This paper describes development of a decision support system for diagnosis of malaria using color image analysis. A hematologist has to study around 100 to 300 microscopic views of Giemsa-stained thin blood smear images to detect malaria parasites, evaluate the extent of infection and to identify the species of the parasite. The proposed algorithm picks up the suspicious regions and detects the parasites in images of all the views. The subimages representing all these parasites are put together to form a composite image which can be sent over a communication channel to obtain the opinion of a remote expert for accurate diagnosis and treatment. We demonstrate the use of the proposed technique for use as a decision support system by developing an android application which facilitates the communication with a remote expert for the final confirmation on the decision for treatment of malaria. Our algorithm detects around 96% of the parasites with a false positive rate of 20%. The Spearman correlation r was 0.88 with a confidence interval of 0.838 to 0.923, p < 0.0001.
Color image analysis; Decision support system; Telemedicine; Malaria diagnosis
Radiology reports are permanent legal documents that serve as official interpretation of imaging tests. Manual analysis of textual information contained in these reports requires significant time and effort. This study describes the development and initial evaluation of a toolkit that enables automated identification of relevant information from within these largely unstructured text reports. We developed and made publicly available a natural language processing toolkit, Information from Searching Content with an Ontology-Utilizing Toolkit (iSCOUT). Core functions are included in the following modules: the Data Loader, Header Extractor, Terminology Interface, Reviewer, and Analyzer. The toolkit enables search for specific terms and retrieval of (radiology) reports containing exact term matches as well as similar or synonymous term matches within the text of the report. The Terminology Interface is the main component of the toolkit. It allows query expansion based on synonyms from a controlled terminology (e.g., RadLex or National Cancer Institute Thesaurus [NCIT]). We evaluated iSCOUT document retrieval of radiology reports that contained liver cysts, and compared precision and recall with and without using NCIT synonyms for query expansion. iSCOUT retrieved radiology reports with documented liver cysts with a precision of 0.92 and recall of 0.96, utilizing NCIT. This recall (i.e., utilizing the Terminology Interface) is significantly better than using each of two search terms alone (0.72, p = 0.03 for liver cyst and 0.52, p = 0.0002 for hepatic cyst). iSCOUT reliably assembled relevant radiology reports for a cohort of patients with liver cysts with significant improvement in document retrieval when utilizing controlled lexicons.
Controlled vocabulary; Natural language processing; Information storage and retrieval
Gold chloride technique can be combined with Adobe Photoshop® software to yield a quantitative assessment of the different areas in heterogeneous structures as are ligament. A semi-automatized method based on the sum of two- and three-dimensional morphological criteria upon colorimetric criteria allows the identification and measurement of the area occupied by a structure of interest. It also allows the quantification of color intensity to differentiate structures with similar staining avidity, like vessels and nerves. This computer-assisted, semiquantitative procedure for computerized morphometry is relatively simple to perform. The accuracy, efficiency, and reproducibility of this method based on a commercially available imaging program were considered adequate when tested on the anterior cruciate ligament of the cat. Image normalization by trained observers using a commercially available software package designed for photography, applied to a sample randomly chosen, has provided the means of making reproducible measurements of heterogeneous structures.
Adobe Photoshop®; Proprioception; Gold chloride; Anterior cruciate ligament; Colorimetry; Computer-assisted morphometry
There is a growing interest in three-dimensional computed tomography (3D-CT) as a research tool for the study of bone, joint anatomy, and kinematics. However, when CT data are processed and handled manually using image processing programs to yield 3D image and coordinate value, systematic and random errors should be validated. We evaluated the accuracy and reliability of length measurement on CT with OsiriX software. 3D-CT scans were made of 14 frozen pig knees with five transosseous holes in the metaphyseal portion of femur. The lengths between tunnel orifices were measured using Mitutoyo Digimatic digital calipers to establish the gold standard, and with the OsiriX program in 3D multi-planar reformatting mode for comparison. All measurements were recorded by a principal (replicate 1, trial 1) and a secondary observer (replicate 2, trial 1) and were repeated once by each observer (trial 2). The mean differences between OsiriX and real measurements were less than 0.1 mm in both replicates, and maximum differences were less than 0.3 mm. There were no significant differences between the replicates and real measurements (p = 0.544 and 0.622 for replicates 1 and 2, respectively). The intraclass correlation coefficients (ICC) were very high between trials and between replicates (ICC = 0.998 and 0.999, respectively). For kinematic analysis of the knees, length measurements on 3D-CT using OsiriX program can be used as alternatives to real measurements with less than 0.3-mm accuracy and very high reliability.
Computed tomography; Accuracy; Reliability; Length; OsiriX
Computer-aided diagnosis systems (CADs) can quantify the severity of diseases by analyzing a set of images and employing prior statistical models. In general, CADs have proven to be effective at providing quantitative measurements of the extent of a particular disease, thus helping physicians to better monitor the progression of cancer, infectious diseases, and other health conditions. Electronic Health Records frequently include a large amount of clinical data and medical history that can provide critical information about the underlying condition of a patient. We hypothesize that the fusion of image and clinical–physiological features can be used to enhance the accuracy of automatic image classification models. In particular, this paper shows how image analytic tools can move beyond classical image interpretation models to broader systems where image and physiological measurements are fused and used to create more generic detection models. To test our hypothesis, a CAD system capable of quantifying the severity of patients with pulmonary fibrosis has been developed. Results show that CAD systems augmented with multimodal physiological values are more robust and accurate at determining the severity of the disease.
Data fusion; Physiological values; Multimodal biomarkers; Computer-aided diagnosis; Pulmonary fibrosis
The images generated in modern IC laboratories are created with high-quality standard (1,024 × 1,024 pixels and 10–12 bits/pixel) enabling cardiologists to perform interventions in the best conditions. But these images are in most of the cases archived in a basic quality standard (512 × 512 pixels and 8 bits/pixel). The purpose of this work is to complete the research developed in a previous paper and analyze the influence of the matrix size and the bit depth reduction on the image quality acquired on a polymethylmethacrylate (PMMA) phantom with a test object. The variation in contrast-to-noise ratio (CNR) and high contrast spatial resolution (HCSR) were investigated when the matrix size and the bit depth were independently modified for different phantom thicknesses. These two image quality parameters did not suffer noticeable alterations under bits depth reduction from 10 to 8 bits. Such a result seems to imply that bits depth reduction could be used to reduce file sizes with a suitable algorithm and without losing perceptible image quality information. But when the matrix size was reduced from 1,024 × 1,024 to 512 × 512 pixels, a reduction from 17% to 25% in HCSR was noticed when changing phantom thickness, and an increase of 27% in CNR was observed. These findings should be taken into account and it would be wise to conduct further investigations in the field of clinical images.
Image quality; Test object; Matrix size; Bits depth; Image metrics; Cardiology
Under typical dark chest radiography reading room conditions, a radiologist’s pupils contract and dilate as their visual focus intermittently shifts between the high luminance monitor and the darker background wall, resulting in increased visual fatigue and degradation of diagnostic performance. A controlled increase of ambient lighting may minimize these visual adjustments and potentially improve comfort and accuracy. This study was designed to determine the effect of a controlled increase of ambient lighting on chest radiologist nodule detection performance. Four chest radiologists read 100 radiographs (50 normal and 50 containing a subtle nodule) under low (E = 1 lx) and elevated (E = 50 lx) ambient lighting levels on a DICOM-calibrated, medical-grade liquid crystal display. Radiologists were asked to identify nodule locations and rate their detection confidence. A receiver operating characteristic (ROC) analysis of radiologist results was performed and area under ROC curve (AUC) values calculated for each ambient lighting level. Additionally, radiologist selection times under both illuminance conditions were determined. Average AUC values did not significantly differ (p > 0.05) between ambient lighting levels (estimated mean difference = −0.03; 95% CI, (−0.08, 0.03)). Average selection times decreased or remained constant with increased illuminance. The most considerable decreases occurred for false positive identification times (35.4 ± 18.8 to 26.2 ± 14.9 s) and true positive identification times (29.7 ± 18.3 to 24.5 ± 15.5 s). No performance differences were statistically significant. Study findings suggest that a controlled increase of ambient lighting within darkly lit chest radiology reading rooms, to a level more suitable for performance of common radiological tasks, does not appear to have a statistically significant effect on nodule detection performance.
Chest radiographs; Image perception; Visual perception
The purpose of this study was to retrospectively evaluate radiologist performance in detection of lacunar infarcts on T1- and T2-weighted images, without and with the use of a computer-aided diagnosis (CAD) scheme. Thirty T1-weighted and 30 T2-weighted MR images obtained from 30 patients were used for assessing observer performance. These images were acquired using the fast spin-echo sequence with a 1.5-T MR imaging scanner. The group included 15 patients (age range, 48–83 years; mean age, 67.2 years; 10 men and five women) with a lacunar infarct and 15 patients (age range, 39–76 years; mean age, 64.0 years; eight men and seven women) without lacunar infarcts. Nine radiologists participated in the study. The radiologists initially interpreted the T1- and T2-weighted images without and then with the use of CAD, which indicated their confidence levels regarding the presence (or absence) of lacunar infarcts and the most likely position of a lesion on each MR scan. The observers’ performance without and with the computer output was evaluated by performing receiver operating characteristic analysis. For the nine radiologists, the mean area under the best-fit binormal receiver operating characteristic curve plotted for unit square values of radiologists who interpreted the images without and with the scheme were 0.891 and 0.937, respectively. The performance of the radiologists improved significantly when they used the computer output (p = 0.032). The CAD scheme has potential to improve the accuracy of radiologists’ performance in detection of lacunar infarcts.
Lacunar infarct; Magnetic resonance (MR); Computer-aided diagnosis (CAD); Observer study; Receiver operating characteristic (ROC)
In a computer-aided diagnosis (CADx) scheme for evaluating the likelihood of malignancy of clustered microcalcifications on mammograms, it is necessary to segment individual calcifications correctly. The purpose of this study was to develop a computerized segmentation method for individual calcifications with various sizes while maintaining their shapes in the CADx schemes. Our database consisted of 96 magnification mammograms with 96 clustered microcalcifications. In our proposed method, a mammogram image was decomposed into horizontal subimages, vertical subimages, and diagonal subimages for a second difference at scales 1 to 4 by using a filter bank. The enhanced subimages for nodular components (NCs) and the enhanced subimages for both nodular and linear components (NLCs) were obtained from analysis of a Hessian matrix composed of the pixel values in those subimages for the second difference at each scale. At each pixel, eight objective features were given by pixel values in the subimages for NCs at scales 1 to 4 and the subimages for NLCs at scales 1 to 4. An artificial neural network with the eight objective features was employed to enhance calcifications on magnification mammograms. Calcifications were finally segmented by applying a gray-level thresholding technique to the enhanced image for calcifications. With the proposed method, a sensitivity of calcifications within clustered microcalcifications and the number of false positives per image were 96.5% (603/625) and 1.69, respectively. The average shape accuracy for segmented calcifications was also 91.4%. The proposed method with high sensitivity of calcifications while maintaining their shapes would be useful in the CADx schemes.
Computerized segmentation; Calcification; Magnification mammogram; Multiresolution analysis; Artificial neural network
Traditionally, image studies evaluating the effectiveness of computer-aided diagnosis (CAD) use a single label from a medical expert compared with a single label produced by CAD. The purpose of this research is to present a CAD system based on Belief Decision Tree classification algorithm, capable of learning from probabilistic input (based on intra-reader variability) and providing probabilistic output. We compared our approach against a traditional decision tree approach with respect to a traditional performance metric (accuracy) and a probabilistic one (area under the distance–threshold curve—AuCdt). The probabilistic classification technique showed notable performance improvement in comparison with the traditional one with respect to both evaluation metrics. Specifically, when applying cross-validation technique on the training subset of instances, boosts of 28.26% and 30.28% were noted for the probabilistic approach with respect to accuracy and AuCdt, respectively. Furthermore, on the validation subset of instances, boosts of 20.64% and 23.21% were noted again for the probabilistic approach with respect to the same two metrics. In addition, we compared our CAD system results with diagnostic data available for a small subset of the Lung Image Database Consortium database. We discovered that when our CAD system errs, it generally does so with low confidence. Predictions produced by the system also agree with diagnoses of truly benign nodules more often than radiologists, offering the possibility of reducing the false positives.
Chest CT; Computer-aided diagnosis (CAD); Feature extraction; Image analysis; Machine learning; Radiographic image interpretation; Computer-assisted
Image de-identification has focused on the removal of textual protected health information (PHI). Surface reconstructions of the face have the potential to reveal a subject’s identity even when textual PHI is absent. This study assessed the ability of a computer application to match research subjects’ 3D facial reconstructions with conventional photographs of their face. In a prospective study, 29 subjects underwent CT scans of the head and had frontal digital photographs of their face taken. Facial reconstructions of each CT dataset were generated on a 3D workstation. In phase 1, photographs of the 29 subjects undergoing CT scans were added to a digital directory and tested for recognition using facial recognition software. In phases 2–4, additional photographs were added in groups of 50 to increase the pool of possible matches and the test for recognition was repeated. As an internal control, photographs of all subjects were tested for recognition against an identical photograph. Of 3D reconstructions, 27.5% were matched correctly to corresponding photographs (95% upper CL, 40.1%). All study subject photographs were matched correctly to identical photographs (95% lower CL, 88.6%). Of 3D reconstructions, 96.6% were recognized simply as a face by the software (95% lower CL, 83.5%). Facial recognition software has the potential to recognize features on 3D CT surface reconstructions and match these with photographs, with implications for PHI.
Facial recognition; Privacy; 3D Reconstruction; 3D Imaging (imaging, three-dimensional); HIPPA
Surgeons have to deal with many devices from different vendors within the operating room during surgery. Independent communication standards are necessary for the system integration of these devices. For implantations, three new extensions of the Digital Imaging and Communications in Medicine (DICOM) standard make use of a common communication standard that may optimise one of the surgeon's presently very time-consuming daily tasks. The paper provides a brief description of these DICOM Supplements and gives recommendations to their application in practice based on workflows that are proposed to be covered by the new standard extension. Two of the workflows are described in detail and separated into phases that are supported by the new data structures. Examples for the application of the standard within these phases give an impression of the potential usage. Even if the presented workflows are from different domains, we identified a generic core that may benefit from the surgical DICOM Supplements. In some steps of the workflows, the surgical DICOM Supplements are able to replace or optimise conventional methods. Standardisation can only be a means for integration and interoperability. Thus, it can be used as the basis for new applications and system architectures. The influence on current applications and communication processes is limited. Additionally, the supplements provide the basis for further applications, such as the support of surgical navigation systems. Given the support of all involved stakeholders, it is possible to provide a benefit for surgeons and patients.
Digital Imaging and Communications in Medicine (DICOM); Infrastructure; Medical devices; Navigation
Chest radiologists rely on the segmentation and quantificational analysis of ground-glass opacities (GGO) to perform imaging diagnoses that evaluate the disease severity or recovery stages of diffuse parenchymal lung diseases. However, it is computationally difficult to segment and analyze patterns of GGO while compared with other lung diseases, since GGO usually do not have clear boundaries. In this paper, we present a new approach which automatically segments GGO in lung computed tomography (CT) images using algorithms derived from Markov random field theory. Further, we systematically evaluate the performance of the algorithms in segmenting GGO in lung CT images under different situations. CT image studies from 41 patients with diffuse lung diseases were enrolled in this research. The local distributions were modeled with both simple and adaptive (AMAP) models of maximum a posteriori (MAP). For best segmentation, we used the simulated annealing algorithm with a Gibbs sampler to solve the combinatorial optimization problem of MAP estimators, and we applied a knowledge-guided strategy to reduce false positive regions. We achieved AMAP-based GGO segmentation results of 86.94%, 94.33%, and 94.06% in average sensitivity, specificity, and accuracy, respectively, and we evaluated the performance using radiologists’ subjective evaluation and quantificational analysis and diagnosis. We also compared the results of AMAP-based GGO segmentation with those of support vector machine-based methods, and we discuss the reliability and other issues of AMAP-based GGO segmentation. Our research results demonstrate the acceptability and usefulness of AMAP-based GGO segmentation for assisting radiologists in detecting GGO in high-resolution CT diagnostic procedures.
Image segmentation; Lung diseases; Markov chains; Tomography; X-ray computed
In medio-lateral oblique view of mammogram, pectoral muscle may sometimes affect the detection of breast cancer due to their similar characteristics with abnormal tissues. As a result pectoral muscle should be handled separately while detecting the breast cancer. In this paper, a novel approach for the detection of pectoral muscle using average gradient- and shape-based feature is proposed. The process first approximates the pectoral muscle boundary as a straight line using average gradient-, position-, and shape-based features of the pectoral muscle. Straight line is then tuned to a smooth curve which represents the pectoral margin more accurately. Finally, an enclosed region is generated which represents the pectoral muscle as a segmentation mask. The main advantage of the method is its’ simplicity as well as accuracy. The method is applied on 200 mammographic images consisting 80 randomly selected scanned film images from Mammographic Image Analysis Society (mini-MIAS) database, 80 direct radiography (DR) images, and 40 computed radiography (CR) images from local database. The performance is evaluated based upon the false positive (FP), false negative (FN) pixel percentage, and mean distance closest point (MDCP). Taking all the images into consideration, the average FP and FN pixel percentages are 4.22%, 3.93%, 18.81%, and 6.71%, 6.28%, 5.12% for mini-MIAS, DR, and CR images, respectively. Obtained MDCP values for the same set of database are 3.34, 3.33, and 10.41 respectively. The method is also compared with two well-known pectoral muscle detection techniques and in most of the cases, it outperforms the other two approaches.
Adaptive band division; Biomedical image analysis; Breast cancer; Mammography; Pectoral muscle; Segmentation
Structured reporting uses consistent ordering of results and standardized terminology to improve the quality and reduce the complexity of radiology reports. We sought to define a generalized approach for radiology reporting that produces flexible outline-style reports, accommodates structured information and named reporting elements, allows reporting terms to be linked to controlled vocabularies, uses existing informatics standards, and allows structured report data to be extracted readily. We applied the Regular Language for XML–Next Generation (RELAX NG) schema language to create templates for 110 reporting templates created as part of the Radiological Society of North America reporting initiative. We evaluated how well this approach addressed the project’s goals. The RELAX NG schema language expressed the cardinality and hierarchical relationships of reporting concepts, and allowed reporting elements to be mapped to terms in controlled medical vocabularies, such as RadLex®, Systematized Nomenclature of Medicine Clinical Terms®, and Logical Observation Identifiers Names and Codes®. The approach provided extensibility and accommodated the addition of new features. Overall, the approach has proven to be useful and will form the basis for a supplement to the Digital Imaging and Communication in Medicine Standard.
Radiology; Structured reporting; Standards; Knowledge representation; Extensible Markup Language (XML); RELAX NG; Grammar; Regular language
3D imaging systems are used to construct high-resolution meshes of patient’s heads that can be analyzed by computer algorithms. Our work starts with such 3D head meshes and produces both global and local descriptors of 3D shape. Since these descriptors are numeric feature vectors, they can be used in both classification and quantification of various different abnormalities. In this paper, we define these descriptors, describe our methodology for constructing them from 3D head meshes, and show through a set of classification experiments involving cases and controls for a genetic disorder called 22q11.2 deletion syndrome that they are suitable for use in craniofacial research studies. The main contributions of this work include: automatic generation of novel global and local data representations, robust automatic placement of anthropometric landmarks, generation of local descriptors for nasal and oral facial features from landmarks, use of local descriptors for predicting various local facial features, and use of global features for 22q11.2DS classification, showing their potential use as descriptors in craniofacial research.
Image analysis; Imaging; Three-dimensional; Data mining
Reading room design can have a major impact on radiologists’ health, productivity, and accuracy in reading. Several factors must be taken into account in order to optimize the work environment for radiologists. Further, with the advancement in imaging technology, clinicians now have the ability to view and see digital exams without having to interact with radiologists. However, it is important to design components that encourage and enhance interactions between clinicians and radiologists to increase patient safety, and to combine physician and radiologist expertise. The present study evaluates alternative workstations in a real-world testbed space, using qualitative data (users’ perspectives) to measure satisfaction with the lighting, ergonomics, furniture, collaborative spaces, and radiologist workstations. In addition, we consider the impact of the added collaboration components of the future reading room design, by utilizing user evaluation surveys to devise baseline satisfaction data regarding the innovative reading room environment.
Radiology reading room; Ergonomics; User evaluation; Musculoskeletal
The use of the endovascular prostheses in abdominal aortic aneurysm has proven to be an effective technique to reduce the pressure and rupture risk of aneurysm. Nevertheless, in a long-term perspective, complications such as leaks inside the aneurysm sac (endoleaks) could appear causing a pressure elevation and increasing the danger of rupture consequently. At present, computed tomographic angiography (CTA) is the most common examination for medical surveillance. However, endoleak complications cannot always be detected by visual inspection on CTA scans. The investigation on new techniques to detect endoleaks and analyse their effects on treatment evolution is of great importance for endovascular aneurysm repair (EVAR) technique. The purpose of this work was to evaluate the capability of texture features obtained from the aneurysmatic thrombus CT images to discriminate different types of evolutions caused by endoleaks. The regions of interest (ROIs) from patients with different post-EVAR evolution were extracted by experienced radiologists. Three techniques were applied to each ROI to obtain texture parameters, namely the grey level co-occurrence matrix (GLCM), the grey level run length matrix (GLRLM) and the grey level difference method (GLDM). The results showed that GLCM, GLRLM and GLDM features presented a good discrimination ability to differentiate between favourable or unfavourable evolutions. GLCM was the most efficient in terms of classification accuracy (93.41% ± 0.024) followed by GLRLM (90.17% ± 0.077) and finally by GLDM (81.98% ± 0.045). According to the results, we can consider texture analysis as complementary information to classified abdominal aneurysm evolution after EVAR.
Aneurysm; EVAR; Texture features; Neural network
Attending radiologists routinely edit radiology trainee dictated preliminary reports as part of standard workflow models. Time constraints, high volume, and spatial separation may not always facilitate clear discussion of these changes with trainees. However, these edits can represent significant teaching moments that are lost if they are not communicated back to trainees. We created an electronic method for retrieving and displaying changes made to resident written preliminary reports by attending radiologists during the process of radiology report finalization. The Radiology Information System is queried. Preliminary and final radiology reports, as well as report metadata, are extracted and stored in a database indexed by accession number and trainee/radiologist identity. A web application presents to trainees their 100 most recent preliminary and final report pairs both side by side and in a “track changes” mode. Web utilization audits showed regular utilization by trainees. Surveyed residents stated they compared reports for educational value, to improve future reports, and to improve patient care. Residents stated that they compared reports more frequently after deployment of this software solution and that regular assessment of their work using the Report Comparator allowed them to routinely improve future report quality and improved radiological understanding. In an era with increasing workload demands, trainee work hour restrictions, and decentralization of department resources (e.g., faculty, PACS), this solution helps to retain an important part of the educational experience that would have otherwise run the risk of being lost and provides it to the trainees in an efficient and highly consumable manner.
Communication; Computers in medicine; Continuing medical education; Databases; Medical education; Efficiency; Electronic medical record; Electronic teaching file; Internship and residency; Internet; Interpretation errors; Medical records systems; PACS support; Radiology reporting
Diabetic retinopathy has become an increasingly important cause of blindness. Nevertheless, vision loss can be prevented from early detection of diabetic retinopathy and monitor with regular examination. Common automatic detection of retinal abnormalities is for microaneurysms, hemorrhages, hard exudates, and cotton wool spot. However, there is a worse case of retinal abnormality, but not much research was done to detect it. It is neovascularization where new blood vessels grow due to extensive lack of oxygen in the retinal capillaries. This paper shows that various combination of techniques such as image normalization, compactness classifier, morphology-based operator, Gaussian filtering, and thresholding techniques were used in developing of neovascularization detection. A function matrix box was added in order to classify the neovascularization from natural blood vessel. A region-based neovascularization classification was attempted as a diagnostic accuracy. The developed method was tested on images from different database sources with varying quality and image resolution. It shows that specificity and sensitivity results were 89.4% and 63.9%, respectively. The proposed approach yield encouraging results for future development.
Biomedical Image Analysis; Digital Image Processing; Image Segmentation; Feature selection; Diabetic Retinopathy; Neovascularization
Clinical picture archiving and communications systems provide convenient, efficient access to digital medical images from multiple modalities but can prove challenging to deploy, configure and use. MRIdb is a self-contained image database, particularly suited to the storage and management of magnetic resonance imaging data sets for population phenotyping. It integrates a mature image archival system with an intuitive web-based user interface that provides visualisation and export functionality. In addition, utilities for auditing, data migration and system monitoring are included in a virtual machine image that is easily deployed with minimal configuration. The result is a freely available turnkey solution, designed to support epidemiological and imaging genetics research. It allows the management of patient data sets in a secure, scalable manner without requiring the installation of any bespoke software on end users’ workstations. MRIdb is an open-source software, available for download at http://www3.imperial.ac.uk/bioinfsupport/resources/software/mridb.
PACS; Digital imaging and communications in medicine (DICOM); MR imaging