Bruno Barufaldi

OpenVCT: a GPU-accelerated virtual clinical trial pipeline for mammography and digital breast tomosynthesis

Virtual clinical trials (VCTs) have a critical role in preclinical testing of imaging systems. A VCT pipeline has been developed to model the human body anatomy, image acquisition systems, display and processing, and image analysis and interpretation. VCTs require the execution of multiple computer simulations in a reasonable time. This study presents the OpenVCT Framework, consisting of graphical software to design a sequence of processing steps for the VCT pipeline; management software that coordinates the pipeline execution, manipulates, and retrieves phantoms and images using a relational database; and a server that executes the individual steps of the virtual patient accrual process using GPU optimized software. The framework is modular and supports various data types, algorithms, and modalities. The framework can be used to conduct massive simulations and several hundred imaging studies can be simulated per day on a single workstation. On average, we can simulate a Tomo Combo (DM + DBT) study using anthropomorphic breast phantoms in less than 9 minutes (voxel size = 100 μm3 and volume = 700 mL). Tomo Combo images from an entire virtual population can be simulated in less than a week. We can accelerate system performance using phantoms with large voxels. The VCT pipeline can also be accelerated by using multiple GPU’s (e.g., using SLI mode, GPU clusters).

Validation of no-reference image quality index for the assessment of digital mammographic images

To ensure optimal clinical performance of digital mammography, it is necessary to obtain images with high spatial resolution and low noise, keeping radiation exposure as low as possible. These requirements directly affect the interpretation of radiologists. The quality of a digital image should be assessed using objective measurements. In general, these methods measure the similarity between a degraded image and an ideal image without degradation (ground-truth), used as a reference. These methods are called Full-Reference Image Quality Assessment (FR-IQA). However, for digital mammography, an image without degradation is not available in clinical practice; thus, an objective method to assess the quality of mammograms must be performed without reference. The purpose of this study is to present a Normalized Anisotropic Quality Index (NAQI), based on the Rényi entropy in the pseudo-Wigner domain, to assess mammography images in terms of spatial resolution and noise without any reference. The method was validated using synthetic images acquired through an anthropomorphic breast software phantom, and the clinical exposures on anthropomorphic breast physical phantoms and patient’s mammograms. The results reported by this noreference index follow the same behavior as other well-established full-reference metrics, e.g., the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Reductions of 50% on the radiation dose in phantom images were translated as a decrease of 4dB on the PSNR, 25% on the SSIM and 33% on the NAQI, evidencing that the proposed metric is sensitive to the noise resulted from dose reduction. The clinical results showed that images reduced to 53% and 30% of the standard radiation dose reported reductions of 15% and 25% on the NAQI, respectively. Thus, this index may be used in clinical practice as an image quality indicator to improve the quality assurance programs in mammography; hence, the proposed method reduces the subjectivity inter-observers in the reporting of image quality assessment.

Summary of Outcomes from Consecutive Years of Tomosynthesis Screening at an American Academic Institution

Digital breast tomosynthesis DBT screening outcomes are sustainable over consecutive years with significant reductions in recall and increasing cancers per recalled patients compared to screening with digital mammography alone DM. There is a prevalence effect with a reduction in cancer detection at the second round of screening that is no longer present at the third round. There is a non-statistically significant trend of decreased interval cancers with DBT compared to DM alone screening. Early data on the implementation of synthetic 2D s2D imaging coupled with DBT shows maintenance of screening outcomes with reduction in radiation dose compared to DM/DBT screening .

Feasibility Study of Dose Reduction in Digital Breast Tomosynthesis Using Non-Local Denoising Algorithms

The main purpose of this work is to study the ability of denoising algorithms to reduce the radiation dose in Digital Breast Tomosynthesis (DBT) examinations. Clinical use of DBT is normally performed in “combo-mode”, in which, in addition to DBT projections, a 2D mammogram is taken with the standard radiation dose. As a result, patients have been exposed to radiation doses higher than used in digital mammography. Thus, efforts to reduce the radiation dose in DBT examinations are of great interest. However, a decrease in dose leads to an increased quantum noise level, and related decrease in image quality. This work is aimed at addressing this problem by the use of denoising techniques, which could allow for dose reduction while keeping the image quality acceptable. We have studied two “state of the art” denoising techniques for filtering the quantum noise due to the reduced dose in DBT projections: Non-local Means (NLM) and Block-matching 3D (BM3D). We acquired DBT projections at different dose levels of an anthropomorphic physical breast phantom with inserted simulated microcalcifications. Then, we found the optimal filtering parameters where the denoising algorithms are capable of recovering the quality from the DBT images acquired with the standard radiation dose. Results using objective image quality assessment metrics showed that BM3D algorithm achieved better noise adjustment (mean difference in peak signal to noise ratio < 0.1dB) and less blurring (mean difference in image sharpness ~ 6%) than the NLM for the projections acquired with lower radiation doses.

Text Classification by Literary Period Using PPM-C Data Compression

Methods and techniques for data compression have been used for pattern recognition, including automatic text classification. The performance of the Prediction by Partial Matching (PPM) as a text classifier has already been proofed by many works, including authorship attribution for Portuguese texts. Classes involved in classification process may not be restricted by only one author. By including two or more authors in one class, one can create a literature style. This work presents a literature style classifier for texts from Brazilian literature by using the PPM-C statistical model.

Virtual clinical trial of lesion detection in digital mammography and digital breast tomosynthesis

We have designed and conducted 35 virtual clinical trials (VCTs) of breast lesion detection in digital mammography (DM) and digital breast tomosynthesis (DBT) using a novel open-source simulation pipeline, OpenVCT. The goal of the VCTs is to test in-silico reports that DBT provides substantial improvements in the detectability of masses, while the detectability of microcalcifications remains comparable to DM. For this test, we generated 12 software breast phantoms (volume 700ml, compressed thickness 6.33cm), varying the number of simulated tissue compartments and their shape. Into each phantom, we inserted multiple lesions located 2cm apart in the plane parallel to detector at the level of the nipple. Simulated ellipsoidal masses (oblate spheroids 7mm in diameter and of various thicknesses) and single calcifications of various size and composition were inserted; a total of 17,640 lesions were simulated for this project. DM and DBT projections of phantoms with and without lesions were synthesized assuming a clinical acquisition geometry. Exposure parameters (mAs and kVp) were selected to match AEC settings. Processed DM images and reconstructed DBT slices were obtained using a commercially available software library. Lesion detection was simulated by channelized Hotelling observers, with 15 LG channels and a spread of 22, using independent sets of 480 image samples (150×150 pixel ROIs) for training and 480 samples for testing. Our VCTs showed an average AUC improvement for DBT vs DM of 0.027 for microcalcifications and 0.103 for masses, in close agreement (within 1%) of clinical data reported in the literature.

Interactions of lesion detectability and size across single-slice DBT and 3D DBT.

Three dimensional image modalities introduce a new paradigm for visual search requiring visual exploration of a larger search space than 2D imaging modalities. The large number of slices in the 3D volumes and the limited reading times make it difficult for radiologists to explore thoroughly by fixating with their high resolution fovea on all regions of each slice. Thus, for 3D images, observers must rely much more on their visual periphery (points away from fixation) to process image information. We previously found a dissociation in signal detectability between 2D and 3D search tasks for small signals in synthetic textures evaluated with non-radiologist trained observers. Here, we extend our evaluation to more clinically realistic backgrounds and radiologist observers. We studied the detectability of simulated microcalcifications (MCALC) and masses (MASS) in Digital Breast Tomosynthesis (DBT) utilizing virtual breast phantoms. We compared the lesion detectability of 8 radiologists during free search in 3D DBT and a 2D single-slice DBT (center slice of the 3D DBT). Our results show that the detectability of the microcalcification degrades significantly in 3D DBT with respect to the 2D single-slice DBT. On the other hand, the detectability for masses does not show this behavior and its detectability is not significantly different. The large deterioration of the 3D detectability of microcalcifications relative to masses may be related to the peripheral processing given the high number of cases in which the microcalcification was missed and the high number of search errors. Together, the results extend previous findings with synthetic textures and highlight how search in 3D images is distinct from 2D search as a consequence of the interaction between search strategies and the visibility of signals in the visual periphery.

Optimized simulation of breast anatomy for virtual clinical trials.

Virtual clinical trials (VCTs), computer simulations of clinical trials, can take many forms. In the field of breast imaging, VCTs often involve simulations of breast anatomy, which are used to produce simulated images of the breast with or without lesions. Our breast anatomy model consists of an array of voxels labeled to denote specific tissue types; the voxel labels are arrayed spatially so as to simulation various anatomic structures. Our most recent breast model includes numerous innovations in the anatomy simulation and data representation. The breast model has been revised in size and shape to better reflect the range of women seen clinically; the breast is divided into three breast regions (subcutaneous, interior, and posterior) with different rules to guide tissue arrangement; and tissue microstructure has been added to reflect a hierarchy of Cooper’s ligaments. The lesion simulation has been enhanced to support lesions with various shapes (e.g., spherical lesions with tapered periphery, circumscribed non-spherical lesions, and single or clustered microcalcifications) and lesion placement that follows the clinical prevalence. Finally, the data representation has been formalized to support large VCTs using the VCT pipeline software previously developed in our lab. These innovations have resulted in breast phantoms that are more realistic and more widely applicable.

Developing populations of software breast phantoms for virtual clinical trials.

Virtual Clinical Trials (VCTs) of breast imaging have been used as a tool for the evaluation and optimization of novel imaging systems through computer simulations of breast anatomy, image acquisition, and interpretation. VCTs offer significant advantages over clinical trials in terms of cost, duration, and radiation risk. The performance of VCTs depends on the selection of simulated breasts to represent the population of interest. We have developed a method for selecting populations of software breast phantoms to match the clinical distribution of compressed breast thickness and breast percent density. We extracted the compressed thickness information from anonymized DICOM headers of mammography images from 10,705 women who had their breast screening exams within a year (09/2010-08/2011). Percent density was estimated using an open source software tool. Characteristic clinical sub-populations were identified by performing k-means clustering, and represented by separate sets of phantoms. The corresponding thickness of uncompressed phantoms was selected assuming 50% thickness reduction during mammographic compression. The phantom volumetric density was selected based upon a relationship between mammographic (2D) percent density and volumetric (3D) density, estimated from clinical images. Using a set of 24 representative phantoms, we were able to match the analyzed clinical population completely for the compressed breast thickness, and within two percentage points of the volumetric breast density. Representative phantoms can be used to generate the full population of virtual patients, of a size determined by the power-analysis of the specific VCT, by random variations of the internal phantom composition.

Diagnostic radiation dose after the implementation of digital breast tomosynthesis screening.

Our clinical practice transitioned from digital mammography (DM) to digital breast tomosynthesis (DBT) screening in 2011. This study analyzes the radiation dose of diagnostic exams after recall from screening in two cohorts, before (I) and after (II) the transition to screening with four Hologic Selenia Dimensions DM/DBT systems. We considered four different imaging modes: DM, magnification DM, and the 2D and 3D components of DBT. Diagnostic exams were classified into four groups based on screening recalled finding: asymmetry, architectural distortion, masses, and calcifications. The study set consisted of 7,409 images from 1,857 women (mean age 55.3±10 yrs.) acquired at two time periods (2010-11, cohort I; 2012-13, cohort II). The average glandular dose (AGD) for the population was computed from the sum of all exposures and analyzed by finding type. The AGD, breast thickness, and exposure settings were obtained using an automated dose-reporting software that stores DICOM metadata in a database for real-time data exploration. The average AGD per patient was 5.82 mGy for cohort I (4.95, 5.40, 6.01 and 8.96 mGy for masses, asymmetry, architectural distortion and calcifications, respectively) and 7.15 mGy for cohort II (6.23, 7.19, 7.61 and 9.03 mGy, respectively). While the AGD for calcifications remained the same after the transition, a dose increase of 23% was found due to the addition of 3D imaging for the other findings. The implementation of DBT in the diagnostic setting results in increased radiation dose to the diagnostic population. However, due to the reduction of recalled patients from screening, we achieved a 15.4% reduction in the utilization of diagnostic imaging. This resulted in a net increase of only 4.0% in the total radiation dose to the screening population arising from diagnostic imaging at recall.