J. Michael O'Connor

Characterization of scatter in cone‐beam CT breast imaging: Comparison of experimental measurements and Monte Carlo simulation

It is commonly understood that scattered radiation in x-ray computed tomography (CT) degrades the reconstructed image. As a precursor to developing scatter compensation methods, it is important to characterize this scatter using both empirical measurements and Monte Carlo simulations. Previous studies characterizing scatter using both experimental measurements and Monte Carlo simulations have been reported in diagnostic radiology and conventional mammography. The emerging technology of cone-beam CT breast imaging (CTBI) differs significantly from conventional mammography in the breast shape and imaging geometry, aspects that are important factors impacting the measured scatter. This study used a bench-top cone-beam CTBI system with an indirect flat-panel detector. A cylindrical phantom with equivalent composition of 50% fibroglandular and 50% adipose tissues was used, and scatter distributions were measured by beam stop and aperture methods. The GEANT4-based simulation package GATE was used to model x-ray photon interactions in the phantom and detector. Scatter to primary ratio (SPR) measurements using both the beam stop and aperture methods were consistent within 5% after subtraction of nonbreast scatter contributions and agree with the low energy electromagnetic model simulation in GATE. The validated simulation model was used to characterize the SPR in different CTBI conditions. In addition, a realistic, digital breast phantom was simulated to determine the characteristics of various scatter components that cannot be separated in measurements. The simulation showed that the scatter distribution from multiple Compton and Rayleigh scatterings, as well as from the single Compton scattering, has predominantly low-frequency characteristics. The single Rayleigh scatter was observed to be the primary contribution to the spatially variant scatter component.

Evaluation of a variable dose acquisition technique for microcalcification and mass detection in digital breast tomosynthesis

In this article the authors evaluate a recently proposed variable dose (VD)-digital breast tomosynthesis (DBT) acquisition technique in terms of the detection accuracy for breast masses and microcalcification (MC) clusters. With this technique, approximately half of the total dose is used for one center projection and the remaining dose is split among the other tomosynthesis projection views. This acquisition method would yield both a projection view and a reconstruction view. One of the aims of this study was to evaluate whether the center projection alone of the VD acquisition can provide equal or superior MC detection in comparison to the 3D images from uniform dose (UD)-DBT. Another aim was to compare the mass-detection capabilities of 3D reconstructions from VD-DBT and UD-DBT. In a localization receiver operating characteristic (LROC) observer study of MC detection, the authors compared the center projection of a VD acquisitioh scheme (at 2 mGy dose) with detector pixel size of 100 microm with the UD-DBT reconstruction (at 4 mGy dose) obtained with a voxel size of 100 microm. MCs with sizes of 150 and 180 microm were used in the study, with each cluster consisting of seven MCs distributed randomly within a small volume. Reconstructed images in UD-DBT were obtained from a projection set that had a total of 4 mGy dose. The current study shows that for MC detection, using the center projection alone of VD acquisition scheme performs worse with area under the LROC curve (AL) of 0.76 than when using the 3D reconstructed image using the UD acquisition scheme (AL=0.84). A 2D ANOVA found a statistically significant difference (p=0.038) at a significance level of 0.05. In the current study, although a reconstructed image was also available using the VD acquisition scheme, it was not used to assist the MC detection task which was done using the center projection alone. In the case of evaluation of detection accuracy of masses, the reconstruction with VD-DBT (AL=0.71) was compared to that obtained from the UD-DBT (AL=0.78). The authors found no statistically significant difference between the two (p-value=0.22), although all the observers performed better for UD-DBT.

Generation of voxelized breast phantoms from surgical mastectomy specimens

PURPOSE In the research and development of dedicated tomographic breast imaging systems, digital breast object models, also known as digital phantoms, are useful tools. While various digital breast phantoms do exist, the purpose of this study was to develop a realistic high-resolution model suitable for simulating three-dimensional (3D) breast imaging modalities. The primary goal was to design a model capable of producing simulations with realistic breast tissue structure. METHODS The methodology for generating an ensemble of digital breast phantoms was based on imaging surgical mastectomy specimens using a benchtop, cone-beam computed tomography system. This approach allowed low-noise, high-resolution projection views of the mastectomy specimens at each angular position. Reconstructions of these projection sets were processed using correction techniques and diffusion filtering prior to segmentation into breast tissue types in order to generate phantoms. RESULTS Eight compressed digital phantoms and 20 uncompressed phantoms from which an additional 96 pseudocompressed digital phantoms with voxel dimensions of 0.2 mm(3) were generated. Two distinct tissue classification models were used in forming breast phantoms. The binary model classified each tissue voxel as either adipose or fibroglandular. A multivalue scaled model classified each tissue voxel as percentage of adipose tissue (range 1%-99%). Power spectral analysis was performed to compare simulated reconstructions using the breast phantoms to the original breast specimen reconstruction, and fits were observed to be similar. CONCLUSIONS The digital breast phantoms developed herein provide a high-resolution anthropomorphic model of the 3D uncompressed and compressed breast that are suitable for use in evaluating and optimizing tomographic breast imaging modalities. The authors believe that other research groups might find the phantoms useful, and therefore they offer to make them available for wider use.

Using Mastectomy Specimens to Develop Breast Models for Breast Tomosynthesis and CT Breast Imaging

Dedicated x-ray computed tomography (CT) of the breast using a cone-beam flat-panel detector system is a modality under investigation by a number of research teams. As previously reported, we have fabricated a prototype, bench-top flat-panel CT breast imaging (CTBI) system and developed computer simulation software to model such a system. We are developing a methodology to use high resolution, low noise CT reconstructions of fresh mastectomy specimens for generating an ensemble of 3D digital breast phantoms that realistically model 3D compressed and uncompressed breast anatomy. These breast models can be used to simulate realistic projection data for both breast tomosynthesis (BT) and CT systems thereby providing a powerful evaluation and optimization mechanism.

Comparison of Two Methods to Develop Breast Models for Simulation of Breast Tomosynthesis and CT

Dedicated x-ray computed tomography (CT) of the breast using a cone-beam flat-panel detector system is a modality under investigation by a number of research teams. Several teams, including ours, have fabricated prototype, bench-top flat-panel CT breast imaging (CTBI) systems. We also use computer simulation software to optimize system parameters. We are developing a methodology to use high resolution, low noise CT reconstructions of fresh mastectomy specimens in order to generate an ensemble of 3D digital breast phantoms that realistically model 3D compressed and uncompressed breast anatomy. The resulting breast models can then be used to simulate realistic projection data for both Breast Tomosynthesis (BT) and Breast CT (BCT) systems thereby providing a powerful evaluation and optimization mechanism for research and development of novel breast imaging systems as well as the optimization of imaging techniques for such systems. Having the capability of using breast object models and simulation software is clinically significant because prior to a clinical trial of any prototype breast imaging system many parameter tradeoffs can be investigated in a simulation environment. This capability is worthwhile not only for the obvious benefit of improving patient safety during initial clinical trials but also because simulation prior to prototype implementation should result in reduced cost and increased speed of development. The main goal of this study is to compare results obtained using two different methods to develop breast object models in order to select the better technique for developing the entire ensemble.

Development of an ensemble of digital breast object models

In the investigation of emerging tomographic breast imaging methods using flat-panel detectors (FPDs), digital breast object models are useful tools These models are also commonly referred to as digital phantoms We have created an ensemble of digital breast object phantoms based on CT scans of surgical mastectomy specimens Early versions of the phantoms have been used in our published research Recently we have improved some of our processing tools such as the use of 3-D anisotropic diffusion filtering (ADF) prior to segmentation, and we have evaluated breast object models generated with different methods including power spectral analysis, ROI statistics and an observation study.

Characterization of a prototype tabletop x-ray CT breast imaging system

Planar X-ray mammography is the standard medical imaging modality for the early detection of breast cancer. Based on advancements in digital flat-panel detector technology, dedicated x-ray computed tomography (CT) mammography is a modality under investigation that offers the potential for improved breast tumor imaging. We have implemented a prototype half cone-beam CT breast imaging system that utilizes an indirect flat-panel detector. This prototype can be used to explore and evaluate the effect of varying acquisition and reconstruction parameters on image quality. This report describes our system and characterizes the performance of the system through the analysis of Modulation Transfer Function (MTF) and Noise Power Spectrum (NPS). All CT reconstructions were made using Feldkamps filtered backprojection algorithm. The 3D MTF was determined by the analysis of the plane spread function (PlSF) derived from the surface spread function (SSF) of reconstructed 6.3mm spheres. 3D NPS characterization was performed through the analysis of a 3D volume extracted from zero-mean CT noise of air reconstructions. The effect of varying locations on MTF and the effect of different Butterworth filter cutoff frequencies on NPS are reported. Finally, we present CT images of mastectomy excised breast tissue. Breast specimen images were acquired on our CTMS using an x-ray technique similar to the one used during performance characterization. Specimen images demonstrate the inherent CT capability to reduce the masking effect of anatomical noise. Both the quantitative system characterization and the breast specimen images continue to reinforce the hope that dedicated flat-panel detector, x-ray cone-beam CT will eventually provide enhanced breast cancer detection capability.

Temporal change analysis for improved tumor detection in dedicated CT breast imaging using affine and free-form deformation

Preliminary evidence has suggested that computerized tomographic (CT) imaging of the breast using a cone-beam, flat-panel detector system dedicated solely to breast imaging has potential for improving detection and diagnosis of early-stage breast cancer. Hypothetically, a powerful mechanism for assisting in early stage breast cancer detection from annual screening breast CT studies would be to examine temporal changes in the breast from year-to-year. We hypothesize that 3D image registration could be used to automatically register breast CT volumes scanned at different times (e.g., yearly screening exams). This would allow radiologists to quickly visualize small changes in the breast that have developed during the period since the last screening CT scan, and use this information to improve the diagnostic accuracy of early-stage breast cancer detection. To test our hypothesis, fresh mastectomy specimens were imaged with a flat-panel CT system at different time points, after moving the specimen to emulate the re-positioning motion of the breast between yearly screening exams. Synthetic tumors were then digitally inserted into the second CT scan at a clinically realistic location (to emulate tumor growth from year-to-year). An affine and a spline-based 3D image registration algorithm was implemented and applied to the CT reconstructions of the specimens acquired at different times. Subtraction of registered image volumes was then performed to better analyze temporal change. Results from this study suggests that temporal change analysis in 3D breast CT can potentially be a powerful tool in improving the visualization of small lesion growth.

A Naive-Bayes model observer for a human observer in detection, localization and assessment of perfusion defects in SPECT

In medical imaging, it is widely accepted that image quality should be assessed through the performance of human observers at some diagnostic task. For these evaluations, mathematical algorithms known as model observers (MOs) are often used as a substitute for human observers in early stages of image reconstruction algorithm development. For SPECT-MPI (myocardial perfusion imaging), diagnostic tasks involve detection, localization and assessment of regions with abnormal myocardial perfusion. In this work we propose a new MO for these tasks. The proposed MO is based on a machine-learning algorithm known as Naive-Bayes classifier (NB-MO). In the proposed approach, NB-MO is applied over a set of image features extracted from SPECT polar maps, aiming to predict perfusion scores given by humans for each myocardium region (segment). Next we compute average MO performance by first directly predicting individual human observer scores, and then using multi reader alternative free-response analysis (AFROC) in the same fashion as used on human observers. Our human observer study was comprised of five experienced (physicians) readers who scored location and severity of perfusion defects in 179 simulated SPECT-MPI cases and two different reconstruction methods (FBP and OSEM). The presented results show good performance agreement between humans and the proposed NB-MO, as well as excellent generalization properties of NB-MO between different reconstruction methods.

Improved microcalcification detection for breast tomosynthesis using a penalized-maximum-likelihood reconstruction method

In this paper we explore the use of a penalized maximum likelihood (PML) based reconstruction method to improve the image quality and microcalcification detectability in digital breast tomosythesis (DBT) To evaluate performance, a human observer psychophysical study was performed with computer simulated images The simulation used realistic structured breast models derived from CT scans of surgical mastectomy specimens giving sufficient statistical variability in terms of breast background structural noise Sensitivity and specificity of microcalcification detectability measured with PML reconstruction was compared to that obtained with the filtered back projection (FBP) method for simulated breast tomosynthesis images An observer study conducted using localized receiver operating characteristic (LROC) analysis showed significantly better sensitivity and specificity using the PML reconstruction method for simulated mean glandular dose levels of 1.0 mGy for a 5 cm compressed breast This study suggests that MC detection accuracy is improved using PML reconstruction technique and that it might be feasible to reduce the imaging dose of DBT using this technique.