algorithms, we built a 3D printed patient-specific brain model to scale. It is a first of its kind model that depicts the total white matter lesion (WML) load using T2 FLAIR images in an MS patient. The patient images in Digital Imaging and Communications in Medicine (DICOM) format were imported into Mimics inPrint 2.0 (Materialise NV, Leuven, Belgium) a dedicated medical image processing software for the purposes of image segmentation and 3D modeling. The imported axial images were automatically formatted to display coronal and sagittal slices within the software. The imaging study was then segmented into regions and surface rendered to achieve 3D virtual printable files of the desired structures of interest. Rendering brain tumor(s) in 3D has been attempted with the specific intent of extending the options available to a surgeon but no study to our knowledge has attempted to quantify brain disease in MS that has, for all practical purposes, no surgical options.

Medical imaging uses a variety of modalities to provide visual information about a patient. Various methods are used to process this data. Many of them are based on discrete wavelet transform (DWT). Its use will allow effective denoising and compression of 2D and 3D images. This paper proposes a new approach to linear time-invariant wavelet filtering using quantized filter coefficients when using which the computational errors have different signs and allow to partially compensate each other as a result of which the processed image is of high quality. The analysis of the quantization noise of the direct multilevel DWT filter coefficients is carried out. The derived formulas demonstrate the relationship between the quantization accuracy of these coefficients and the processing quality of digital 3D images. The derived formulas for calculating the minimum accuracy of the wavelet filter coefficients representation in the computing devices memory allow minimizing the effect of quantization noise on the result of 3D images processing. Modelling of 3D medical tomographic images DWT processing showed that a decrease in the ratio of the average voxel brightness to the maximum allowable value with increasing color depth of images leads to faster achievement of high quality compared to the results of theoretical analysis with an increase in the value of the scaling degree of the wavelet filter coefficients. The obtained theoretical and practical results open up the possibility for reducing the computational complexity of software and hardware implementation of wavelet processing of 3D medical visual data on modern microelectronic devices (field-programmable gate arrays, application-specific integrated circuits, etc.).

The current state of the art in three dimensional oncologic imaging: An overview

The digital imaging and communications in medicine (DICOM) protocol is nowadays the leading standard for capture, exchange and storage of image data in medical applications. A broad range of commercial, free, and open source software tools supporting a variety of DICOM functionality exists. However, different from patient's care in hospital, DICOM has not yet arrived in electronic data capture systems (EDCS) for clinical trials. Due to missing integration, even just the visualization of patient's image data in electronic case report forms (eCRFs) is impossible. Four increasing levels for integration of DICOM components into EDCS are conceivable, raising functionality but also demands on interfaces with each level. Hence, in this paper, a comprehensive evaluation of 27 DICOM viewer software projects is performed, investigating viewing functionality as well as interfaces for integration. Concerning general, integration, and viewing requirements the survey involves the criteria (i) license, (ii) support, (iii) platform, (iv) interfaces, (v) two-dimensional (2D) and (vi) three-dimensional (3D) image viewing functionality. Optimal viewers are suggested for applications in clinical trials for 3D imaging, hospital communication, and workflow. Focusing on open source solutions, the viewers ImageJ and MicroView are superior for 3D visualization, whereas GingkoCADx is advantageous for hospital integration. Concerning workflow optimization in multi-centered clinical trials, we suggest the open source viewer Weasis. Covering most use cases, an EDCS and PACS interconnection with Weasis is suggested.

Structured Report (SR) is new standard of interchanging diagnostic report between medical devices. SR is encoded as traditional DICOM (Digital Imaging and Communications in Medicine) format. There are two types of SR, which are Basic Text SR and Enhanced SR. Enhanced SR includes more information, such as measurement information, than Basic Text SR. In the system point of view, SR can be classified as Report Creator, Report Repository, Report Manager, Report Reader, External Report Repository Access, and Enterprise Result Repository. Specific system transactions are defined at IHE Year3 Technical Framework (Fig1). This paper is an implementation note for Report Creator and Report Reader of the Basic Text SR. The Report Creator has a tree structure for the user interface. The tree structure is converted to DICOM SR. This DICOM SR can be transferred to Report Manager. Report Reader gets SRs through DICOM SR Query/Retrieve Service. Report Reader saves the SR at local Database. The SR is converted to XML and the XML is combined with XSL to be displayed at the SR Browser. Microsoft Internet Explorer is utilized for the SR Browser.

Mutual information-based 3D image Registration algorithm is shown to be an accurate, robust and more general registration method for medical image processing. However, its potential application in clinic is jeopardized by its high computational costs. Like many other 3D imaging algorithms, 3D registration needs to process vast amount of image data. The intrinsic computing characteristics of the mutual information-based registration algorithm make it even more difficult to manage and cache the input data. In this paper, we introduce our FPGA-based computing platform and detail its application in accelerating the mutual information-based 3D image registration. This platform is designed to accelerate a series of 3D medical imaging algorithms dominated by local operations. It is a System-on-Chip (SoC) architecture with an intelligent data caching system for efficient data fetching and buffering. A reconfigurable multi-pipeline execution unit in the platform is designed to perform high-speed parallel computation. This unit can be customized according to different algorithm requirements to achieve high performance. The simulation results for accelerating the mutual information based registration with our platform show that a speed-up of about 30 can be achieved when compared to a single-CPU computer. Moreover, the platform can be easily reconfigured to accelerate other 3D medical imaging algorithms.

To evaluate the influence of JPEG quality factors 100, 80 and 60 on the reproducibility of identification of cephalometric points on images of lateral cephalograms, compared with the Digital Imaging and Communications in Medicine (DICOM) format. The sample was composed of 30 images of digital lateral cephalograms obtained from 30 individuals (15 males and 15 females) on a phosphor plate system in DICOM format. The images were converted to JPEG with quality factors 100, 80 and 60 with the aid of software, adding up to 90 images. The 120 images (DICOM, JPEG 100, 80 and 60) were blinded and 12 cephalometric points were identified on each image by three calibrated orthodontists, using the x-y coordinate system, on a cephalometric software. The results revealed that identification of cephalometric points was highly reproducible, except for the point Orbitale (Or) on the x-axis. The different file formats did not present a statistically significant difference. JPEG images of lateral cephalograms with quality factors 100, 80 and 60 did not present alterations in the reproducibility of identification of cephalometric points compared with the DICOM format. Good reproducibility was achieved for the 12 points, except for point Or on the x-axis.

We read with interest the Opinion of Day and colleagues regarding speckle-tracking assessment of fetal myocardial deformation1 and we would like to share a few comments. First, figure 1 suggests that the strain is computed for the entire myocardium, as represented by the dotted lines on the image of the septal and lateral walls of the left ventricle. It is not clear if this approach has been used to compute the individual endocardial strain (inner dotted line), the myocardial strain (middle dotted line) or the epicardial strain (outer dotted line), or a sum of all three. The authors should clarify if their results represent a composite measurement of the endocardial, myocardial and epicardial strain, or each of these portions separately. Second, the authors state that ‘In the absence of an electrocardiogram signal (as in fetal echocardiography), data transfer using the Digital Imaging and Communications in Medicine (DICOM) format standard occurs at a frame rate of 25–30 fps [frames per second]’. While this may be true for earlier studies, current ultrasound technology allows the DICOM frames to be acquired and exported at a frame rate equal to the frequency of image acquisition2. The authors cite our review of speckle tracking of the fetal heart (their reference 54) in which we provide an illustration (figure 1) of frame rates acquired with ultrasound machines used in obstetric assessment, which demonstrates frame rates ranging between 50 and 218 per second. These frames were exported from the ultrasound machine in DICOM format. Thus, DICOM export of image frames used in speckle-tracking analysis is not limited to between 25 and 30 fps, but rather, it is possible to achieve frame rates that are equivalent to those obtained during image acquisition2. Third, in another study from our group cited by Day et al. (their reference 37), we found that left ventricular longitudinal strain did not change as a function of gestational age and the mean left ventricular global strain was –22.93%3. In the same paper, we compared our results with the mean and SD values of left ventricular global strain reported in 14 other studies (table 4) and found that, in all but five of them, the delta Z-score values were within 2 SD of our findings. All of the reported studies were performed on dedicated cardiovascular ultrasound machines, while our study used an offline program that only required DICOM clips for analysis3. Lastly, we have reported a number of cardiac measurements derived from speckle-tracking analysis of the right and left ventricular endocardium that analyze ventricular size, shape and contractility2-13. This analysis, a relatively new adaptation of speckle-tracking assessment, enables the examiner to obtain numerous cardiac measurements from a single endocardial tracing of each ventricle, which takes less than 3 min, and is now commercially available on ultrasound platforms dedicated primarily to fetal evaluation. In conclusion, we appreciate the insights of Day and colleagues regarding the benefits and limitations of speckle-tracking assessment of fetal myocardial deformation, however, our experience to date suggests that speckle tracking of the fetal heart has great potential, it demonstrates adequate frame rates, it is reproducible and easy to obtain, and can provide simultaneous measurements of ventricular size, shape and contractility.

Over the past 15 years Digital Imaging and Communications in Medicine (DICOM) has established itself as the international standard for medical image communication. Most medical imaging equipment uses DICOM network and media services to export image data, thus making this standard highly relevant for medical image processing. The first section of this chapter provides a basic introduction into DICOM with its more than 3,600 pages of technical documentation, followed by a section covering selected advanced topics of special interest for medical image processing. The introductory text familiarizes the reader with the standard’s main concepts such as information objects and DICOM media and network services. The rendering pipeline for image display and the concept of DICOM conformance are also discussed. Specialized DICOM services such as advanced image display services that provide means for storing how an image was viewed (“Softcopy Presentation States”) and how multiple images should be aligned on an output device (“Structured Display” and “Hanging Protocols”) are described. We further describe DICOM’s sophisticated approach (“Structured Reporting”) for storing structured documents such as CAD information, which is then covered in more detail. Finally, the last section provides an insight into a newly developed DICOM service called “Application Hosting”, which introduces a standardized plug-in architecture for image processing, thus permitting users to utilize cross-vendor image processing plug-ins in DICOM applications.