As hospitals move towards providing in-house 24 × 7 services, there is an increasing need for information systems to be available around the clock. This study investigates one organization’s need for a workflow continuity solution that provides around the clock availability for information systems that do not provide highly available services. The organization investigated is a large multifacility healthcare organization that consists of 20 hospitals and more than 30 imaging centers. A case analysis approach was used to investigate the organization’s efforts. The results show an overall reduction in downtimes where radiologists could not continue their normal workflow on the integrated Picture Archiving and Communications System (PACS) solution by 94 % from 2008 to 2011. The impact of unplanned downtimes was reduced by 72 % while the impact of planned downtimes was reduced by 99.66 % over the same period. Additionally more than 98 h of radiologist impact due to a PACS upgrade in 2008 was entirely eliminated in 2011 utilizing the system created by the workflow continuity approach. Workflow continuity differs from high availability and business continuity in its design process and available services. Workflow continuity only ensures that critical workflows are available when the production system is unavailable due to scheduled or unscheduled downtimes. Workflow continuity works in conjunction with business continuity and highly available system designs. The results of this investigation revealed that this approach can add significant value to organizations because impact on users is minimized if not eliminated entirely.
Workflow continuity; Business continuity; PACS planning; PACS integration; PACS downtime procedures; PACS administration; PACS; PACS service; Software design; Systems integration; Workflow; Productivity; Management information systems; Information system; Image retrieval; Health level 7 (HL7); Efficiency
Surgeons use information from multiple sources when making surgical decisions. These include volumetric datasets (such as CT, PET, MRI, and their variants), 2D datasets (such as endoscopic videos), and vector-valued datasets (such as computer simulations). Presenting all the information to the user in an effective manner is a challenging problem. In this paper, we present a visualization approach that displays the information from various sources in a single coherent view. The system allows the user to explore and manipulate volumetric datasets, display analysis of dataset values in local regions, combine 2D and 3D imaging modalities and display results of vector-based computer simulations. Several interaction methods are discussed: in addition to traditional interfaces including mouse and trackers, gesture-based natural interaction methods are shown to control these visualizations with real-time performance. An example of a medical application (medialization laryngoplasty) is presented to demonstrate how the combination of different modalities can be used in a surgical setting with our approach.
Volume visualization; Human–computer interaction; Volume rendering; Image-guided surgery
This study evaluated a method to maintain the optimal image quality in clinical practice for image quality management in a picture archiving and communication system (PACS) that uses typical technology for digital medical images. This study conducted a survey of 25 hospitals in Seoul and metropolitan areas that had installed PACS to examine the reality of image quality management. Sixteen diagnostic monitors were used as calibration tools to compare and analyze the external illuminance uniformity and grayscale standard display function (GSDF) values at each frequency. According to the survey results, most of the hospitals did not have any particular rules or standardized methods for image quality control. In a PACS, the calibration frequency was examined within the allowable limits of error for each week and month. The calibration was not affected by the difference in brightness of the environment for reading an image. The GSDF measurement values were quite different from the standard values. In conclusion, to improve the image quality of the digital system, it is important to make good use of the system and maintain the image quality. Therefore, it is critical to capitalize on the method suggested in this study and maintain the optimal image quality to guarantee a high level of observer satisfaction.
PACS; Korean hospital; Image quality
This paper presents an adaptive denoising approach aiming to improve the visibility and detectability of hemorrhage from brain computed tomography (CT) images. The suggested approach fuses the images denoised by total variation (TV) method, denoised by curvelet-based method, and edge information extracted from the noise residue of TV method. The edge information is extracted from the noise residue of TV method by processing it through curvelet transform. The visual interpretation shows that the proposed approach not only reduces the staircase effect caused by total variation method but also reduces visual distortion induced by curvelet transform in the homogeneous areas of the CT images. The denoising abilities of the proposed method are further evaluated by segmenting the hemorrhagic brain area using region-growing method. The sensitivity, specificity, Jaccard index, and Dice coefficients were calculated for different noise levels. The comparative results show that the significant improvement has yielded in the brain hemorrhage detection from CT images after denoising it with the proposed approach.
Curvelet transform; Total variation; Computed tomography
This paper is aimed at developing and evaluating a content-based retrieval method for contrast-enhanced liver computed tomographic (CT) images using bag-of-visual-words (BoW) representations of single and multiple phases. The BoW histograms are extracted using the raw intensity as local patch descriptor for each enhance phase by densely sampling the image patches within the liver lesion regions. The distance metric learning algorithms are employed to obtain the semantic similarity on the Hellinger kernel feature map of the BoW histograms. The different visual vocabularies for BoW and learned distance metrics are evaluated in a contrast-enhanced CT image dataset comprised of 189 patients with three types of focal liver lesions, including 87 hepatomas, 62 cysts, and 60 hemangiomas. For each single enhance phase, the mean of average precision (mAP) of BoW representations for retrieval can reach above 90 % which is significantly higher than that of intensity histogram and Gabor filters. Furthermore, the combined BoW representations of the three enhance phases can improve mAP to 94.5 %. These preliminary results demonstrate that the BoW representation is effective and feasible for retrieval of liver lesions in contrast-enhanced CT images.
Content-based image retrieval; Bag of visual words; Contrast-enhanced CT; Liver lesion; Distance metric learning
This study aims to evaluate the utility of compressed computed tomography (CT) studies (to expedite transmission) using Motion Pictures Experts Group, Layer 4 (MPEG-4) movie formatting in combat hospitals when guiding major treatment regimens. This retrospective analysis was approved by Walter Reed Army Medical Center institutional review board with a waiver for the informed consent requirement. Twenty-five CT chest, abdomen, and pelvis exams were converted from Digital Imaging and Communications in Medicine to MPEG-4 movie format at various compression ratios. Three board-certified radiologists reviewed various levels of compression on emergent CT findings on 25 combat casualties and compared with the interpretation of the original series. A Universal Trauma Window was selected at −200 HU level and 1,500 HU width, then compressed at three lossy levels. Sensitivities and specificities for each reviewer were calculated along with 95 % confidence intervals using the method of general estimating equations. The compression ratios compared were 171:1, 86:1, and 41:1 with combined sensitivities of 90 % (95 % confidence interval, 79–95), 94 % (87–97), and 100 % (93–100), respectively. Combined specificities were 100 % (85–100), 100 % (85–100), and 96 % (78–99), respectively. The introduction of CT in combat hospitals with increasing detectors and image data in recent military operations has increased the need for effective teleradiology; mandating compression technology. Image compression is currently used to transmit images from combat hospital to tertiary care centers with subspecialists and our study demonstrates MPEG-4 technology as a reasonable means of achieving such compression.
Compression; MPEG4; Universal trauma window; Combat trauma
The development cycle of an image-guided surgery navigation system is too long to meet current clinical needs. This paper presents an integrated system developed by the integration of two open-source software (IGSTK and MITK) to shorten the development cycle of the image-guided surgery navigation system and save human resources simultaneously. An image-guided surgery navigation system was established by connecting the two aforementioned open-source software libraries. It used the Medical Imaging Interaction Toolkit (MITK) as a framework providing image processing tools for the image-guided surgery navigation system of medical imaging software with a high degree of interaction and used the Image-Guided Surgery Toolkit (IGSTK) as a library that provided the basic components of the system for location, tracking, and registration. The electromagnetic tracking device was used to measure the real-time position of surgical tools and fiducials attached to the patient’s anatomy. IGSTK was integrated into MITK; at the same time, the compatibility and the stability of this system were emphasized. Experiments showed that an integrated system of the image-guided surgery navigation system could be developed in 2 months. The integration of IGSTK into MITK is feasible. Several techniques for 3D reconstruction, geometric analysis, mesh generation, and surface data analysis for medical image analysis of MITK can connect with the techniques for location, tracking, and registration of IGSTK. This integration of advanced modalities can decrease software development time and emphasize the precision, safety, and robustness of the image-guided surgery navigation system.
MITK; IGSTK; Image-guided surgery navigation; Development cycle; Open-source software; Integration
The use of color LCDs in medical imaging is growing as more clinical specialties use digital images as a resource in diagnosis and treatment decisions. Telemedicine applications such as telepathology, teledermatology, and teleophthalmology rely heavily on color images. However, standard methods for calibrating, characterizing, and profiling color displays do not exist, resulting in inconsistent presentation. To address this, we developed a calibration, characterization, and profiling protocol for color-critical medical imaging applications. Physical characterization of displays calibrated with and without the protocol revealed high color reproduction accuracy with the protocol. The present study assessed the impact of this protocol on observer performance. A set of 250 breast biopsy virtual slide regions of interest (half malignant, half benign) were shown to six pathologists, once using the calibration protocol and once using the same display in its “native” off-the-shelf uncalibrated state. Diagnostic accuracy and time to render a decision were measured. In terms of ROC performance, Az (area under the curve) calibrated = 0.8570 and Az uncalibrated = 0.8488. No statistically significant difference (p = 0.4112) was observed. In terms of interpretation speed, mean calibrated = 4.895 s; mean uncalibrated = 6.304 s which is statistically significant (p = 0.0460). Early results suggest a slight advantage diagnostically for a properly calibrated and color-managed display and a significant potential advantage in terms of improved workflow. Future work should be conducted using different types of color images that may be more dependent on accurate color rendering and a wider range of LCDs with varying characteristics.
Color displays; Diagnostic accuracy; Color calibration; Color management; Pathology
The objective of this study is to assess the impact on nodule detection and efficiency using a computer-aided detection (CAD) device seamlessly integrated into a commercially available picture archiving and communication system (PACS). Forty-eight consecutive low-dose thoracic computed tomography studies were retrospectively included from an ongoing multi-institutional screening study. CAD results were sent to PACS as a separate image series for each study. Five fellowship-trained thoracic radiologists interpreted each case first on contiguous 5 mm sections, then evaluated the CAD output series (with CAD marks on corresponding axial sections). The standard of reference was based on three-reader agreement with expert adjudication. The time to interpret CAD marking was automatically recorded. A total of 134 true-positive nodules, measuring 3 mm and larger were included in our study; with 85 ≥ 4 and 50 ≥ 5 mm in size. Readers detection improved significantly in each size category when using CAD, respectively, from 44 to 57 % for ≥3 mm, 48 to 61 % for ≥4 mm, and 44 to 60 % for ≥5 mm. CAD stand-alone sensitivity was 65, 68, and 66 % for nodules ≥3, ≥4, and ≥5 mm, respectively, with CAD significantly increasing the false positives for two readers only. The average time to interpret and annotate a CAD mark was 15.1 s, after localizing it in the original image series. The integration of CAD into PACS increases reader sensitivity with minimal impact on interpretation time and supports such implementation into daily clinical practice.
Radiographic image interpretation; Computer-assisted; Radiography; Thoracic; PACS reading; Clinical workflow; Lung; Efficiency; Computed tomography; Computer-assisted detection; Chest CT
The Certification for Imaging Informatics Professionals (CIIP) program is sponsored by the Society of Imaging Informatics in Medicine and the American Registry of Radiologic Technologists through the American Board of Imaging Informatics. In 2005, a survey was conducted of radiologists, technologists, information technology specialists, corporate information officers, and radiology administrators to identify the competencies and skill set that would define a successful PACS administrator. The CIIP examination was created in 2007 in response to the need for an objective way to test for such competencies, and there have been 767 professionals who have been certified through this program to date. The validity of the psychometric integrity of the examination has been previously established. In order to further understand the impact and future direction of the CIIP certification on diplomats, a survey was conducted in 2010. This paper will discuss the results of the survey.
PACS administration; Informatics training; Medical informatics applications
In this paper, a novel lesion segmentation within breast ultrasound (BUS) image based on the cellular automata principle is proposed. Its energy transition function is formulated based on global image information difference and local image information difference using different energy transfer strategies. First, an energy decrease strategy is used for modeling the spatial relation information of pixels. For modeling global image information difference, a seed information comparison function is developed using an energy preserve strategy. Then, a texture information comparison function is proposed for considering local image difference in different regions, which is helpful for handling blurry boundaries. Moreover, two neighborhood systems (von Neumann and Moore neighborhood systems) are integrated as the evolution environment, and a similarity-based criterion is used for suppressing noise and reducing computation complexity. The proposed method was applied to 205 clinical BUS images for studying its characteristic and functionality, and several overlapping area error metrics and statistical evaluation methods are utilized for evaluating its performance. The experimental results demonstrate that the proposed method can handle BUS images with blurry boundaries and low contrast well and can segment breast lesions accurately and effectively.
Breast neoplasm; Image segmentation; Ultrasound; Cellular automata
In part one of this series, best practices were described for acquiring and handling data at study sites and importing them into an image repository or database. Here, we present a similar treatment on data management practices for imaging-based studies.
Clinical trials; Research image database
Mammography is the most efficient technique for detecting and diagnosing breast cancer. Clusters of microcalcifications have been mainly targeted as a reliable early sign of breast cancer and their earliest detection is essential to reduce the probability of mortality rate. Since the size of microcalcifications is very tiny and may be overlooked by the observing radiologist, we have developed a Computer Aided Diagnosis system for automatic and accurate cluster detection. A three-phased novel approach is presented in this paper. Firstly, regions of interest that corresponds to microcalcifications are identified. This can be achieved by analyzing the bandpass coefficients of the mammogram image. The suspicious regions are passed to the second phase, in which the nodular structured microcalcifications are detected based on eigenvalues of second order partial derivatives of the image and microcalcification pixels are segmented out by exploiting the foveal segmentation in multiscale analysis. Finally, by combining the responses coming out from the second order partial derivatives and the foveal method, potential microcalcifications are detected. The detection performance of the proposed method has been evaluated by using 370 mammograms. The detection method has a TP ratio of 97.76 % with 0.68 false positives per image. We have examined the performance of our computerized scheme using free-response operating characteristics curve.
Breast cancer; Computer Aided Diagnosis; Hessian matrix; Foveal segmentation
Although mammography is the only clinically accepted imaging modality for screening the general population to detect breast cancer, interpreting mammograms is difficult with lower sensitivity and specificity. To provide radiologists “a visual aid” in interpreting mammograms, we developed and tested an interactive system for computer-aided detection and diagnosis (CAD) of mass-like cancers. Using this system, an observer can view CAD-cued mass regions depicted on one image and then query any suspicious regions (either cued or not cued by CAD). CAD scheme automatically segments the suspicious region or accepts manually defined region and computes a set of image features. Using content-based image retrieval (CBIR) algorithm, CAD searches for a set of reference images depicting “abnormalities” similar to the queried region. Based on image retrieval results and a decision algorithm, a classification score is assigned to the queried region. In this study, a reference database with 1,800 malignant mass regions and 1,800 benign and CAD-generated false-positive regions was used. A modified CBIR algorithm with a new function of stretching the attributes in the multi-dimensional space and decision scheme was optimized using a genetic algorithm. Using a leave-one-out testing method to classify suspicious mass regions, we compared the classification performance using two CBIR algorithms with either equally weighted or optimally stretched attributes. Using the modified CBIR algorithm, the area under receiver operating characteristic curve was significantly increased from 0.865 ± 0.006 to 0.897 ± 0.005 (p < 0.001). This study demonstrated the feasibility of developing an interactive CAD system with a large reference database and achieving improved performance.
Computer-aided detection and diagnosis (CAD); Content-based image retrieval (CBIR); Breast cancer; Mammograms
Breast ultrasound (BUS) image segmentation is a very difficult task due to poor image quality and speckle noise. In this paper, local features extracted from roughly segmented regions of interest (ROIs) are used to describe breast tumors. The roughly segmented ROI is viewed as a bag. And subregions of the ROI are considered as the instances of the bag. Multiple-instance learning (MIL) method is more suitable for classifying breast tumors using BUS images. However, due to the complexity of BUS images, traditional MIL method is not applicable. In this paper, a novel MIL method is proposed for solving such task. First, a self-organizing map is used to map the instance space to the concept space. Then, we use the distribution of the instances of each bag in the concept space to construct the bag feature vector. Finally, a support vector machine is employed for classifying the tumors. The experimental results show that the proposed method can achieve better performance: the accuracy is 0.9107 and the area under receiver operator characteristic curve is 0.96 (p < 0.005).
Multiple-instance learning (MIL); Breast ultrasound (BUS) image; SVM (support vector machine); Classification
Tablet computers such as the iPad, which have a large format, improved graphic display resolution and a touch screen interface, may have an advantage compared to existing mobile devices such as smartphones and laptops for viewing radiological images. We assessed their potential for emergency radiology teleconsultation by reviewing multi-image CT and MRI studies on iPad tablet computers compared to Picture Archival and Communication Systems (PACS) workstations. Annonymised DICOM images of 79 CT and nine MRI studies comprising a range of common on-call conditions, reported on full-featured diagnostic PACS workstation by one Reporting Radiologist, were transferred from PACS to three iPad tablet computers running OsiriX HD v 2.02 DICOM software and viewed independently by three reviewing radiologists. Structured documentation was made of major findings (primary diagnosis or other clinically important findings), minor findings (incidental findings), and user feedback. Two hundred and sixty four readings (88 studies read by three reviewing radiologists) were compared, with 3.4 % (nine of 264) major discrepancies and 5.6 % (15 of 264) minor discrepancies. All reviewing radiologists reported favorable user experience but noted issues with software stability and limitations of image manipulation tools. Our results suggest that emergency conditions commonly encountered on CT and MRI can be diagnosed using tablet computers with good agreement with dedicated PACS workstations. Shortcomings in software and application design should be addressed if the potential of tablet computers for mobile teleradiology is to be fully realized.
iPad; Tablet computer; CT; MRI; Emergency radiology; Teleradiology
While previous research has determined the contrast detection threshold in medical images, it has focused on uniform backgrounds, has not used calibrated monitors, or has involved a low number of readers. With complex clinical images, how the Grayscale Standard Display Function (GSDF) affects the detection threshold and whether the median background intensity shift has been minimized by GSDF remains unknown. We set out to determine if the median background affected the detection of a low-contrast object in a clustered lumpy background, which simulated a mammography image, and to define the contrast detection threshold for these complex images. Clustered lumpy background images were created of different median intensities and disks of varying contrasts were inserted. A reader study was performed with 17 readers of varying skill level who scored with a five-point confidence scale whether a disk was present. The results were analyzed using reader operating characteristic (ROC) methodology. Contingency tables were used to determine the contrast detection threshold. No statistically significant difference was seen in the area under the ROC curve across all of the backgrounds. Contrast detection fell below 50 % between +3 and +2 gray levels. Our work supports the conclusion that Digital Imaging and Communications in Medicine GSDF calibrated monitors do perceptually linearize detection performance across shifts in median background intensity. The contrast detection threshold was determined to be +3 gray levels above the background for an object of 1° visual angle.
Image perception; ROC-based analysis; Digital display; Contrast threshold; GSDF
The use of clinical imaging modalities within the pharmaceutical research space provides value and challenges. Typical clinical settings will utilize a Picture Archive and Communication System (PACS) to transmit and manage Digital Imaging and Communications in Medicine (DICOM) images generated by clinical imaging systems. However, a PACS is complex and provides many features that are not required within a research setting, making it difficult to generate a business case and determine the return on investment. We have developed a next-generation DICOM processing system using open-source software, commodity server hardware such as Apple Xserve®, high-performance network-attached storage (NAS), and in-house-developed preprocessing programs. DICOM-transmitted files are arranged in a flat file folder hierarchy easily accessible via our downstream analysis tools and a standard file browser. This next-generation system had a minimal construction cost due to the reuse of all the components from our first-generation system with the addition of a second server for a few thousand dollars. Performance metrics were gathered and the system was found to be highly scalable, performed significantly better than the first-generation system, is modular, has satisfactory image integrity, and is easier to maintain than the first-generation system. The resulting system is also portable across platforms and utilizes minimal hardware resources, allowing for easier upgrades and migration to smaller form factors at the hardware end-of-life. This system has been in production successfully for 8 months and services five clinical instruments and three pre-clinical instruments. This system has provided us with the necessary DICOM C-Store functionality, eliminating the need for a clinical PACS for day-to-day image processing.
PACS; DCMTK; DICOM; DICOM workflow; DICOM storage
To determine which Breast Imaging Reporting and Data System (BI-RADS) descriptors for ultrasound are predictors for breast cancer using logistic regression (LR) analysis in conjunction with interobserver variability between breast radiologists, and to compare the performance of artificial neural network (ANN) and LR models in differentiation of benign and malignant breast masses. Five breast radiologists retrospectively reviewed 140 breast masses and described each lesion using BI-RADS lexicon and categorized final assessments. Interobserver agreements between the observers were measured by kappa statistics. The radiologists’ responses for BI-RADS were pooled. The data were divided randomly into train (n = 70) and test sets (n = 70). Using train set, optimal independent variables were determined by using LR analysis with forward stepwise selection. The LR and ANN models were constructed with the optimal independent variables and the biopsy results as dependent variable. Performances of the models and radiologists were evaluated on the test set using receiver-operating characteristic (ROC) analysis. Among BI-RADS descriptors, margin and boundary were determined as the predictors according to stepwise LR showing moderate interobserver agreement. Area under the ROC curves (AUC) for both of LR and ANN were 0.87 (95% CI, 0.77–0.94). AUCs for the five radiologists ranged 0.79–0.91. There was no significant difference in AUC values among the LR, ANN, and radiologists (p > 0.05). Margin and boundary were found as statistically significant predictors with good interobserver agreement. Use of the LR and ANN showed similar performance to that of the radiologists for differentiation of benign and malignant breast masses.
Breast; Ultrasonography; Artificial neural network; Breast neoplasm; Logistic regression
The Digital Imaging and Communications in Medicine (DICOM) is the standard for encoding and communicating medical imaging information. It is used in radiology as well as in many other imaging domains such as ophthalmology, dentistry, and pathology. DICOM information objects are used to encode medical images or information about the images. Their usage outside of the imaging department is increasing, especially with the sharing of medical images within Electronic Health Record systems. However, learning DICOM is long and difficult because it defines and uses many specific abstract concepts that relate to each other. In this paper, we present an approach, based on problem solving, for teaching DICOM as part of a graduate course on healthcare information. The proposed approach allows students with diversified background and no software development experience to grasp a large breadth of knowledge in a very short time.
Digital Imaging and Communications in Medicine; DICOM; Medical imaging; Healthcare information; Teaching; Problem solving
The purpose of this study was to demonstrate the robustness of our prior computerized texture analysis method for breast cancer risk assessment, which was developed initially on a limited dataset of screen-film mammograms. This current study investigated the robustness by (1) evaluating on a large clinical dataset, (2) using full-field digital mammograms (FFDM) as opposed to screen-film mammography, and (3) incorporating analyses over two types of high-risk patient sets, as well as patients at low risk for breast cancer. The evaluation included the analyses on the parenchymal patterns of women at high risk of developing of breast cancer, including both BRCA1/2 gene mutation carriers and unilateral cancer patients, and of women at low risk of developing breast cancer. A total of 456 cases, including 53 women with BRCA1/2 gene mutations, 75 women with unilateral cancer, and 328 low-risk women, were retrospectively collected under an institutional review board approved protocol. Regions-of-interest (ROIs), were manually selected from the central breast region immediately behind the nipple. These ROIs were subsequently used in computerized feature extraction to characterize the mammographic parenchymal patterns in the images. Receiver operating characteristic analysis was used to assess the performance of the computerized texture features in the task of distinguishing between high-risk and low-risk subjects. In a round robin evaluation on the FFDM dataset with Bayesian artificial neural network analysis, AUC values of 0.82 (95% confidence interval [0.75, 0.88]) and 0.73 (95% confidence interval [0.67, 0.78]) were obtained between BRCA1/2 gene mutation carriers and low-risk women, and between unilateral cancer and low-risk women, respectively. These results from computerized texture analysis on digital mammograms demonstrated that high-risk and low-risk women have different mammographic parenchymal patterns. On this large clinical dataset, we validated our methods for quantitative analyses of mammographic patterns on FFDM, statistically demonstrating again that women at high risk tend to have dense breasts with coarse and low-contrast texture patterns.
Computerized texture analysis; Breast cancer risk assessment; Mammographic parenchymal patterns; Full-field digital mammograms; Quantitative imaging analysis
Advances in handheld computing now allow review of DICOM datasets from remote locations. As the diagnostic ability of this tool is unproven, we evaluated the ability to diagnose acute appendicitis on abdominal CT using a mobile DICOM viewer. This HIPAA compliant study was IRB-approved. Twenty-five abdominal CT studies from patients with RLQ pain were interpreted on a handheld device (iPhone) using a DICOM viewer (OsiriX mobile) by five radiologists. All patients had surgical confirmation of acute appendicitis or follow-up confirming no acute appendicitis. Studies were evaluated for the ability to find the appendix, maximum appendiceal diameter, presence of an appendicolith, periappendiceal stranding and fluid, abscess, and an assessment of the diagnosis of acute appendicitis. Results were compared to PACS workstation. Fifteen cases of acute appendicitis were correctly identified on 98% of interpretations, with no false-positives. Eight appendicoliths were correctly identified on 88% of interpretations. Three abscesses were correctly identified by all readers. Handheld device measurement of appendiceal diameter had a mean 8.6% larger than PACS measurements (p = 0.035). Evaluation for acute appendicitis on abdominal CT studies using a portable device DICOM viewer can be performed with good concordance to reads performed on PACS workstations.
Appendicitis; Computed tomography; Gastrointestinal; Mobile; Teleradiology
Increasing radiology studies has led to the emergence of new requirements for management medical information, mainly affecting the storage of digital images. Today, it is a necessary interaction between workflow management and legal rules that govern it, to allow an efficient control of medical technology and associated costs. Another important topic that is growing in importance within the healthcare sector is compliance, which includes the retention of studies, information security, and patient privacy. Previously, we conducted a series of extensive analysis and measurements of pre-existing operating conditions. These studies and projects have been described in other papers. The first phase: hardware and software installation and initial tests were completed in March 2006. The storage phase was built step by step until the PACS-INR was totally completed. Two important aspects were considered in the integration of components: (1) the reliability and performance of the system to transfer and display DICOM images, and (2) the availability of data backups for disaster recovery and downtime scenarios. This paper describes the high-availability model for a large-scale PACS to support the storage and retrieve of data using CAS and DAS technologies to provide an open storage platform. This solution offers a simple framework that integrates and automates the information at low cost and minimum risk. Likewise, the model allows an optimized use of the information infrastructure in the clinical environment. The tests of the model include massive data migration, openness, scalability, and standard compatibility to avoid locking data into a proprietary technology.
Archive; Information storage and retrieval; Database management systems; Digital image management; Enterprise PACS
This paper presents novel multiple keywords annotation for medical images, keyword-based medical image retrieval, and relevance feedback method for image retrieval for enhancing image retrieval performance. For semantic keyword annotation, this study proposes a novel medical image classification method combining local wavelet-based center symmetric–local binary patterns with random forests. For keyword-based image retrieval, our retrieval system use the confidence score that is assigned to each annotated keyword by combining probabilities of random forests with predefined body relation graph. To overcome the limitation of keyword-based image retrieval, we combine our image retrieval system with relevance feedback mechanism based on visual feature and pattern classifier. Compared with other annotation and relevance feedback algorithms, the proposed method shows both improved annotation performance and accurate retrieval results.
Image annotation; Random forests; Confidence score; Body relation graph; Relevance feedback
The measurement of angles between anatomical structures is common in radiological and orthopedic practice. Frequently used measurements include scapholunate angle for assessment of wrist instability and Cobb’s angle used for assessment of scoliosis. Measurements of these angles are easily performed on plain X-ray radiographs. However, the situation is more complicated when these measurements are to be performed on cross-sectional (CT or MRI) examinations. On some of the diagnostic workstations, it is not possible to perform angle measurements between the structures if they are not identified on the same image and are located on different images of the same projection or plane. We present a simple solution to measure angles between structures on different images that can be used both in CT and MR.
Computed tomography; Image viewer; Magnetic resonance imaging