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1.  An Integrated Approach to a Teaching File Linked to PACS 
Journal of Digital Imaging  2006;20(4):402-410.
To meet the educational needs of a medical imaging department with a strong teaching commitment, a teaching file that uses digital data supplied by the institutional picture archiving and communications system (PACS) was required. This teaching file had to be easily used by the end users, have a simple submission process, be able to support multiple users, be searchable on all data fields, and implementing the teaching file must not incur any additional software or hardware costs. The teaching file developed to address this problem takes advantage of the database structure and capabilities of several components included in the commercial PACS installed at the hospital. MS Access is used to seamlessly integrate with the digital imaging and communication in medicine (DICOM) database of a normal work station that is part of the PACS. This integration allows relevant patient and study demographics to be copied from images of interest and then to be stored in a separate database as the back-end of the digital teaching file. When images for a particular teaching file case need to be reviewed, they are automatically retrieved and displayed from the main PACS database using an open application programming interface (API) connection defined on the PACS web server. Utilizing this open API connection means the teaching file contains only the relevant demographic information of each teaching file case; no image data is stored locally. The open API connection allows access to imaging data usually not encountered in a teaching file, allowing more comprehensive imaging case files to be developed by the radiologist. Other advantages of this teaching file design are that it does not duplicate image data, it is small allowing simple ongoing backup, and it can be opened with multiple users accessing the database without compromising data access or integrity.
PMCID: PMC3043913  PMID: 17191104
Electronic teaching files; DICOM; PACS
2.  An ROC Study of Chest Radiographs: 2K Versus 4K High-Resolution Soft-Copy Images 
Journal of Digital Imaging  2006;20(4):347-351.
Computed radiography of chest with a 4K image array was recently introduced. We performed a multiobserver study to compare the diagnostic accuracy of 2K (standard) and 4K (high quality) chest radiographs displayed on a 5-mega-pixel monitor (2K monitor). One hundred cases of posteroanterior chest radiographs (a total of 200 images) were selected by two chest radiologists. Those radiographs included pneumothorax (n = 14), nodules (n = 15), interstitial disease (n = 10), or neither abnormality (n = 61). These were interpreted by four radiologists in two separate sessions. They recorded their confidence scale for the presence or absence of abnormality. Diagnostic accuracy was determined by receiver operating characteristic (ROC) analysis for each observer. ROC analysis showed no statistically significant difference between the 2K and 4K modes for the detection of any of the different abnormalities by individual readers. Our preliminary study suggests that 2K mode would be sufficient for the detection of abnormality on chest radiograph and there is no considerable validity to incline toward the 4K mode in current picture archiving and communication system environment using 2K monitor. However, we think that additional investigation using more subtle parenchymal or rib lesion should be followed.
PMCID: PMC3043916  PMID: 17191100
Observer performance; ROC; computed radiography; PACS
3.  Part 2. Automated Change Detection and Characterization Applied to Serial MR of Brain Tumors may Detect Progression Earlier than Human Experts 
Journal of Digital Imaging  2006;20(4):321-328.
An algorithm was developed which compares serial MRI brain examinations of brain tumor patients and judges them as either “stable” or “progressing”. A set of 88 serial MR cases were obtained, consisting of cases which were stable and remained stable for at least 8 months, cases which were stable but progressed in less than 8 months, and cases which were progressing. The algorithm was run and its output was compared to the original clinical interpretation. Of the exam pairs which were judged stable and which remained stable at least 8 months after the later examination, the algorithm diagnosed 45/46 as stable. For exam pairs judged to be progressing, the algorithm judged 15/17 to be progressing. Of the exam pairs which were judged stable, but which went on to progress less than 8 months after the later of the pair, 16/25 were judged by the algorithm to be progressing.
PMCID: PMC3043917  PMID: 17216586
Brain tumor; serial imaging; computer aided diagnosis; change detection
4.  Comparison of Techniques for Correction of Magnification of Pelvic X-rays for Hip Surgery Planning 
Journal of Digital Imaging  2006;20(4):329-335.
The aim of this study was to develop an accurate method for correction of magnification of pelvic x-rays to enhance accuracy of hip surgery planning.
All investigated methods aim at estimating the anteroposterior location of the hip joint in supine position to correctly position a reference object for correction of magnification. An existing method—which is currently being used in clinical practice in our clinics—is based on estimating the position of the hip joint by palpation of the greater trochanter. It is only moderately accurate and difficult to execute reliably in clinical practice. To develop a new method, 99 patients who already had a hip implant in situ were included; this enabled determining the true location of the hip joint deducted from the magnification of the prosthesis. Physical examination was used to obtain predictor variables possibly associated with the height of the hip joint. This included a simple dynamic hip joint examination to estimate the position of the center of rotation. Prediction equations were then constructed using regression analysis. The performance of these prediction equations was compared with the performance of the existing protocol.
The mean absolute error in predicting the height of the hip joint center using the old method was 20 mm (range −79 mm to +46 mm). This was 11 mm for the new method (−32 mm to +39 mm). The prediction equation is: height (mm) = 34 + 1/2 abdominal circumference (cm).
The newly developed prediction equation is a superior method for predicting the height of the hip joint center for correction of magnification of pelvic x-rays. We recommend its implementation in the departments of radiology and orthopedic surgery.
PMCID: PMC3043918  PMID: 17192815
Pelvic x-rays; hip joint; hip surgery planning
5.  Design and Implementation of Gradient Vector Flow Snake to Detect a Reference Object in Pelvic X-Rays for Preoperative Total Hip Arthroplasty Planning Application 
Journal of Digital Imaging  2006;20(4):373-380.
The main interest of this research project is to promote automation in performing preoperative planning for hip joint replacement surgery using a special medical image viewing software, ViewPro™. Preoperative planning is performed to carefully prepare the surgery and to accurately select the hip implants. The first step of preoperative planning is to calibrate the x-ray image to adjust the magnification factor. A femoral head implant is used as magnification factor reference. Automation is introduced by developing an algorithm to automatically detect this reference object in the image. The algorithm used for performing the automatic detection of the reference object is gradient vector flow (GVF) snake. A study has been performed to compare the newly developed semiautomatic algorithm to the old manual calibration algorithm. The results show a close relation between the two algorithms (less than 1% of average relative difference). It is concluded that the developed semiautomated algorithm can be used as an alternative for performing the manual calibration.
PMCID: PMC3043919  PMID: 17192814
Preoperative planning; total hip arthroplasty; snake algorithm; gradient vector flow
6.  Increasing the Number of Gray Shades in Medical Display Systems—How Much is Enough? 
Journal of Digital Imaging  2006;20(4):422-432.
Medical images produced by x-ray detectors, computed tomography (CT) scanners, and other modalities typically contain between 12–16 bits/pixel, which corresponds to 4,096–65,536 shades of gray. On the other hand, we see that these images are visualized by means of medical displays that have much lower available number of gray shades. For a long time medical LCDs only supported 8 bits or 256 shades of gray per pixel. With the introduction of medical displays optimized for mammography, the available number of gray scales increased to 1,024. Recently, several manufacturers announced new display systems with higher bit depth. Because higher bit depth often directly results in higher display cost, it is a logical question to ask if this is required or even useful at all. This paper will give an answer by investigating several aspects such as limitations of the human visual system, digital imaging and communication in medicine grayscale standard display function calibration, and characteristics of medical LCDs.
PMCID: PMC3043920  PMID: 17195900
Medical display; grayscale resolution; bit depth; shades of gray; GSDF; human visual system
7.  Microcatheter Tip Enhancement in Fluoroscopy: A Comparison of Techniques 
Journal of Digital Imaging  2006;20(4):367-372.
We compared three techniques for enhancement of microcatheter tips in fluoroscopic images: conventional subtraction technique (CST); averaged image subtraction technique (AIST), which we have developed; and double average filtering (DAF) technique, which uses nonlinear background estimates. A pulsed fluoroscopic image sequence was obtained as a microcatheter was passed through a carotid phantom that was on top of a head phantom. The carotid phantom was a silicone cylinder containing a simulated vessel with the shape and curvatures of the internal carotid artery. The three techniques were applied to the images of the sequence, then the catheter tip was manually identified in each image, and 100 x 100 pixel images, centered at the indicated microcatheter tip positions, were extracted for the evaluations. The signal-to-noise ratio (SNR) was calculated in each of the extracted images from which the mean value of the SNR and its standard deviation (SD) were calculated for each technique. The mean values and the standard deviations were 4.36 (SD 3.40) for CST, 6.34 (SD 3.62) for AIST, and 3.55 (SD 1.27) for DAF. AIST had a higher SNR compared to CST in almost all frames. Although DAF yielded the smallest mean SNR value, it yielded the best SNR in those frames in which the microcatheter tip did not move between frames. We conclude that AIST provides the best SNR for a moving microcatheter tip and that DAF is optimal for a temporarily stationary microcatheter tip.
PMCID: PMC3043922  PMID: 16946988
Microcatheter tracking; enhancement technique; subtraction technique; signal-to-noise ratio; comparison of techniques; fluorography; endovascular intervention
8.  The Impact of PACS on Radiologists’ Work Practice 
Journal of Digital Imaging  2006;20(4):411-421.
This paper identifies and analyzes how the implementation and use of picture archiving and communication system impacts radiologists’ work practice. The study is longitudinal from 1999 to 2005 and have a qualitative perspective were data were collected by structured interviews in a total of 46. The interviews were transcribed, analyzed, and coded using grounded theory as an organizing principle. In radiologists’ work practice, three main categories were defined: professional role, diagnostic practice, and technology in use. The changing trends within the professional role indicated that radiologists moved from a more individual professional expertise to become more of an actor in a network. The diagnostic practice changed, as reading x-ray films was seen as an art form in 1999, requiring years of training. Once everyone could view digital images, including 3-dimensional technology, it was easier for other clinicians to see and interpret the images and the skills become accessible to everyone. The change in technology in use as a result of the shift to digital images led to an increased specialization of the radiologist.
PMCID: PMC3043924  PMID: 17191101
Radiology information systems; organization and administration; trends; professional practice; health care; PACS
9.  Correlation of PET/CT Standardized Uptake Value Measurements Between Dedicated Workstations and a PACS-Integrated Workstation System 
Journal of Digital Imaging  2006;20(3):307-313.
This study was conducted to evaluate the clinical utility of a Positron Emission Tomography/Computed Tomography (PET/CT) analysis module of a picture archiving communication system (PACS) workstation in comparison to a dedicated PET/CT interpretation workstation.
Materials and Methods
The study included 32 consecutive patients referred for an [18F] Fluro-2-Deoxy-D-Glucose (18F-FDG) PET/CT at our institution. Images were reviewed at dedicated PET/CT and at PACS-integrated workstations. Mean standardized uptake values (SUVs) were calculated for the liver and the lung. Maximum SUVs were recorded for the bladder and an index lesion with the highest FDG uptake. The time spent for SUV measurements was recorded. Correlation of the SUV measurements was calculated with the Pearson coefficient.
Pearson coefficients between the workstations ranged from 0.96 to 0.99 for bladder and lesion maximum SUVs. For liver and lung average SUVs, the coefficients varied from 0.53 to 0.98. The mean time spent to perform the four SUV measurements was 122.6 s for the dedicated workstations and 134.6 s for the PACS-integrated system.
The correlation of SUV measurements between dedicated PET/CT and PACS-integrated workstations is very good, especially for maximum SUVs. For routine reading of PET/CT scans, a PACS workstation with a PET/CT analysis module offers an excellent alternative to the use of a dedicated PET/CT workstation.
PMCID: PMC3043894  PMID: 16972011
PET/CT; PACS; workstation; SUV
10.  Pseudonymization of Radiology Data for Research Purposes 
Journal of Digital Imaging  2006;20(3):284-295.
Medical image processing methods and algorithms, developed by researchers, need to be validated and tested. Test data would ideally be real clinical data especially that clinical data is varied and exists in large volumes. Nowadays, clinical data is accessible electronically and has important value for researchers. However, the usage of clinical data for research purposes should respect data confidentiality, patient right to privacy, and patient consent. In fact, clinical data is nominative given that it contains information about the patient such as name, age, and identification number. Evidently, clinical data needs to be de-identified to be exported to research databases. However, the same patient is usually followed during a long period of time. The disease progression and the diagnostic evolution represent extremely valuable information for researchers as well. Our objective is to build a research database from de-identified clinical data while enabling the data set to be easily incremented by exporting new pseudonymous data, acquired over a long period of time. Pseudonymization is data de-identification, such that data belonging to an individual in the clinical environment still belong to the same individual in the de-identified research version. In this paper, we explore various software architectures to enable the implementation of an imaging research database that can be incremented in time. We also evaluate their security and discuss their security pitfalls. As most imaging data accessible electronically is available with the digital imaging and communication in medicine (DICOM) standard, we propose a de-identification scheme that closely follows DICOM recommendations. Our work can be used to enable electronic health record (EHR) secondary usage such as public surveillance and research, while maintaining patient confidentiality.
PMCID: PMC3043895  PMID: 17191099
Research database; confidentiality; security; privacy; de-identification; pseudonymization; nominative health care data; radiology; medical imaging
11.  Part 1. Automated Change Detection and Characterization in Serial MR Studies of Brain-Tumor Patients 
Journal of Digital Imaging  2006;20(3):203-222.
The goal of this study was to create an algorithm which would quantitatively compare serial magnetic resonance imaging studies of brain-tumor patients. A novel algorithm and a standard classify–subtract algorithm were constructed. The ability of both algorithms to detect and characterize changes was compared using a series of digital phantoms. The novel algorithm achieved a mean sensitivity of 0.87 (compared with 0.59 for classify–subtract) and a mean specificity of 0.98 (compared with 0.92 for classify–subtract) with regard to identification of voxels as changing or unchanging and classification of voxels into types of change. The novel algorithm achieved perfect specificity in seven of the nine experiments. The novel algorithm was additionally applied to a short series of clinical cases, where it was shown to identify visually subtle changes. Automated change detection and characterization could facilitate objective review and understanding of serial magnetic resonance imaging studies in brain-tumor patients.
PMCID: PMC3043896  PMID: 17216385
Brain tumor; serial imaging; change detection
12.  Usefulness of Texture Analysis for Computerized Classification of Breast Lesions on Mammograms 
Journal of Digital Imaging  2006;20(3):248-255.
This work presents the usefulness of texture features in the classification of breast lesions in 5518 images of regions of interest, which were obtained from the Digital Database for Screening Mammography that included microcalcifications, masses, and normal cases. Sixteen texture features were used, i.e., 13 were based on the spatial gray-level dependence matrix and 3 on the wavelet transform. The nonparametric K-NN classifier was used in the classification stage. The results obtained from receiver operating characteristic analysis indicated that the texture features can be used for separating normal regions and lesions with masses and microcalcifications, yielding the area under the curve (AUC) values of 0.957 and 0.859, respectively. However, the texture features were not very effective for distinguishing between malignant and benign lesions because the AUC was 0.617 for masses and 0.607 for microcalcifications. The study showed that the texture features can be used for the detection of suspicious regions in mammograms.
PMCID: PMC3043897  PMID: 17122993
Mammography; computer-aided diagnosis; texture analysis
13.  Standardize and Compare Contrast-enhanced Ultrasonographic Digital Images Obtained with Different Technologies: How to Overcome the Subjectivity 
Journal of Digital Imaging  2006;20(3):256-262.
This study was conducted to compare digital images obtained with cadence contrast pulse sequencing (CPS) and coherent contrast imaging (CCI) technologies for contrast-enhanced ultrasonography (CEUS).
A CEUS study on 17 focal liver lesions was performed using CPS and CCI technologies with a second-generation contrast media. The lesion/liver ratio and conspicuity index were then calculated and compared with Adobe Photoshop 6.0.
Lesion/liver ratio and conspicuity index using CCI ranged from 1.3 to 7.1 (mean value, 3) and 19 to 127 (mean value, 58), respectively; by using CPS, we obtained results ranging from 2 to 19.1 (mean value, 8.9) and 57 to 164 (mean value, 109.2). Lesion/liver ratio and the conspicuity index for the lesions using CPS showed significantly (p < 0.0001) superior results than those obtained using CCI.
The computed analysis with standardization allows an objective evaluation of digital images of CEUS. CPS technology resulted in better lesion conspicuity compared to CCI during CEUS study on focal liver lesions.
PMCID: PMC3043898  PMID: 17021927
Ultrasound; contrast-enhanced; focal liver lesion; standardization; adobe photoshop
14.  ‘Wet Reads’ in the Age of PACS: Technical and Workflow Considerations for a Preliminary Report System 
Journal of Digital Imaging  2006;20(3):296-306.
Communication between clinicians, technologists, and radi ologists has become more complex, with Picture Archiving and Communication Systems (PACS) now allowing the radiologist to be removed from the physical location of the patients and the site of imaging. With these changes, effective communication becomes an ongoing challenge. Efficient communication of study interpretations has also become a priority for radiologists as they struggle to maintain relevance and provide added value to patient care when clinicians have ready access to radiology images. The purpose of this paper is to share our experience in developing and implementing the Collaborative Notification System (CNS), a communication tool used to inform referring clinicians of urgent findings—a.k.a. “wet reads.” The system utilizes a system of web pages integrated into PACS for the sending and receiving of succinct messages to provide clinical information at the point of need. A second system of pager alerts provides notification of the need for such communication through a relatively unintrusive, one-way, acknowledged alert system. The CNS provides asynchronous, integrated communication for the reporting of urgent and emergent radiology findings in a complex, geographically distributed medical environment.
PMCID: PMC3043899  PMID: 17191102
Workflow; emergency radiology; asynchronous communication; PACS
15.  Fractal Analysis of Contours of Breast Masses in Mammograms 
Journal of Digital Imaging  2006;20(3):223-237.
Fractal analysis has been shown to be useful in image processing for characterizing shape and gray-scale complexity. Breast masses present shape and gray-scale characteristics that vary between benign masses and malignant tumors in mammograms. Limited studies have been conducted on the application of fractal analysis specifically for classifying breast masses based on shape. The fractal dimension of the contour of a mass may be computed either directly from the 2-dimensional (2D) contour or from a 1-dimensional (1D) signature derived from the contour. We present a study of four methods to compute the fractal dimension of the contours of breast masses, including the ruler method and the box counting method applied to 1D and 2D representations of the contours. The methods were applied to a data set of 111 contours of breast masses. Receiver operating characteristics (ROC) analysis was performed to assess and compare the performance of fractal dimension and four previously developed shape factors in the classification of breast masses as benign or malignant. Fractal dimension was observed to complement the other shape factors, in particular fractional concavity, in the representation of the complexity of the contours. The combination of fractal dimension with fractional concavity yielded the highest area (Az) under the ROC curve of 0.93; the two measures, on their own, resulted in Az values of 0.89 and 0.88, respectively.
PMCID: PMC3043900  PMID: 17021926
Box counting method; breast cancer; breast masses; breast tumors; contour analysis; fractal analysis; fractal dimension; ruler method; shape analysis; signatures of contours
16.  Contrast Enhancement in Dense Breast Images to Aid Clustered Microcalcifications Detection 
Journal of Digital Imaging  2006;20(1):53-66.
This paper presents a method to provide contrast enhancement in dense breast digitized images, which are difficult cases in testing of computer-aided diagnosis (CAD) schemes. Three techniques were developed, and data from each method were combined to provide a better result in relation to detection of clustered microcalcifications. Results obtained during the tests indicated that, by combining all the developed techniques, it is possible to improve the performance of a processing scheme designed to detect microcalcification clusters. It also allows operators to distinguish some of these structures in low-contrast images, which were not detected via conventional processing before the contrast enhancement. This investigation shows the possibility of improving CAD schemes for better detection of microcalcifications in dense breast images.
PMCID: PMC3043882  PMID: 16820957
Image processing; contrast enhancement; dense breasts; microcalcifications; mammography
17.  A CT Database for Research, Development and Education: Concept and Potential 
Journal of Digital Imaging  2006;20(1):17-22.
Both in radiology and in surgery, numerous applications are emerging that enable 3D visualization of data from various imaging modalities. In clinical practice, the patient's images are analyzed on work stations in the Radiology Department. For specific preclinical and educational applications, however, data from single patients are insufficient. Instead, similar scans from a number of individuals within a collective must be compiled. The definition of standardized acquisition procedures and archiving formats are prerequisite for subsequent analysis of multiple data sets.
Focusing on bone morphology, we describe our concept of a computer database of 3D human bone models obtained from computed tomography (CT) scans. We further discuss and illustrate deployment areas ranging from prosthesis design, over virtual operation simulation up to 3D anatomy atlases. The database of 3D bone models described in this work, created and maintained by the AO Development Institute, may be accessible to research institutes on request.
PMCID: PMC3043884  PMID: 16897321
Bone; biometrics; morphology; computed tomography; database; education
18.  Diagnostic Contribution of Virtual Endoscopy in Diseases of the Upper Airways 
Journal of Digital Imaging  2006;20(1):67-71.
Virtual endoscopy (VE) is a new diagnostic tool that generates 3-dimensional (3D) views of a lumen by exploiting cross-sectional images. The purpose of this study was to evaluate the usefulness of VE as a diagnostic tool in the diseases of the larynx and pharynx.
Materials and Methods
Twenty-two patients with a mean age of 57 years were included in the study. The patients underwent larynx examination, optical endoscopy (OE), and computed tomography (CT) of the larynx. Later, VE was produced from the CT images.
Eight patients had larynx carcinoma, a 5-year-old patient had a laryngeal web, a 43-year-old man had fish bone stuck in his submucosal layer, 10 patients were normal, and the remaining two patients were under follow-up for treated nasopharynx carcinoma and had no evidence for recurrence. VE showed the laryngeal tumor in seven patients and the laryngeal web in one patient, but failed to show a plaquelike tumor and the fishbone within the submucosa.
Our findings suggest that VE is a useful and complimentary method of 3D imaging in the diseases compromising the laryngeal lumen. Furthermore, it may be superior to OE in severe stenosis or obstructions where the endoscope cannot be passed through.
PMCID: PMC3043886  PMID: 16946987
Virtual endoscopy; larynx; pharynx; CT; imaging; optical endoscopy
19.  A Presentation System for Just-in-time Learning in Radiology 
Journal of Digital Imaging  2006;20(1):6-16.
There is growing interest in bringing medical educational materials to the point of care. We sought to develop a system for just-in-time learning in radiology. A database of 34 learning modules was derived from previously published journal articles. Learning objectives were specified for each module, and multiple-choice test items were created. A web-based system—called TEMPO—was developed to allow radiologists to select and view the learning modules. Web services were used to exchange clinical context information between TEMPO and the simulated radiology work station. Preliminary evaluation was conducted using the System Usability Scale (SUS) questionnaire. TEMPO identified learning modules that were relevant to the age, sex, imaging modality, and body part or organ system of the patient being viewed by the radiologist on the simulated clinical work station. Users expressed a high degree of satisfaction with the system’s design and user interface. TEMPO enables just-in-time learning in radiology, and can be extended to create a fully functional learning management system for point-of-care learning in radiology.
PMCID: PMC3043887  PMID: 16960683
Just-in-time learning; continuing medical education (CME); decision support; education; PACS; systems integration; radiology workflow
20.  Creation of a CT Image Library for the Lung Screening Study of the National Lung Screening Trial 
Journal of Digital Imaging  2006;20(1):23-31.
The CT Image Library (CTIL) of the Lung Screening Study (LSS) network of the National Lung Screening Trial (NLST) consists of up to three annual screens using CT imaging from each of 17,308 participants with a significant history of smoking but no evidence of cancer at trial enrollment (Fall 2002–Spring 2004). Screens performed at numerous medical centers associated with 10 LSS-NLST screening centers are deidentified of protected health information and delivered to the CTIL via DVD, external hard disk, or Internet/Virtual Private Network transmission. The collection will be completed in late 2006. The CTIL is of potential interest to clinical researchers and software developers of nodule detection algorithms. Its attractiveness lies in its very specific, well-defined patient population, scanned via a common CT protocol, and in its collection of evenly spaced serial screens. In this work, we describe the technical details of the CTIL collection process from screening center retrieval through library storage.
PMCID: PMC3043889  PMID: 16783598
CT image library; NLST; NCI; CTIL; clinical trial; lung cancer
21.  Bio-Image Warehouse System: Concept and Implementation of a Diagnosis-Based Data Warehouse for Advanced Imaging Modalities in Neuroradiology 
Journal of Digital Imaging  2006;20(1):32-41.
Advanced neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), chemical shift spectroscopy imaging (CSI), diffusion tensor imaging (DTI), and perfusion-weighted imaging (PWI) create novel challenges in terms of data storage and management: huge amounts of raw data are generated, the results of analysis may depend on the software and settings that have been used, and most often intermediate files are inherently not compliant with the current DICOM (digital imaging and communication in medicine) standard, as they contain multidimensional complex and tensor arrays and various other types of data structures. A software architecture, referred to as Bio-Image Warehouse System (BIWS), which can be used alongside a radiology information system/picture archiving and communication system (RIS/PACS) system to store neuroimaging data for research purposes, is presented. The system architecture is conceived with the purpose of enabling to query by diagnosis according to a predefined two-layered classification taxonomy. The operational impact of the system and the time needed to get acquainted with the web-based interface and with the taxonomy are found to be limited. The development of modules enabling automated creation of statistical templates is proposed.
PMCID: PMC3043890  PMID: 16953339
PACS; image repository; data warehouse; web-based; neuroradiology
22.  A Comparative Study of Conventional Mammography Film Interpretations with Soft Copy Readings of the Same Examinations 
Journal of Digital Imaging  2006;20(1):42-52.
An acceptable mammography film digitizer must provide high-quality images at a level of diagnostic accuracy comparable to reading conventional film examinations. The purpose of this study was to determine if there are significant differences between the interpretations of conventional film-screen mammography examinations and soft copy readings of the images produced by a mammography film digitizer. Eight radiologists interpreted 120 mammography examinations, half as original films and the other half as digital images on a soft copy work station. No radiologist read the same examination twice. The interpretations were recorded in accordance with the Breast Imaging Reporting and Data System and included other variables such as perceived image quality and diagnostic difficulty and confidence. The results provide support for the hypothesis that there are no significant differences between the interpretations of conventional film-screen mammography examinations and soft copy examinations produced by a mammography film digitizer.
PMCID: PMC3043891  PMID: 17191103
Digitized mammography; interpreting digitized mammography images; transition to digital imaging; ROC-based analysis
23.  White Matter Fiber Tracking Computation Based on Diffusion Tensor Imaging for Clinical Applications 
Journal of Digital Imaging  2006;20(1):88-97.
Fiber tracking allows the in vivo reconstruction of human brain white matter fiber trajectories based on magnetic resonance diffusion tensor imaging (MR-DTI), but its application in the clinical routine is still in its infancy. In this study, we present a new software for fiber tracking, developed on top of a general-purpose DICOM (digital imaging and communications in medicine) framework, which can be easily integrated into existing picture archiving and communication system (PACS) of radiological institutions. Images combining anatomical information and the localization of different fiber tract trajectories can be encoded and exported in DICOM and Analyze formats, which are valuable resources in the clinical applications of this method. Fiber tracking was implemented based on existing line propagation algorithms, but it includes a heuristic for fiber crossings in the case of disk-shaped diffusion tensors. We successfully performed fiber tracking on MR-DTI data sets from 26 patients with different types of brain lesions affecting the corticospinal tracts. In all cases, the trajectories of the central spinal tract (pyramidal tract) were reconstructed and could be applied at the planning phase of the surgery as well as in intraoperative neuronavigation.
PMCID: PMC3043892  PMID: 16946990
Diffusion tensor imaging; fiber tracking; pyramidal tract; cortico-spinal tracts; neurosurgical planning and navigation; DICOM
24.  Three-Dimensional Segmentation of the Tumor in Computed Tomographic Images of Neuroblastoma 
Journal of Digital Imaging  2006;20(1):72-87.
Segmentation of the tumor in neuroblastoma is complicated by the fact that the mass is almost always heterogeneous in nature; furthermore, viable tumor, necrosis, and normal tissue are often intermixed. Tumor definition and diagnosis require the analysis of the spatial distribution and Hounsfield unit (HU) values of voxels in computed tomography (CT) images, coupled with a knowledge of normal anatomy. Segmentation and analysis of the tissue composition of the tumor can assist in quantitative assessment of the response to therapy and in the planning of delayed surgery for resection of the tumor. We propose methods to achieve 3-dimensional segmentation of the neuroblastic tumor. In our scheme, some of the normal structures expected in abdominal CT images are delineated and removed from further consideration; the remaining parts of the image volume are then examined for the tumor mass. Mathematical morphology, fuzzy connectivity, and other image processing tools are deployed for this purpose. Expert knowledge provided by a radiologist in the form of the expected structures and their shapes, HU values, and radiological characteristics are incorporated into the segmentation algorithm. In this preliminary study, the methods were tested with 10 CT exams of four cases from the Alberta Children's Hospital. False-negative error rates of less than 12% were obtained in eight of the 10 exams; however, seven of the exams had false-positive error rates of more than 20% with respect to manual segmentation of the tumor by a radiologist.
PMCID: PMC3043888
3D image segmentation; neuroblastoma; computed tomography; fuzzy connectivity; tumor segmentation
25.  Study of Automatic Enhancement for Chest Radiograph 
Journal of Digital Imaging  2006;19(4):371-375.
Because of the large difference of the densities in the lung and other structures, the chest x-ray image behaves as a wide-range intensity distribution, which brings on a bit of difficulty to investigate the focus. In the paper, according to the intensity properties of the chest radiograph, the chest radiographic image is divided into three subregions, and a piecewise linear transformation model is established. An approach of automatic enhancement is presented, based on the gray-level normalization. The average enhanced ratios of three subregions of the normal and severe acute respiratory syndrome image are increased by 10.70% and 25.55%, respectively. The technique is proved to be effective through the evaluation of the improved images.
PMCID: PMC3045154  PMID: 16752044
Medical image processing; image enhancement; chest radiograph; SARS

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