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1.  Implementation of a channelized Hotelling observer model to assess image quality of x-ray angiography systems 
Journal of Medical Imaging  2015;2(1):015503.
Evaluation of flat-panel angiography equipment through conventional image quality metrics is limited by the scope of standard spatial-domain image quality metric(s), such as contrast-to-noise ratio and spatial resolution, or by restricted access to appropriate data to calculate Fourier domain measurements, such as modulation transfer function, noise power spectrum, and detective quantum efficiency. Observer models have been shown capable of overcoming these limitations and are able to comprehensively evaluate medical-imaging systems. We present a spatial domain-based channelized Hotelling observer model to calculate the detectability index (DI) of our different sized disks and compare the performance of different imaging conditions and angiography systems. When appropriate, changes in DIs were compared to expectations based on the classical Rose model of signal detection to assess linearity of the model with quantum signal-to-noise ratio (SNR) theory. For these experiments, the estimated uncertainty of the DIs was less than 3%, allowing for precise comparison of imaging systems or conditions. For most experimental variables, DI changes were linear with expectations based on quantum SNR theory. DIs calculated for the smallest objects demonstrated nonlinearity with quantum SNR theory due to system blur. Two angiography systems with different detector element sizes were shown to perform similarly across the majority of the detection tasks.
PMCID: PMC4478895  PMID: 26158086
angiography; image quality; observer model; detection
2.  The influence of mammogram acquisition on the mammographic density and breast cancer association in the mayo mammography health study cohort 
Breast Cancer Research : BCR  2012;14(6):R147.
Mammographic density is a strong risk factor for breast cancer. Image acquisition technique varies across mammograms to limit radiation and produce a clinically useful image. We examined whether acquisition technique parameters at the time of mammography were associated with mammographic density and whether the acquisition parameters confounded the density and breast cancer association.
We examined this question within the Mayo Mammography Health Study (MMHS) cohort, comprised of 19,924 women (51.2% of eligible) seen in the Mayo Clinic mammography screening practice from 2003 to 2006. A case-cohort design, comprising 318 incident breast cancers diagnosed through December 2009 and a random subcohort of 2,259, was used to examine potential confounding of mammogram acquisition technique parameters (x-ray tube voltage peak (kVp), milliampere-seconds (mAs), thickness and compression force) on the density and breast cancer association. The Breast Imaging Reporting and Data System four-category tissue composition measure (BI-RADS) and percent density (PD) (Cumulus program) were estimated from screen-film mammograms at time of enrollment. Spearman correlation coefficients (r) and means (standard deviations) were used to examine the relationship of density measures with acquisition parameters. Hazard ratios (HR) and C-statistics were estimated using Cox proportional hazards regression, adjusting for age, menopausal status, body mass index and postmenopausal hormones. A change in the HR of at least 15% indicated confounding.
Adjusted PD and BI-RADS density were associated with breast cancer (p-trends < 0.001), with a 3 to 4-fold increased risk in the extremely dense vs. fatty BI-RADS categories (HR: 3.0, 95% CI, 1.7 - 5.1) and the ≥ 25% vs. ≤ 5% PD categories (HR: 3.8, 95% CI, 2.5 - 5.9). Of the acquisition parameters, kVp was not correlated with PD (r = 0.04, p = 0.07). Although thickness (r = -0.27, p < 0.001), compression force (r = -0.16, p < 0.001), and mAs (r = -0.06, p = 0.008) were inversely correlated with PD, they did not confound the PD or BI-RADS associations with breast cancer and their inclusion did not improve discriminatory accuracy. Results were similar for associations of dense and non-dense area with breast cancer.
We confirmed a strong association between mammographic density and breast cancer risk that was not confounded by mammogram acquisition technique.
PMCID: PMC3701143  PMID: 23152984
3.  An Automated DICOM Database Capable of Arbitrary Data Mining (Including Radiation Dose Indicators) for Quality Monitoring 
Journal of Digital Imaging  2010;24(2):223-233.
The U.S. National Press has brought to full public discussion concerns regarding the use of medical radiation, specifically x-ray computed tomography (CT), in diagnosis. A need exists for developing methods whereby assurance is given that all diagnostic medical radiation use is properly prescribed, and all patients’ radiation exposure is monitored. The “DICOM Index Tracker©” (DIT) transparently captures desired digital imaging and communications in medicine (DICOM) tags from CT, nuclear imaging equipment, and other DICOM devices across an enterprise. Its initial use is recording, monitoring, and providing automatic alerts to medical professionals of excursions beyond internally determined trigger action levels of radiation. A flexible knowledge base, aware of equipment in use, enables automatic alerts to system administrators of newly identified equipment models or software versions so that DIT can be adapted to the new equipment or software. A dosimetry module accepts mammography breast organ dose, skin air kerma values from XA modalities, exposure indices from computed radiography, etc. upon receipt. The American Association of Physicists in Medicine recommended a methodology for effective dose calculations which are performed with CT units having DICOM structured dose reports. Web interface reporting is provided for accessing the database in real-time. DIT is DICOM-compliant and, thus, is standardized for international comparisons. Automatic alerts currently in use include: email, cell phone text message, and internal pager text messaging. This system extends the utility of DICOM for standardizing the capturing and computing of radiation dose as well as other quality measures.
PMCID: PMC3056966  PMID: 20824303
Data extraction; medical informatics applications; radiation dose; database management systems; knowledge base
4.  Diagnostic Ionizing Radiation Exposure in a Population-Based Cohort of Patients with Inflammatory Bowel Disease 
For diagnosis, assessing disease activity, complications and extraintestinal manifestations, and monitoring response to therapy, patients with inflammatory bowel disease undergo many radiological studies employing ionizing radiation. However, the extent of radiation exposure in these patients is unknown.
A population-based inception cohort of 215 patients with inflammatory bowel disease from Olmsted County, Minnesota, diagnosed between 1990 and 2001, was identified. The total effective dose of diagnostic ionizing radiation was estimated for each patient. Linear regression was used to assess the median total effective dose since symptom onset.
The number of patients with Crohn's disease and ulcerative colitis was 103 and 112, with a mean age at diagnosis of 38.6 and 39.4 yr, respectively. Mean follow-up was 8.9 yr for Crohn's disease and 9.0 yr for ulcerative colitis. Median total effective dose for Crohn's disease was 26.6 millisieverts (mSv) (range, 0–279) versus 10.5 mSv (range, 0–251) for ulcerative colitis (P < 0.001). Computed tomography accounted for 51% and 40% of total effective dose, respectively. Patients with Crohn's disease had 2.46 times higher total effective dose than ulcerative colitis patients (P = 0.001), adjusting for duration of disease.
Annualizing our data, the radiation exposure in the inflammatory bowel disease population was equivalent to the average annual background radiation dose from naturally occurring sources in the U.S. (3.0 mSv). However, a subset of patients had substantially higher doses. The development of imaging management guidelines to minimize radiation dose, dose-reduction techniques in computed tomography, and faster, more robust magnetic resonance techniques are warranted.
PMCID: PMC2831296  PMID: 18564113

Results 1-5 (5)