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.
We are reporting the optimized acquisition scheme of multi-projection breast Correlation Imaging (CI)
technique, which was pioneered in our lab at Duke University. CI is similar to tomosynthesis in its image
acquisition scheme. However, instead of analyzing the reconstructed images, the projection images are directly
analyzed for pathology. Earlier, we presented an optimized data acquisition scheme for CI using mathematical
observer model. In this article, we are presenting a Computer Aided Detection (CADe)-based optimization
methodology. Towards that end, images from 106 subjects recruited for an ongoing clinical trial for
tomosynthesis were employed. For each patient, 25 angular projections of each breast were acquired. Projection
images were supplemented with a simulated 3 mm 3D lesion. Each projection was first processed by a
traditional CADe algorithm at high sensitivity, followed by a reduction of false positives by combining
geometrical correlation information available from the multiple images. Performance of the CI system was
determined in terms of free-response receiver operating characteristics (FROC) curves and the area under ROC
curves. For optimization, the components of acquisition such as the number of projections, and their angular
span were systematically changed to investigate which one of the many possible combinations maximized the
sensitivity and specificity. Results indicated that the performance of the CI system may be maximized with 7-11
projections spanning an angular arc of 44.8°, confirming our earlier findings using observer models. These
results indicate that an optimized CI system may potentially be an important diagnostic tool for improved breast
cancer detection.
KEYWORDS: Breast, Tissues, 3D image reconstruction, Mammography, X-rays, 3D image processing, Digital imaging, Image processing, X-ray imaging, Imaging systems
Due to the high prevalence of breast cancer among women, much is being done to detect breast cancer
earlier and more accurately. In current clinical practice, the most widely-used mode of breast imaging is
mammography. Its main advantages are high sensitivity and low patient dose, although it is still merely a two-dimensional
projection of a three-dimensional object. In digital breast tomosynthesis, a three-dimensional image of
the breast can be reconstructed, but x-ray projection images of the breast are taken over a limited angular span.
However, the breast tomosynthesis device itself is more similar to a digital mammography system and thus is a
feasible replacement for mammography. Because of the angular undersampling in breast tomosynthesis, the
reconstructed images are not considered quantitative, so a worthwhile question to answer would be whether the
voxel values (VVs) in breast tomosynthesis images can be made to indicate tissue type as Hounsfield units do in CT.
through some image processing scheme. To investigate this, simple phantoms were imaged consisting of layers of
uniform, tissue-equivalent plastic for the background sandwiching a layer of interest containing multiple, small
cuboids of tissue-equivalent plastic. After analyzing the reconstructed tomosynthesis images, it was found that the
VV in each lesion increases linearly with tissue glandularity. However, for the two different x-ray tube energies and
for the two different beam exposure levels tested, the trend-lines all have different slopes and y-intercepts. Thus,
breast tomosynthesis has a definite potential to be quantitative, and it would be worthwhile to study other possible
dependent parameters (phantom thickness, overall density, etc.) as well as alternative reconstruction algorithms.
Imaging tumor angiogenesis in small animals is extremely challenging due to the size of the tumor vessels.
Consequently, both dedicated small animal imaging systems and specialized intravascular contrast agents are
required. The goal of this study was to investigate the use of a liposomal contrast agent for high-resolution micro-CT
imaging of breast tumors in small animals. A liposomal blood pool agent encapsulating iodine with a concentration
of 65.5 mg/ml was used with a Duke Center for In Vivo Microscopy (CIVM) prototype micro-computed
tomography (micro-CT) system to image the R3230AC mammary carcinoma implanted in rats. The animals were
injected with equivalent volume doses (0.02 ml/kg) of contrast agent. Micro-CT with the liposomal blood pool
contrast agent ensured a signal difference between the blood and the muscle higher than 450 HU allowing the
visualization of the tumors 3D vascular architecture in exquisite detail at 100-micron resolution. The micro-CT data
correlated well with the histological examination of tumor tissue. We also studied the ability to detect vascular
enhancement with limited angle based reconstruction, i.e. tomosynthesis. Tumor volumes and their regional vascular
percentage were estimated. This imaging approach could be used to better understand tumor angiogenesis and be the
basis for evaluating anti-angiogenic therapies.
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 algorithm's 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.
We studied the influence of signal variability on human and model observer performances for a detection task with
mammographic backgrounds and computer generated clustered lumpy backgrounds (CLB). We used synthetic yet
realistic masses and backgrounds that have been validated by radiologists during previous studies, ensuring conditions
close to the clinical situation. Four trained non-physician observers participated in two-alternative forced-choice (2-AFC)
experiments. They were asked to detect synthetic masses superimposed on real mammographic backgrounds or CLB.
Separate experiments were conducted with sets of benign and malignant masses. Results under the signal-known-exactly
(SKE) paradigm were compared with signal-known-statistically (SKS) experiments. In the latter case, the signal was
chosen randomly for each of the 1,400 2-AFC trials (image pairs) among a set of 50 masses with similar dimensions, and
the observers did not know which signal was present. Human observers' results were then compared with model
observers (channelized Hotelling with Difference-of-Gaussian and Gabor channels) in the same experimental conditions.
Results show that the performance of the human observers does not differ significantly when benign masses are
superimposed on real images or on CLB with locally matched gray level mean and standard deviation. For both benign
and malignant masses, the performance does not differ significantly between SKE and SKS experiments, when the
signals' dimensions do not vary throughout the experiment. However, there is a performance drop when the SKS signals'
dimensions vary from 5.5 to 9.5 mm in the same experiment. Noise level in the model observers can be adjusted to
reproduce human observers' proportion of correct answers in the 2-AFC task within 5% accuracy for most conditions.
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 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.
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.
KEYWORDS: Photons, Modulation transfer functions, Breast, Sensors, Monte Carlo methods, Systems modeling, Imaging systems, Image resolution, Radiation effects, Signal to noise ratio
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 scatter's 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.)
KEYWORDS: Sensors, Modulation transfer functions, Digital mammography, Prototyping, Molybdenum, Tungsten, Aluminum, Calibration, Quantum efficiency, Signal to noise ratio
This study evaluated the physical performance of a selenium-based direct full-field digital mammography prototype detector (Siemens Mammomat NovationDR), including the pixel value vs. exposure linearity, the modulation transfer function (MTF), the normalized noise power spectrum (NNPS), and the detective quantum efficiency (DQE). The current detector is the same model which received an approvable letter from FDA for release to the US market. The results of the current prototype are compared to those of an earlier prototype. Two IEC standard beam qualities (RQA-M2: Mo/Mo, 28 kVp, 2 mm Al; RQA-M4: Mo/Mo, 35 kVp, 2 mm Al) and two additional beam qualities (MW2: W/Rh, 28 kVp, 2 mm Al; MW4: W/Rh, 35 kVp, 2 mm Al) were investigated. To calculate the modulation transfer function (MTF), a 0.1 mm Pt-Ir edge was imaged at each beam quality. Detector pixel values responded linearly against exposure values (R2 0.999). As before, above 6 cycles/mm Mo/Mo MTF was slightly higher along the chest-nipple axis compared to the left-right axis. MTF was comparable to the previously reported prototype, with slightly reduced resolution. The DQE peaks ranged from 0.71 for 3.31 μC/kg (12.83 mR) to 0.4 for 0.48 μC/kg (1.86 mR) at 1.75 cycles/mm for Mo/Mo at 28 kVp. The DQE range for W/Rh at 28 kVP was 0.81 at 2.03 μC/kg (7.87 mR) to 0.50 at 0.50 μC/kg (1.94 mR) at 1 cycle/mm. NNPS tended to increase with greater exposures, while all exposures had a significant low-frequency component. Bloom and detector edge artifacts observed previously were no longer present in this prototype. The new detector shows marked noise improvement, with slightly reduced resolution. There remain artifacts due to imperfect gain calibration, but at a reduced magnitude compared to a prototype detector.
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.)
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.
The current system performance metrics for Digital Radiographic detectors describe physical parameters, such as resolution (Modulation Transfer Function), noise (Noise Power Spectrum) and efficiency (Detective Quantum Efficiency). However, little has been done to substantiate the impact of these quantitative image quality metrics on a detector's utility for specific clinical tasks.
In order to simulate the effects of these physical parameters, image modification routines were developed capable of modifying a perfect input image to the resolution and noise characteristics specified by an input MTF and input NPS and included sampling effects such as aliasing. Experimental verification of these routines showed excellent correspondence between the resolution and noise properties of the output images and the input NPS and MTF curves.
In order to investigate the effect of noise and resolution on signal detection tasks, high-quality images containing simulated lesions are altered by the image modification routines to the resolution and noise properties of two commercial digital radiographic detectors, one direct and one indirect. The sets of modified images had noise properties consistent with acquisitions at comparable, clinically relevant exposures for the two detectors. An observer study is performed with the resultant images followed by a Receiver Operating Characteristic (ROC) analysis. The results revealed the direct detector had a higher area under the ROC curve with a statistically significant difference for a 2.75 mm nodule (Az = 0.90 vs. 0.76, p<0.01). The findings illustrated the connection between the physical performance metrics and utility for the signal detection tasks necessary for clinical use.
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