Search results

The purpose of this paper is to investigate the relationship between hospital adoption and use of computed tomography (CT) scanners, and magnetic resonance imaging (MRI) machines and in-patient mortality and length of stay.

This study used panel data (2007–2010) from 124 hospital corporations operating in Ontario, Canada. Imaging use focused on medical patients accounting for 25 percent of hospital discharges. Main outcomes were in-hospital mortality rates and average length of stay. A model for each outcome-technology combination was built, and controlled for hospital structural characteristics, market factors and patient characteristics.

In 2010, 36 and 59 percent of hospitals had adopted MRI machines and CT scanners, respectively. Approximately 23.5 percent of patients received CT scans and 3.5 percent received MRI scans during the study period. Adoption of these technologies was associated with reductions of up to 1.1 percent in mortality rates and up to 4.5 percent in length of stay. The imaging use–mortality relationship was non-linear and varied by technology penetration within hospitals. For CT, imaging use reduced mortality until use reached 19 percent in hospitals with one scanner and 28 percent in hospitals with 2+ scanners. For MRI, imaging use was largely associated with decreased mortality. The use of CT scanners also increased length of stay linearly regardless of technology penetration (4.6 percent for every 10 percent increase in use). Adoption and use of MRI was not associated with length of stay.

These results suggest that there may be some unnecessary use of imaging, particularly in small hospitals where imaging is contracted out. In larger hospitals, the results highlight the need to further investigate the use of imaging beyond certain thresholds. Independent of the rate of imaging use, the results also indicate that the presence of CT and MRI devices within a hospital benefits quality and efficiency.

To the authors’ knowledge, this study is the first to investigate the combined effect of adoption and use of medical imaging on outcomes specific to CT scanners and MRI machines in the context of hospital in-patient care.

This paper aims to provide image repository to the medical professional in an open source platform, which will increase the visibility of Digital Imaging and Communication in Medicine (DICOM) image in a network mode; further, the proposed system will reduce the storage cost of the images to significant level.

The authors have developed a new institutional repository model for the medical professionals cum radiologists to preserve, store and retrieve medical images from one database with the help of open source software. The authors used JavaScript programming to integrate and develop the DICOM Standard with DSpace.

Major outcome of this work is that DICOM images can be accommodated in DSpace without modifying the image properties and keeping intact the various dimensions of image viewing options. Further, it was found that the images are retrieved without any ease because of the robust indexing system.

Major limitation of this study was the size of the data (5000 DICOM image) with which the authors have tested the system. The scalability of the system has to be tested on various fronts, for which separate study has to be done.

Once this system is in place, DICOM user can store, retrieve and access the image from Web platform. This proposed repository will be the storehouse of various DICOM images with reasonable storage costs.

In addition to exploring the opportunities of open source software (OSS) implementation in Medical Fields, this study includes issues related to implementation of open source repository for storing and preserving medical image. This is the first time in Library Science field to create and develop Open Source DICOM Medical Image Library with the help of DSpace. The study will create value for library professionals as well as medical professionals and OSS vendors to understand the medical market in the context of OSS.

The aim of this article is to show how Taguchi methods can be applied to health care to improve the quality of medical images. Quality is often integrated with the performance and parameters of the design of medical applications. Many imaging methods can be designed by setting the correct combination of parameters and estimating the contribution of individual quality influencing factors by means of incorporating parameter design and orthogonal arrays. The performance of any imaging equipment can be measured by signal‐to‐noise ratio. This inherent index can give a sense of how close the performance is to the ideal.

Data were collected from a database of 82 diagnostic thoracic computed tomography (CT) scans. Signal‐to‐noise ratios (S/N) were calculated.

Given the S/N's, the best CT level was found to be level 4.

To reduce bias resulting from the observer's readings, robust equipments should be designed incorporating Taguchi's experimental design. Further work is needed to establish imaging protocols and new hardware design.

This paper aims to present a prototype of medical transportation robot whose positioning accuracy can reach millimeter-level in terms of patient transportation. By using this kind of mobile robot, a fully automatic image diagnosis process among independent CT/PET devices and the image fusion can be achieved.

Following a short introduction, a large-load 4WD-4WS (four-wheel driving and four-wheel steering) mobile robot for carrying patient among multiple medical imaging equipments is developed. At the same time, a specially designed bedplate with self-locking function is also introduced. For further improving the positioning accuracy, the authors proposed a calibration method based on Gaussian process regression (GPR) to process the measuring data of the sensors. The performance of this robot is verified by the calibration experiment and Image fusion experiment. Finally, concluding comments are drawn.

By calibrating the robot’s positioning system through the proposed GPR method, one can obtain the accuracy of the robot’s offset distance and deflection angle, which are 0.50 mm and +0.21°, respectively. Independent repeated trials were then set up to verify this result. Subsequent phantom experiment shows the accuracy of image fusion can be accurate within 0.57 mm in the front-rear direction and 0.83 in the left-right direction, respectively, while the clinical experiment shows that the proposed robot can practically realize the transportation of patient and image fusion between multiple imaging diagnosis devices.

The proposed robot offers an economical image fusion solution for medical institutions whose imaging diagnosis system basically comprises independent MRI, CT and PET devices. Also, a fully automatic diagnosis process can be achieved so that the patient’s suffering of getting in and out of the bed and the doctor’s radiation dose can be obviated.

The general bedplate presented in Section 2 that can be mounted on the CT and PET devices and the self-locking mechanism has realized the catching and releasing motion of the patient on different medical devices. They also provide a detailed method regarding patient handling and orientation maintenance, which was hardly mentioned in previous research. By establishing the positioning system between the robot and different medical equipment, a fully automatic diagnosis process can be achieved so that the patient’s suffering of getting in and out of the bed and the doctor’s radiation dose can be obviated.

The GPR-based method proposed in this paper offers a novel method for enhancing the positioning accuracy of the industrial AGV while the transportation robot proposed in this paper also offers a solution for modern imaging fusion diagnosis, which are basically predicated on the conjoint analysis between different kinds of medical devices.

Reviews some of the improvements in image sensor technology that are yielding applications in the medical field.

Discusses the characteristics and gives examples of cameras and imaging sensors used in endoscopy, microscopy, pharmaceutical label inspection and X‐radiography. Reviews some innovative camera‐based products for endoscopy, skin imaging and health monitoring.

Improvements in camera resolution, miniaturisation and interfacing are widening the applications in medical imaging and enabling the development of some exciting new products addressing the needs of patients and medical staff.

Identifies some suppliers of medical imaging devices and their applications.

Gliomas are common intracranial tumors with the characteristic of diffuse and invasive growth. The prognosis is poor, and the recurrence rate and mortality are higher. With the development of big data technology, many methods such as natural language processing, computer vision and image processing have been deeply applied in the medical field. This can help clinicians to provide personalized and precise diagnosis and therapeutic schedule for patients with different type of gliomas to achieve the best therapeutic effect. The purpose of this paper is to summarize and extract useful information from published research results by conducting a secondary analysis of the literature.

The PubMed and China National Knowledge Infrastructure (CNKI) literature database were used to retrieve published Chinese and English research papers about human gliomas. Comprehensive analysis was applied to conduct this research. The factors affecting survival and prognosis were screened and analyzed respectively in this paper, and different methods for multidimensional data of patients were discussed.

This paper identified biomarkers and therapeutic modalities associated with prognosis for different grade of gliomas. This paper investigated the relationship among these clinical prognostic factors and different histopathologic tying and grade of gliomas by comprehensive analysis. This paper summarizes the research progress of biomarker in medical imaging and genomics of gliomas to improve prognosis and the current status of treatment in China.

Combined with multimodal data such as genomics data, medical image data and clinical information data, this paper comprehensively analyzed the prognostic factors of glioma and provided guidance and evidence for rational treatment planning and improvement of clinical treatment prognosis.

Today, medical models can be made by the use of medical imaging systems through modern image processing methods and rapid prototyping (RP) technology. In ultrasound imaging systems, as images are not layered and are of lower quality as compared to those of computerized tomography (CT) and magnetic resonance imaging (MRI), the process for making physical models requires a series of intermediate processes and it is a challenge to fabricate a model using ultrasound images due to the inherent limitations of the ultrasound imaging process. The purpose of this paper is to make high quality, physical models from medical ultrasound images by combining modern image processing methods and RP technology.

A novel and effective semi‐automatic method was developed to improve the quality of 2D image segmentation process. In this new method, a partial histogram of 2D images was used and ideal boundaries were obtained. A 3D model was achieved using the exact boundaries and then the 3D model was converted into the stereolithography (STL) format, suitable for RP fabrication. As a case study, the foetus was chosen for this application since ultrasonic imaging is commonly used for foetus imaging so as not to harm the baby. Finally, the 3D Printing (3DP) and PolyJet processes, two types of RP technique, were used to fabricate the 3D physical models.

The physical models made in this way proved to have sufficient quality and shortened the process time considerably.

It is still a challenge to fabricate an exact physical model using ultrasound images. Current commercial histogram‐based segmentation method is time‐consuming and results in a less than optimum 3D model quality. In this research work, a novel and effective semi‐automatic method was developed to select the threshold optimum value easily.

This paper aims to present a novel DSpace-based medical image repository system planned explicitly for storing and retrieving clinical images using digital imaging and communication in medicine (DICOM) metadata standards. DSpace institutional repository software is widely used in an academic environment for accessing and mainly storing text-related files. DICOM images are particular types of images embedded with much system-generated metadata and organised using DICOM metadata standards.

The present paper talks about institutional repository software (DSpace) in archiving DICOM images. In the current study, the authors have tried to integrate the DICOM metadata standard with DSpace, which was compatible with Dublin Core (DC) and open archives initiative – protocol for metadata harvesting (OAI-PMH). After combining the DICOM standard with DSpace and the repository tested with a sample of 5,000 images, the retrieval results using various DICOM tags was very satisfactory. This study paves for the use of open source software (OSS) in storing and retrieving medical images.

The author has provided the DSpace software to recognised DICOM (.dcm) files in the first stage. In the second stage, a patch was developed to identify the DICOM metadata standard in Dspace, which has inbuilt DC metadata standards. Finally, in the third stage, retrieval efficiency was tested with a 5,000 .dcm image using the DICOM tag and the results were very fruitful.

A major limitation of this study was the size of the data (5,000 DICOM images) with which the authors have tested the system. The system scalability has to be tested on various fronts like on cloud and local servers with different configurations, for which a separate study has to be done.

Once this system is in place, DICOM users can stock, retrieve and access the image from the Web platform. Furthermore, this proposed repository will be the warehouse of various DICOM images with reasonable storage costs.

In addition to exploring the opportunities of free open source software (FOSS) implementation in medical science, this study includes issues related to the performance of an open-source repository for retrieving and preserving medical images. It created and developed Open Source DICOM Medical Image Library with DICOM metadata standard with the help of DSpace. Thus, the study will generate value for library professionals and medical professionals and FOSS vendors to understand the medical market in the context of FOSS.

Aims to investigate medical rapid prototyping (medical RP) technology applications and methods based on reverse engineering (RE) and medical imaging data.

Medical image processing and RE are applied to construct three‐dimensional models of anatomical structures, from which custom‐made (personalized) medical applications are developed.

The investigated methods were successfully used for design and manufacturing of biomodels, surgical aid tools, implants, medical devices and surgical training models. More than 40 medical RP applications were implemented in Europe and Asia since 1999.

Medical RP is a multi‐discipline area. It involves in many human resources and requires high skills and know‐how in both engineering and medicine. In addition, medical RP applications are expensive, especially for low‐income countries. These practically limit its benefits and applications in hospitals.

In order to transfer medical RP into hospitals successfully, a good link and close collaboration between medical and engineering sites should be established. Moreover, new medical applications should be developed in the way that does not change the traditional approaches that medical doctors (MD) were trained, but provides solutions to improve the diagnosis and treatment quality.

The presented state‐of‐the‐art medical RP is applied for diagnosis and treatment in the following medical areas: cranio‐maxillofacial and dental surgery, neurosurgery, orthopedics, orthosis and tissue engineering. The paper is useful for MD (radiologists and surgeons), biomedical and RP/CAD/CAM engineers.

The purpose of this paper is to investigate how healthcare professionals search for and select the medical images they need within medical settings.

In total, 29 healthcare professionals participated in the study. Using a think‐aloud technique and face‐to‐face interviews, the authors asked participants to explain how they looked for medical images and how they judged the relevancy of retrieved images.

A total of 15 criteria were applied by participants when determining the relevance of medical images in relation to their information needs. Topicality was found to be the primary and most important criterion used by participants.

This study compares the relevance criteria used for medical images with those identified in the literature and highlights six additional criteria that have not been suggested in previous work.