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1.  Multisite Image Data Collection and Management Using the RSNA Image Sharing Network 
Translational Oncology  2014;7(1):36-39.
The execution of a multisite trial frequently includes image collection. The Clinical Trials Processor (CTP) makes removal of protected health information highly reliable. It also provides reliable transfer of images to a central review site. Trials using central review of imaging should consider using CTP for handling image data when a multisite trial is being designed.
PMCID: PMC3998697  PMID: 24772205
2.  Analysis of Hemodynamics and Aneurysm Occlusion after Flow Diverting Treatment in Rabbit Models 
Purpose
to investigate the relationship between hemodynamic conditions created immediately after flow diversion and subsequent occlusion of experimental aneurysms in rabbits.
Methods
The hemodynamic environment before and after flow diversion treatment of elastase induced aneurysms in 20 rabbits was modeled using image-based computational fluid dynamics. Local aneurysm occlusion was quantified using a voxelization technique on 3D images acquired 8 weeks after treatment. Global and local voxel-by-voxel hemodynamic variables were used to statistically compare aneurysm regions that later thrombosed to regions that remained patent.
Results
Six aneurysms remained patent at 8 weeks while 14 were completely or nearly completely occluded. Patent aneurysms had statistically larger neck sizes (p=0.0015) and smaller mean transit times (p=0.02). The velocity, vorticity and shear rate were about 2.8 times (p<0.0001) larger in patent regions, i.e. had larger “flow activity”, than regions that progressed to occlusion. Statistical models based on local hemodynamic variables were capable of predicting local occlusion with good precision (84% accuracy), especially away from the neck (92–94%). Predictions near the neck were poorer (73% accuracy).
Conclusion
These results suggests that the dominant healing mechanism of occlusion within the aneurysm dome are related to slow flow induced thrombosis while near the neck other processes could be at play simultaneously.
doi:10.3174/ajnr.A3913
PMCID: PMC4212815  PMID: 24722302
cerebral aneurysm; flow diversion; hemodynamics; thrombosis
3.  Emerging Trends in the Volume and Format of Outside Examinations Submitted for Secondary Interpretation 
OBJECTIVE
The purpose of this article is to describe the trends of secondary interpretations, including the total volume and format of cases.
MATERIALS AND METHODS
This retrospective study involved all outside neuroradiology examinations submitted for secondary interpretation from November 2006 through December 2010. This practice utilizes consistent criteria and includes all images that cover the brain, neck, and spine. For each month, the total number of outside examinations and their format (i.e., hard-copy film, DICOM CD-ROM, or non-DICOM CD-ROM) were recorded.
RESULTS
There was no significant change in the volume of cases (1043 ± 131 cases/month; p = 0.46, two-sided Student t test). There was a significant decrease in the volume of hard-copy films submitted, with the mean number of examinations submitted per month on hard-copy film declining from 297 in 2007 to 57 in 2010 (p < 0.0001, Student t test). This decrease was mirrored by an increase in the mean number of cases submitted on CD-ROM (753 cases/month in 2007 and 1036 cases/month in 2010; p < 0.0001). Although most were submitted in DICOM format, there was almost a doubling of the volume of cases submitted on non-DICOM CD-ROM (mean number of non-DICOM CD-ROMs, nine cases/month in 2007 and 17 cases/month in 2010; p < 0.001).
CONCLUSION
There has been a significant decrease in the number of hard-copy films submitted for secondary interpretation. There has been almost a doubling of the volume of cases submitted in non-DICOM formats, which is unfortunate, given the many advantages of the internationally derived DICOM standard, including ease of archiving, standardized display, efficient review, improved interpretation, and quality of patient care.
doi:10.2214/AJR.11.7512
PMCID: PMC4030431  PMID: 22451538
DICOM format; outside examination practice; secondary interpretation
4.  Standards for Business Analytics and Departmental Workflow 
Journal of Digital Imaging  2012;26(1):53-57.
Efficient workflow is essential for a successful business. However, there is relatively little literature on analytical tools and standards for defining workflow and measuring workflow efficiency. Here, we describe an effort to define a workflow lexicon for medical imaging departments, including the rationale, the process, and the resulting lexicon.
doi:10.1007/s10278-012-9533-z
PMCID: PMC3553366  PMID: 23065122
Workflow; Cost-effectiveness; Controlled vocabulary; Data mining; Radiology workflow; Workflow re-engineering
5.  Towards a More Cloud-Friendly Medical Imaging Applications Architecture: A Modest Proposal 
Journal of Digital Imaging  2012;26(1):58-64.
Recent information technology literature, in general, and radiology trade journals, in particular, are rife with allusions to the “cloud” suggesting that moving one’s compute and storage assets into someone else’s data center magically solves cost, performance, and elasticity problems. More likely, one is only trading one set of problems for another, including greater latency (aka slower turnaround times) since the image data must now leave the local area network and travel longer paths via encrypted tunnels. To offset this, an imaging system design is needed that reduces the number of high-latency image transmissions, yet can still leverage cloud strengths. This work explores the requirements for such a design.
doi:10.1007/s10278-012-9545-8
PMCID: PMC3553370  PMID: 23135215
DICOM; IHE; Cloud; WADO
7.  Imaging Infrastructure for Research. Part 2. Data Management Practices 
Journal of Digital Imaging  2012;25(5):566-569.
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.
doi:10.1007/s10278-012-9502-6
PMCID: PMC3447091  PMID: 22710986
Clinical trials; Research image database
8.  Imaging in Clinical Trials 
Cancer Informatics  2007;4:13-18.
PMCID: PMC2666946  PMID: 19390660
10.  Whitepapers on Imaging Infrastructure for Research 
Journal of Digital Imaging  2012;25(4):449-453.
doi:10.1007/s10278-012-9490-6
PMCID: PMC3389084  PMID: 22644338
11.  Pituitary Stalk Interruption Syndrome in Chinese People: Clinical Characteristic Analysis of 55 Cases 
PLoS ONE  2013;8(1):e53579.
Objective
Pituitary stalk interruption syndrome (PSIS) is characterized by the absence of pituitary stalk, pituitary hypoplasia, and ectopic posterior pituitary. Due to the rarity of PSIS, clinical data are limited, especially in Chinese people. Herein, we analyzed the clinical characteristics of patients diagnosed with PSIS from our center over 10 years.
Patients and Methods
We retrospectively analyzed the clinical manifestations and laboratory and MRI findings in 55 patients with PSIS.
Results
Of the 55 patients with PSIS, 48 (87.3%) were male. The average age was 19.7±6.7 years and there was no familial case. A history of breech delivery was documented in 40 of 45 patients (88.9%) and 19 of 55 patients (34.5%) had a history of dystocia. Short stature was found in 47 of 55 patients (85.5%) and bone age delayed 7.26±5.37 years. Secondary sex characteristics were poor or undeveloped in most patients. The prevalence of deficiencies in growth hormone, gonadotropins, corticotropin, and thyrotropin were 100%, 95.8%, 81.8%, 76.3%, respectively. Hyperprolactinemia was found in 36.4% of patients. Three or more pituitary hormone deficiencies were found in 92.7% of the patients. All patients had normal posterior pituitary function and absent pituitary stalk on imaging. The average height of anterior pituitary was 28 mm, documented anterior pituitary hypoplasia. Midline abnormalities were presented in 9.1% of patients.
Conclusions
The clinical features of our Chinese PSIS patients seem to be different from other reported patients in regarding to the higher degree of hypopituitarism and lower prevalence of midline defects. In addition, our patients were older at the time of case detection and the bone age was markedly delayed. We also had no cases of familial PSIS.
doi:10.1371/journal.pone.0053579
PMCID: PMC3544917  PMID: 23341953
12.  Experience with Importation of Electronic Images into the Medical Record from Physical Media 
Journal of Digital Imaging  2011;24(4):694-699.
The purpose of this article is to describe a system we developed for importing images on compact discs (CDs) from external imaging departments into our clinical image viewing system, and to report on key metrics regarding veracity of information seen on the CDs. We recommend careful attention to the process of CD importation because of the error rate we have seen. We developed a system and process for importing images on CD into our EMR. The importation system scans the CD for digital imaging and communications in medicine (DICOM) images, and collects all patient information seen. That information is presented to the patient for verification. Once validated, the image data is copied into our clinical viewing system. The importation system includes facilities for collecting instances of incorrect data. About 90% of images are now exchanged between our healthcare enterprise and other entities via CD. Data for the wrong patient (e.g., the wrong CD) is seen in about 0.1% of cases, and a similar number of CDs have data for more than one patient on the CD(s) the patient bring to our facility. Most data are now exchanged via DICOM files. DICOM images burned onto CD media are now commonly used for image exchange. However, applications to import DICOM images are not enough. One must implement a process to assure high confidence that the data imported belongs to the patient you are importing.
doi:10.1007/s10278-011-9364-3
PMCID: PMC3138924  PMID: 21286776
Digital imaging and communications in medicine (DICOM); Electronic medical record (EMR); Image distribution; Integrating healthcare enterprise (IHE); Medical record linkage; Teleradiology; Workflow re-engineering
13.  Beneficial Plasma Exchange Response in CNS Inflammatory Demyelination 
Archives of neurology  2011;68(7):870-878.
Background
Plasma exchange (PLEX) is a beneficial rescue therapy for acute, steroid-refractory central nervous system inflammatory demyelinating disease (CNS-IDD). Despite the ~45% PLEX response rate reported among CNS-IDD patients, determinants of interindividual differences in PLEX response are not well characterized.
Objective
To perform an exploratory analysis of clinical, radiographic, and serological features associated with beneficial PLEX response.
Design
Historical cohort study
Setting
Neurology practice, Mayo Clinic College of Medicine, Rochester, Minnesota.
Patients
All Mayo Clinic patients treated with PLEX between1990–2007 for a steroid-refractory CNS-IDD attack.
Main Outcome Measure
The primary outcome was PLEX response in attack-related, targeted neurological deficit(s) (TND) assessed within the 6 month period following PLEX.
Results
We identified 153 patients treated with PLEX for a steroid-refractory CNS-IDD, of whom 90 (59%) exhibited moderate to marked functional neurological improvement within 6 months following treatment. Pre-PLEX clinical features associated with a beneficial PLEX response were shorter disease duration (p=0.02) and preserved deep tendon reflexes (p=0.001); post-PLEX variables included a diagnosis of relapsing-remitting MS (RRMS) (p=0.008) and a lower EDSS (p<0.001) at last follow-up. PLEX was less effective for MS patients who subsequently developed a progressive disease course (p=0.046). Radiographic features associated with a beneficial PLEX response were presence of ring-enhancing lesions (RELs; OR=4.0, p=0.029) and/or mass effect (OR=3.0, p=0.024). No association was found between NMO-IgG serostatus and PLEX response.
Conclusions
We have identified clinical and radiographic features which may aid in identifying those patients with fulminant, steroid-refractory CNS-IDD attacks who are more likely to respond to PLEX.
doi:10.1001/archneurol.2011.34
PMCID: PMC3134547  PMID: 21403003
14.  Computer-Aided Detection of Intracranial Aneurysms in MR Angiography 
Journal of Digital Imaging  2009;24(1):86-95.
Intracranial aneurysms represent a significant cause of morbidity and mortality. While the risk factors for aneurysm formation are known, the detection of aneurysms remains challenging. Magnetic resonance angiography (MRA) has recently emerged as a useful non-invasive method for aneurysm detection. However, even for experienced neuroradiologists, the sensitivity to small (<5 mm) aneurysms in MRA images is poor, on the order of 30~60% in recent, large series. We describe a fully automated computer-aided detection (CAD) scheme for detecting aneurysms on 3D time-of-flight (TOF) MRA images. The scheme locates points of interest (POIs) on individual MRA datasets by combining two complementary techniques. The first technique segments the intracranial arteries automatically and finds POIs from the segmented vessels. The second technique identifies POIs directly from the raw, unsegmented image dataset. This latter technique is useful in cases of incomplete segmentation. Following a series of feature calculations, a small fraction of POIs are retained as candidate aneurysms from the collected POIs according to predetermined rules. The CAD scheme was evaluated on 287 datasets containing 147 aneurysms that were verified with digital subtraction angiography, the accepted standard of reference for aneurysm detection. For two different operating points, the CAD scheme achieved a sensitivity of 80% (71% for aneurysms less than 5 mm) with three mean false positives per case, and 95% (91% for aneurysms less than 5 mm) with nine mean false positives per case. In conclusion, the CAD scheme showed good accuracy and may have application in improving the sensitivity of aneurysm detection on MR images.
doi:10.1007/s10278-009-9254-0
PMCID: PMC3046787  PMID: 19937083
Computer-aided detection (CAD); magnetic resonance angiography (MRA); intracranial aneurysm; aneurysm detection
15.  Perivenous demyelination: association with clinically defined acute disseminated encephalomyelitis and comparison with pathologically confirmed multiple sclerosis 
Brain  2010;133(2):333-348.
Distinction between acute disseminated encephalomyelitis and acute multiple sclerosis is often clinically difficult. Perivenous demyelination is the pathological hallmark of acute disseminated encephalomyelitis, whereas confluent demyelination is the hallmark of acute multiple sclerosis. We investigated whether perivenous demyelination versus confluent demyelination distinguishes acute disseminated encephalomyelitis from multiple sclerosis. Patients with perivenous demyelination (n = 13; median age 43 years, range 5–67) on brain biopsy and/or autopsy, ascertained retrospectively, were compared with a cohort with confluent demyelination only (n = 91; 84% multiple sclerosis, 16% isolated syndrome at follow-up; median age 39 years, range 10–69). Clinical presentation, course and the International Paediatric Multiple Sclerosis Study Group clinical criteria for acute disseminated encephalomyelitis were assessed in both cohorts. Among the perivenous demyelination cohort, 10 patients had only perivenous demyelination and three also had confluent demyelination. All but one patient with perivenous demyelination only had a monophasic course, whereas two of three with both types had a relapsing course. The perivenous demyelination cohort was more likely than the confluent demyelination cohort to present with encephalopathy (P < 0.001), depressed level of consciousness (P < 0.001), headache (P < 0.001), meningismus (P = 0.04), cerebrospinal fluid pleocytosis (P = 0.04) or multifocal enhancing magnetic resonance imaging lesions (P < 0.001). A distinct pattern of cortical microglial activation and aggregation without associated cortical demyelination was found among six perivenous demyelination patients, all of whom had encephalopathy and four of whom had depressed level of consciousness. This pattern of cortical pathology was not observed in the confluent demyelination cohort, even in one patient with depressed level of consciousness. Clinical criteria were 80% sensitive and 91% specific for pathologically defined acute disseminated encephalomyelitis (perivenous demyelination), but misdiagnosed acute disseminated encephalomyelitis among 9% of patients with confluent demyelination and multiple sclerosis diagnosis at last follow-up. Perivenous demyelination is associated with meningoencephalopathic presentations and a monophasic course. Depressed level of consciousness is a more specific clinical criterion for pathologically confirmed acute disseminated encephalomyelitis than encephalopathy, which over-diagnosed acute disseminated encephalomyelitis among multiple sclerosis patients. A distinct pattern of cortical microglial activation without cortical demyelination may be the pathological correlate of depressed level of consciousness in acute disseminated encephalomyelitis. Although pathological evidence of perivenous demyelination may be superior to clinical criteria for diagnosing acute disseminated encephalomyelitis, the co-occurrence of perivenous and confluent demyelination in some individuals suggests pathogenic overlap between acute disseminated encephalomyelitis and multiple sclerosis and misclassification even with biopsy.
doi:10.1093/brain/awp321
PMCID: PMC2822631  PMID: 20129932
multiple sclerosis; magnetic resonance imaging; neuropathology; immune-mediated demyelination; demyelinating disease
16.  FLAIR Histogram Segmentation for Measurement of Leukoaraiosis Volume 
The purpose of this study was to develop a method to measure brain and white matter hyperintensity (leukoaraiosis) volume that is based on the segmentation of the intensity histogram of fluid attenuated inversion recovery (FLAIR) images, and to assess the accuracy and reproducibility of the method. Whole head synthetic image phantoms with manually introduced leukoaraiosis lesions of varying severity were constructed. These synthetic image phantom sets incorporated image contrast and anatomic features which mimicked leukoaraiosis found in real life. One set of synthetic image phantoms was used to develop the segmentation algorithm (FLAIR-histoseg). A second set was used to measure its accuracy. Test re-test reproducibility was assessed in 10 elderly volunteers who were imaged twice. The mean absolute error of the FLAIR-histoseg method for measurement of leukoaraiosis volume was 6.6% and for brain volume 1.4%. The mean test re-test coefficient of variation for leukoaraiosis volume was 1.4% and for brain volume was 0.3%. We conclude that the FLAIR-histoseg method is an accurate and reproducible method for measuring leukoaraiosis and whole brain volume in elderly subjects.
PMCID: PMC2755497  PMID: 11747022
quantitative MRI; pulse sequences; segmentation; white matter disease; dementia
17.  A Multicenter Observer Performance Study of 3D JPEG2000 Compression of Thin-Slice CT 
Journal of Digital Imaging  2009;23(5):639-643.
The goal of this study was to determine the compression level at which 3D JPEG2000 compression of thin-slice CTs of the chest and abdomen–pelvis becomes visually perceptible. A secondary goal was to determine if residents in training and non-physicians are substantially different from experienced radiologists in their perception of compression-related changes. This study used multidetector computed tomography 3D datasets with 0.625–1-mm thickness slices of standard chest, abdomen, or pelvis, clipped to 12 bits. The Kakadu v5.2 JPEG2000 compression algorithm was used to compress and decompress the 80 examinations creating four sets of images: lossless, 1.5 bpp (8:1), 1 bpp (12:1), and 0.75 bpp (16:1). Two randomly selected slices from each examination were shown to observers using a flicker mode paradigm in which observers rapidly toggled between two images, the original and a compressed version, with the task of deciding whether differences between them could be detected. Six staff radiologists, four residents, and six PhDs experienced in medical imaging (from three institutions) served as observers. Overall, 77.46% of observers detected differences at 8:1, 94.75% at 12:1, and 98.59% at 16:1 compression levels. Across all compression levels, the staff radiologists noted differences 64.70% of the time, the resident’s detected differences 71.91% of the time, and the PhDs detected differences 69.95% of the time. Even mild compression is perceptible with current technology. The ability to detect differences does not equate to diagnostic differences, although perception of compression artifacts could affect diagnostic decision making and diagnostic workflow.
doi:10.1007/s10278-009-9221-9
PMCID: PMC2950269  PMID: 19603232
3D imaging; compression; JPEG2000; observer performance
18.  A Multicenter Observer Performance Study of 3D JPEG2000 Compression of Thin-Slice CT 
Journal of Digital Imaging  2009;23(5):639-643.
The goal of this study was to determine the compression level at which 3D JPEG2000 compression of thin-slice CTs of the chest and abdomen–pelvis becomes visually perceptible. A secondary goal was to determine if residents in training and non-physicians are substantially different from experienced radiologists in their perception of compression-related changes. This study used multidetector computed tomography 3D datasets with 0.625–1-mm thickness slices of standard chest, abdomen, or pelvis, clipped to 12 bits. The Kakadu v5.2 JPEG2000 compression algorithm was used to compress and decompress the 80 examinations creating four sets of images: lossless, 1.5 bpp (8:1), 1 bpp (12:1), and 0.75 bpp (16:1). Two randomly selected slices from each examination were shown to observers using a flicker mode paradigm in which observers rapidly toggled between two images, the original and a compressed version, with the task of deciding whether differences between them could be detected. Six staff radiologists, four residents, and six PhDs experienced in medical imaging (from three institutions) served as observers. Overall, 77.46% of observers detected differences at 8:1, 94.75% at 12:1, and 98.59% at 16:1 compression levels. Across all compression levels, the staff radiologists noted differences 64.70% of the time, the resident’s detected differences 71.91% of the time, and the PhDs detected differences 69.95% of the time. Even mild compression is perceptible with current technology. The ability to detect differences does not equate to diagnostic differences, although perception of compression artifacts could affect diagnostic decision making and diagnostic workflow.
doi:10.1007/s10278-009-9221-9
PMCID: PMC2950269  PMID: 19603232
3D imaging; compression; JPEG2000; observer performance
19.  Discerning Tumor Status from Unstructured MRI Reports—Completeness of Information in Existing Reports and Utility of Automated Natural Language Processing 
Journal of Digital Imaging  2009;23(2):119-132.
Information in electronic medical records is often in an unstructured free-text format. This format presents challenges for expedient data retrieval and may fail to convey important findings. Natural language processing (NLP) is an emerging technique for rapid and efficient clinical data retrieval. While proven in disease detection, the utility of NLP in discerning disease progression from free-text reports is untested. We aimed to (1) assess whether unstructured radiology reports contained sufficient information for tumor status classification; (2) develop an NLP-based data extraction tool to determine tumor status from unstructured reports; and (3) compare NLP and human tumor status classification outcomes. Consecutive follow-up brain tumor magnetic resonance imaging reports (2000–­2007) from a tertiary center were manually annotated using consensus guidelines on tumor status. Reports were randomized to NLP training (70%) or testing (30%) groups. The NLP tool utilized a support vector machines model with statistical and rule-based outcomes. Most reports had sufficient information for tumor status classification, although 0.8% did not describe status despite reference to prior examinations. Tumor size was unreported in 68.7% of documents, while 50.3% lacked data on change magnitude when there was detectable progression or regression. Using retrospective human classification as the gold standard, NLP achieved 80.6% sensitivity and 91.6% specificity for tumor status determination (mean positive predictive value, 82.4%; negative predictive value, 92.0%). In conclusion, most reports contained sufficient information for tumor status determination, though variable features were used to describe status. NLP demonstrated good accuracy for tumor status classification and may have novel application for automated disease status classification from electronic databases.
doi:10.1007/s10278-009-9215-7
PMCID: PMC2837158  PMID: 19484309
Natural language processing; unstructured; structured; radiology reports; tumor status
20.  Discerning Tumor Status from Unstructured MRI Reports—Completeness of Information in Existing Reports and Utility of Automated Natural Language Processing 
Information in electronic medical records is often in an unstructured free-text format. This format presents challenges for expedient data retrieval and may fail to convey important findings. Natural language processing (NLP) is an emerging technique for rapid and efficient clinical data retrieval. While proven in disease detection, the utility of NLP in discerning disease progression from free-text reports is untested. We aimed to (1) assess whether unstructured radiology reports contained sufficient information for tumor status classification; (2) develop an NLP-based data extraction tool to determine tumor status from unstructured reports; and (3) compare NLP and human tumor status classification outcomes. Consecutive follow-up brain tumor magnetic resonance imaging reports (2000–­2007) from a tertiary center were manually annotated using consensus guidelines on tumor status. Reports were randomized to NLP training (70%) or testing (30%) groups. The NLP tool utilized a support vector machines model with statistical and rule-based outcomes. Most reports had sufficient information for tumor status classification, although 0.8% did not describe status despite reference to prior examinations. Tumor size was unreported in 68.7% of documents, while 50.3% lacked data on change magnitude when there was detectable progression or regression. Using retrospective human classification as the gold standard, NLP achieved 80.6% sensitivity and 91.6% specificity for tumor status determination (mean positive predictive value, 82.4%; negative predictive value, 92.0%). In conclusion, most reports contained sufficient information for tumor status determination, though variable features were used to describe status. NLP demonstrated good accuracy for tumor status classification and may have novel application for automated disease status classification from electronic databases.
doi:10.1007/s10278-009-9215-7
PMCID: PMC2837158  PMID: 19484309
Natural language processing; unstructured; structured; radiology reports; tumor status
21.  Effect of Automated Image Registration on Radiologist Interpretation 
Journal of Digital Imaging  2007;20(2):105-113.
In this study, we present preliminary data on the effect of automated 3D image alignment on the time to arrive at a decision about an imaging finding, the agreement of multiple of multiple observers, the prevalence of comparison examinations, and technical success rates for the image alignment algorithm. We found that automated image alignment reduced the average time to make a decision by 25% for cases where the structures are rigid, and when the scanning protocol is similar. For cases where these are not true, there is little or no benefit. In our practice, 54% of cases had prior examinations that could be automatically aligned. The overall benefit seen in our department for highly similar exams might be 20% for neuro and 10% for body; the benefit seen in other practices is likely to vary based on scanning practices and prevalence of prior examinations.
doi:10.1007/s10278-007-9023-x
PMCID: PMC3043907  PMID: 17505869
Image registration; image alignment; practice efficiency; TRIP
22.  Open Source Software Projects of the caBIG™ In Vivo Imaging Workspace Software Special Interest Group 
Journal of Digital Imaging  2007;20(Suppl 1):94-100.
The Cancer Bioinformatics Grid (caBIG™) program was created by the National Cancer Institute to facilitate sharing of IT infrastructure, data, and applications among the National Cancer Institute-sponsored cancer research centers. The program was launched in February 2004 and now links more than 50 cancer centers. In April 2005, the In Vivo Imaging Workspace was added to promote the use of imaging in cancer clinical trials. At the inaugural meeting, four special interest groups (SIGs) were established. The Software SIG was charged with identifying projects that focus on open-source software for image visualization and analysis. To date, two projects have been defined by the Software SIG. The eXtensible Imaging Platform project has produced a rapid application development environment that researchers may use to create targeted workflows customized for specific research projects. The Algorithm Validation Tools project will provide a set of tools and data structures that will be used to capture measurement information and associated needed to allow a gold standard to be defined for the given database against which change analysis algorithms can be tested. Through these and future efforts, the caBIG™ In Vivo Imaging Workspace Software SIG endeavors to advance imaging informatics and provide new open-source software tools to advance cancer research.
doi:10.1007/s10278-007-9061-4
PMCID: PMC2039820  PMID: 17846835
Open source, digital imaging and communications in medicine (DICOM); grid computing; image analysis; imaging informatics; caBIG; XIP; AVT
23.  Open Source Software Projects of the caBIG™ In Vivo Imaging Workspace Software Special Interest Group 
Journal of Digital Imaging  2007;20(Suppl 1):94-100.
The Cancer Bioinformatics Grid (caBIG™) program was created by the National Cancer Institute to facilitate sharing of IT infrastructure, data, and applications among the National Cancer Institute-sponsored cancer research centers. The program was launched in February 2004 and now links more than 50 cancer centers. In April 2005, the In Vivo Imaging Workspace was added to promote the use of imaging in cancer clinical trials. At the inaugural meeting, four special interest groups (SIGs) were established. The Software SIG was charged with identifying projects that focus on open-source software for image visualization and analysis. To date, two projects have been defined by the Software SIG. The eXtensible Imaging Platform project has produced a rapid application development environment that researchers may use to create targeted workflows customized for specific research projects. The Algorithm Validation Tools project will provide a set of tools and data structures that will be used to capture measurement information and associated needed to allow a gold standard to be defined for the given database against which change analysis algorithms can be tested. Through these and future efforts, the caBIG™ In Vivo Imaging Workspace Software SIG endeavors to advance imaging informatics and provide new open-source software tools to advance cancer research.
doi:10.1007/s10278-007-9061-4
PMCID: PMC2039820  PMID: 17846835
Open source, digital imaging and communications in medicine (DICOM); grid computing; image analysis; imaging informatics; caBIG; XIP; AVT
24.  Change Detection & Characterization: a New Tool for Imaging Informatics and Cancer Research 
Cancer Informatics  2007;4:1-11.
Modern imaging systems are able to produce a rich and diverse array of information, regarding various facets of anatomy and function. The quantity of information produced by these systems is so bountiful, however, as to have the potential to become a hindrance to clinical assessment. In the context of serial image evaluation, computer-based change detection and characterization is one important mechanism to process the information produced by imaging systems, so as to reduce the quantity of data, direct the attention of the physician to regions of the data which are the most informative for their purposes, and present the data in the form in which it will be the most useful. Change detection and characterization algorithms may serve as a basis for the creation of an objective definition of progression, which will reduce inter and intra-observer variability, and facilitate earlier detection of disease and recurrence, which in turn may lead to improved outcomes. Decreased observer variability combined with increased acuity should make it easier to discover promising therapies. Quantitative measures of the response to these therapies should provide a means to compare the effectiveness of treatments under investigation. Change detection may be applicable to a broad range of cancers, in essentially all anatomical regions. The source of information upon which change detection comparisons may be based is likewise broad. Validation of algorithms for the longitudinal assessment of cancer patients is expected to be challenging, though not insurmountable, as the many facets of the problem mean that validation will likely need to be approached from a variety of vantage points. Change detection and characterization is quickly becoming a very active field of investigation, and it is expected that this burgeoning field will help to facilitate cancer care both in the clinic and research.
PMCID: PMC2666947  PMID: 19390659
25.  Semiautomated Quantitation of Carotid Artery Stenosis in Gadolinium-Bolus Magnetic Resonance Angiography  
Journal of Digital Imaging  2002;15(2):69-77.
Magnetic resonance angiography (MRA) has become the standard method for evaluation of carotid occlusive disease. Fast imaging methods combined with bolus intravenous injection of gadolinium contrast have improved the quality of these images. Nevertheless, the gold standard for evaluation was based on projection arterial angiography. The properties of these images are rather different. Whereas most previous evaluations of MRA have used visual assessment of images, we evaluate an algorithm in which a computer algorithm plays the primary role in defining arterial lumen margins, hence, disease. The accuracy of this semiautomated algorithm is shown to compare favorably with gold-standard arteriography in a series of 50 patients.
doi:10.1007/s10278-002-0006-7
PMCID: PMC3611606  PMID: 12202972

Results 1-25 (47)