Robert S Saunders

Best Care at Lower Cost

Ab-1 SUMMARY S-1 The Imperatives S-3 The Vision S-11 The Path S-13 Actions for Continuous Learning, Best Care, and Lower Costs S-19 Achieving the Vision S-27 References S-29 PART I: THE IMPERATIVES

Optimization of Dual Energy Contrast Enhanced Breast Tomosynthesis for Improved Mammographic Lesion Detection and Diagnosis

Dual-energy contrast-enhanced breast tomosynthesis has been proposed as a technique to improve the detection of early-stage cancer in young, high-risk women. This study focused on optimizing this technique using computer simulations. The computer simulation used analytical calculations to optimize the signal difference to noise ratio (SdNR) of resulting images from such a technique at constant dose. The optimization included the optimal radiographic technique, optimal distribution of dose between the two single-energy projection images, and the optimal weighting factor for the dual energy subtraction. Importantly, the SdNR included both anatomical and quantum noise sources, as dual energy imaging reduces anatomical noise at the expense of increases in quantum noise. Assuming a tungsten anode, the maximum SdNR at constant dose was achieved for a high energy beam at 49 kVp with 92.5 μm copper filtration and a low energy beam at 49 kVp with 95 μm tin filtration. These analytical calculations were followed by Monte Carlo simulations that included the effects of scattered radiation and detector properties. Finally, the feasibility of this technique was tested in a small animal imaging experiment using a novel iodinated liposomal contrast agent. The results illustrated the utility of dual energy imaging and determined the optimal acquisition parameters for this technique. This work was supported in part by grants from the Komen Foundation (PDF55806), the Cancer Research and Prevention Foundation, and the NIH (NCI R21 CA124584-01). CIVM is a NCRR/NCI National Resource under P41-05959/U24-CA092656.

Characterization of breast masses for simulation purposes

Simulation of radiographic lesions is an important prerequisite for several research applications in medical imaging, including hardware and software design and optimization. For mammography, breast masses are an important class of lesions to be considered. In this study, we first characterized both benign and malignant breast masses with example mammograms from the Digital Database for Screening Mammography (DDSM). The measured properties of each of these mass types were then used to create a simulation routine that was capable of creating example masses from each category. A preliminary observer experiment was conducted to determine whether a mammographer could distinguish between the simulated and true masses. An ROC analysis indicated Az values of 0.59 and 0.61 for benign and malignant lesions, respectively, suggesting very similar appearance for the simulated and real lesions. A larger observer performance experiment with multiple mammographers is underway to validate these results.

Potential for lower absorbed dose in digital mammography: a JAFROC experiment using clinical hybrid images with simulated dose reduction

Purpose: To determine how image quality linked to tumor detection is affected by reducing the absorbed dose to 50% and 30% of the clinical levels represented by an average glandular dose (AGD) level of 1.3 mGy for a standard breast according to European guidelines. Materials and methods: 90 normal, unprocessed images were acquired from the screening department using a full-field digital mammography (FFDM) unit Mammomat Novation (Siemens). Into 40 of these, one to three simulated tumors were inserted per image at various positions. These tumors represented irregular-shaped malignant masses. Dose reduction was simulated in all 90 images by adding simulated quantum noise to represent images acquired at 50% and 30% of the original dose, resulting in 270 images, which were subsequently processed for final display. Four radiologists participated in a free-response receiver operating characteristics (FROC) study in which they searched for and marked suspicious positions of the masses as well as rated their degree of suspicion of occurrence on a one to four scale. Using the jackknife FROC (JAFROC) method, a score between 0 and 1 (where 1 represents best performance), referred to as a figure-of-merit (FOM), was calculated for each dose level. Results: The FOM was 0.73, 0.70, and 0.68 for the 100%, 50% and 30% dose levels, respectively. Using Analysis of the Variance (ANOVA) to test for statistically significant differences between any two of the three FOMs revealed that they were not statistically distinguishable (p-value of 0.26). Conclusion: For the masses used in this experiment, there was no significant change in detection by increasing quantum noise, thus indicating a potential for dose reduction.

Initial human subject results for breast bi-plane correlation imaging technique

Computer aided detection (CADe) systems often present multiple false-positives per image in projection mammography due to overlapping anatomy. To reduce the number of such false-positives, we propose performing CADe on image pairs acquired using a bi-plane correlation imaging (BCI) technique. In this technique, images are acquired of each breast at two different projection angles. A traditional CADe algorithm operates on each image to identify suspected lesions. The suspicious areas from both projections are then geometrically correlated, eliminating any lesion that is not identified on both views. Proof of concept studies showed that that the BCI technique reduced the numbers of false-positives per case up to 70%.

The Healthcare Imperative

This course is designed to support achieving and living our values. Diversity & Inclusion and Cultural Competency education allows Duke University Health System (DUHS) team members to identify and recognize how the umbrella of diversity is a healthcare imperative to embrace and exhibit how we ‘care for our patients, their loved ones, and each other.’ Target Audience: This course is designed for all DUHS staff members who are in non-supervisory or non-managerial roles (those who are not supervising or managing other staff).

Computer-aided detection of breast masses in tomosynthesis reconstructed volumes using information-theoretic similarity measures

The purpose of this project is to study two Computer Aided Detection (CADe) systems for breast masses for digital tomosynthesis using reconstructed slices. This study used eighty human subject cases collected as part of on-going clinical trials at Duke University. Raw projections images were used to identify suspicious regions in the algorithms high sensitivity, low specificity stage using a Difference of Gaussian filter. The filtered images were thresholded to yield initial CADe hits that were then shifted and added to yield a 3D distribution of suspicious regions. The initial system performance was 95% sensitivity at 10 false positives per breast volume. Two CADe systems were developed. In system A, the central slice located at the centroid depth was used to extract a 256 X 256 Regions of Interest (ROI) database centered at the lesion coordinates. For system B, 5 slices centered at the lesion coordinates were summed before the extraction of 256 × 256 ROIs. To avoid issues associated with feature extraction, selection, and merging, information theory principles were used to reduce false positives for both the systems resulting in a classifier performance of 0.81 and 0.865 Area Under Curve (AUC) with leave-one-case-out sampling. This resulted in an overall system performance of 87% sensitivity with 6.1 FPs/ volume and 85% sensitivity with 3.8 FPs/ volume for systems A and B respectively. This system therefore has the potential to detect breast masses in tomosynthesis data sets.

Analyzing the effect of dose reduction on the detection of mammographic lesions using mathematical observer models

The purpose of this study was to determine the effect of dose reduction on the detectability of breast lesions in mammograms. Mammograms with dose levels corresponding to 50% and 25% of the original clinically-relevant exposure levels were simulated. Detection of masses and microcalicifications embedded in these mammograms was analyzed by four mathematical observer models, namely, the Hotelling Observer, Non-prewhitening Matched Filter with Eye Filter (NPWE), and Laguerre-Gauss and Gabor Channelized Hotelling Observers. Performance was measured in terms of ROC curves and Area under ROC Curves (AUC) under Signal Known Exactly but Variable Tasks (SKEV) paradigm. Gabor Channelized Hotelling Observer predicted deterioration in detectability of benign masses. The other algorithmic observers, however, did not indicate statistically significant differences in the detectability of masses and microcalcifications with reduction in dose. Detection of microcalcifications was affected more than the detection of masses. Overall, the results indicate that there is a potential for reduction of radiation dose level in mammographic screening procedures without severely compromising the detectability of lesions.

Effect of display resolution on the detection of mammographic lesions

For diagnosis of breast cancer by mammography, the mammograms must be viewed by a radiologist. The purpose of this study was to determine the effect of display resolution on the specific clinical task of detection of breast lesions by a human observer. Using simulation techniques, this study proceeded through four stages. First, we inserted simulated masses and calcifications into raw digital mammograms. The resulting images were processed according to standard image processing techniques and appropriately windowed and leveled. The processed images were blurred according to MTFs measured from a clinical Cathode Ray Tube display. JNDMetrix, a Visual Discrimination Model, examined the images to estimate human detection. The model results suggested that detection of masses and calcifications decreased under standard CRT resolution. Future work will confirm these results with human observer studies. (This work was supported by grants NIH R21-CA95308 and USAMRMC W81XWH-04-1-0323.)

A Monte Carlo investigation on the impact of scattered radiation on mammographic resolution and noise

Scattered radiation plays a significant role in mammographic imaging, with scatter fractions over 50% for larger, denser breasts. For screen-film systems, scatter primarily affects the image contrast, reducing the conspicuity of subtle lesions. While digital systems can overcome contrast degradation, they remain susceptible to scatters impact on the image resolution and noise. To better understand this impact, we have created a Monte Carlo model of a mammographic imaging system adaptable for different imaging situations. This model flags primary and scatter photons and therefore can produce primary-only, scatter-only, or primary plus scatter images. Resolution was assessed using the edge technique to compute the Modulation Transfer Function (MTF). The MTF of a selenium detector imaged with a 28 kVp Mo/Mo beam filtered through a 6 cm heterogeneous breast was 0.81, 0.0002, and 0.65 at 5 mm-1 for the primary beam, scatter-only, and primary plus scatter beam, respectively. Noise was measured from flat-field images via the noise power spectrum (NNPS). The NNPS-exposure product using the same imaging conditions was 1.5 x 10-5 mm2x mR, 1.6 x 10-5 mm2x mR, and 1.9 x 10-5 mm2x mR at 5 mm-1 for the primary, scatter, and primary plus scatter beam, respectively. The results show that scatter led to a notable low-frequency drop in the MTF and an increased magnitude of the NNPS-exposure product. (This work was supported in part by USAMRMC W81XWH-04-1-0323.)