Alaleh Rashidnasab

Simulation and assessment of realistic breast lesions using fractal growth models

A new method of generating realistic three dimensional simulated breast lesions known as diffusion limited aggregation (DLA) is presented, and compared with the random walk (RW) method. Both methods of lesion simulation utilize a physics-based method for inserting these simulated lesions into 2D clinical mammogram images that takes into account the polychromatic x-ray spectrum, local glandularity and scatter. DLA and RW masses were assessed for realism via a receiver operating characteristic (ROC) study with nine observers. The study comprised 150 images of which 50 were real pathology proven mammograms, 50 were normal mammograms with RW inserted masses and 50 were normal mammograms with DLA inserted masses. The average area under the ROC curve for the DLA method was 0.55 (95% confidence interval 0.51-0.59) compared to 0.60 (95% confidence interval 0.56-0.63) for the RW method. The observer study results suggest that the DLA method produced more realistic masses with more variability in shape compared to the RW method. DLA generated lesions can overcome the lack of complexity in structure and shape in many current methods of mass simulation.

Performance comparison of breast imaging modalities using a 4AFC human observer study

This work compares the visibility of spheres and simulated masses in 2D-mammography and tomosynthesis systems using human observer studies. Performing comparison studies between breast imaging systems poses a number of practical challenges within a clinical environment. We therefore adopted a simulation approach which included synthetic breast blocks, a validated lesion simulation model and a set of validated image modelling tools as a viable alternative to clinical trials. A series of 4-alternative forced choice (4AFC) human observer experiments has been conducted for signal detection tasks using masses and spheres as targets. Five physicists participated in the study viewing images with a 5mm target at a range of contrast levels and 60 trials per experimental condition. The results showed that tomosynthesis has a lower threshold contrast than 2D-mammography for masses and spheres, and that detection studies using spheres may produce overly-optimistic threshold contrast values.

Realistic simulation of breast mass appearance using random walk

The aim of the present work was to develop a method for simulating breast lesions in digital mammographic images. Based on the visual appearance of real masses, three dimensional masses were created using a 3D random walk method where the choice of parameters (number of walks and number of steps) enables one to control the appearance of the simulated structure. This work is the first occasion that the random walk results have been combined with a model of digital mammographic imaging systems. This model takes into account appropriate physical image acquisition processes representing a particular digital X-ray mammography system. The X-ray spectrum, local glandularity above the insertion site and scatter were all taken account during the insertion procedure. A preliminary observer study was used to validate the realism of the masses. Seven expert readers each viewed 60 full field mammograms and rated the realism of the masses they contained. Half of the images contained real, histologically-confirmed masses, and half contained simulated lesions. The ROC analysis of the study (average AUC of 0.58±0.06) suggests that, on the average, there is evidence that the radiologists could distinguish, somewhat, between real and simulated masses.

Simulation of 3D DLA masses in digital breast tomosynthesis

Digital breast tomosynthesis (DBT) is suggested to have superior performance compared to 2D mammography in terms of cancer visibility, especially in the case of dense breasts. However, the overall performance of tomosynthesis for screening applications, and the manner in which tomosynthesis should be optimally used for screening remains unclear. This motivates the development of software tools that can insert user-defined synthetic pathology of realistic appearance into clinical tomosynthesis images for subsequent use in virtual clinical trials. We present a method for inserting lesions grown using Diffusion Limited Aggregation, previously validated in 2D mammograms, into clinical DBT images. A preliminary pilot study was used to validate the realism of the masses, wherein three readers each viewed 19 cases and rated the realism of the inserted masses. Each case included a simulated mass inserted in the tomosynthesis projections and the counterpart digital 2D mammogram. These results show that masses can be successfully embedded in the tomosynthesis projections and can produce visually authentic DBT images containing synthetic pathology. These results will be used to further optimize the appearance of these masses in DBT for an upcoming validation.

A modelling framework for evaluation of 2d-mammography and breast tomosynthesis systems

Planar 2D X-ray mammography is the most common screening technique used for breast cancer detection. Digital breast tomosynthesis (DBT) is a new and emerging technology that overcomes some of the limitations of conventional planar imaging. However, it is important to understand the impact of these two modalities on cancer detection rates and patient recall. Since it is difficult to adequately evaluate different modalities clinically, a collection of modeling tools is introduced in this paper that can be used to emulate the image acquisition process for both modalities. In this paper, we discuss image simulation chains that can be used for the evaluation of 2D-mammography and DBT systems in terms of both technical factors and observer studies.

Virtual clinical trials using inserted pathology in clinical images: investigation of assumptions for local glandularity and noise

Virtual clinical trials have been proposed as a viable alternative to clinical trials for testing and comparing the performance of breast imaging systems. One of the main simulation methodologies used in virtual trials employs clinical images of patients in which simulated models of cancer are inserted using a physics-based template multiplication technique. The purpose of this work is to investigate two assumptions commonly considered in this simulation approach: Firstly, given the absence of useful depth information in a clinical situation, an average measure of the local breast glandularity is commonly used as an estimate of the breast composition at the insertion site; secondly, it is also assumed that any change in the relative noise in the image at the insertion site, after insertion of a mass, is negligible. In order to test the validity of these assumptions, spheres representing idealised masses and anthropomorphic computational breast phantoms with perfect prior knowledge of local tissue composition and distribution were used. Results from several region of interest (ROI) insertions demonstrated a lack of variation obtained in contrast with insertion depth using the template multiplication insertion method as compared to the true depth-wise variation contrast values obtained from voxel replacement in a heterogeneous phantom. It was also found that the amount of noise is underestimated by insertion of spherical masses using template multiplication method by 8% - 29% compared to voxel replacement for the test conditions. This resulted in up to 12% variation in contrast-to-noise-ratio (CNR) values between template multiplication and voxel replacement methods.

Simulation of spiculated breast lesions

Virtual clinical trials are a promising new approach increasingly used for the evaluation and comparison of breast imaging modalities. A key component in such an assessment paradigm is the use of simulated pathology, in particular, simulation of lesions. Breast mass lesions can be generally classified into two categories based on their appearance; nonspiculated masses and spiculated masses. In our previous work, we have successfully simulated non-spiculated masses using a fractal growth process known as diffusion limited aggregation. In this new work, we have extended the DLA model to simulate spiculated lesions by using features extracted from patient DBT images containing spiculated lesions. The features extracted included spicule length, width, curvature and distribution. This information was used to simulate realistic looking spicules which were attached to the surface of a DLA mass to produce a spiculated mass. A batch of simulated spiculated masses was inserted into normal patient images and presented to an experienced radiologist for review. The study yielded promising results with the radiologist rating 60% of simulated lesions in 2D and 50% of simulated lesions in DBT as realistic.

Modeling realistic breast lesions using diffusion limited aggregation

Synthesizing the appearance of malignant masses and inserting these into digital mammograms can be used as part of a wider framework for investigating the radiological detection task in X-ray mammography. However, the randomness associated with cell division within cancerous masses and the associated complex morphology challenges the realism of the modeling process. In this paper, Diffusion Limited Aggregation (DLA), a type of fractal growth process is proposed and utilized for modeling breast lesions. Masses of different sizes, shapes and densities were grown by controlling DLA growth parameters either prior to growth, or dynamically updating these during growth. A validation study was conducted by presenting 30 real and 30 simulated masses in a random order to a team of radiologists. The results from the validation study suggest that the observers found it difficult to differentiate between the real and simulated lesions.

Joint activity/attenuation reconstruction in SPECT using photopeak and scatter sinograms

This work presents a joint activity/attenuation reconstruction method in SPECT, based on the maximisation of the scatter and non-scatter data joint log-likelihood. The activity image is updated with standard expectation maximisation (EM) whereas the attenuation is updated with a quasi-Newton line-search. Results on simulation demonstrates that the utilisation of scatter considerably reduces the ill-posedness of the initial reconstruction problem with non-scatter counts only. Results on phantom data show that using scatter enables myocardial reconstruction similar to an EM reconstruction with CT attenuation correction.

Joint reconstruction of activity and attenuation in dynamic PET

Joint reconstruction of attenuation and emission in positron emission tomography (PET) using the maximum likelihood activity and attenuation estimation (MLAA) algorithm was proposed in the past. However, cross-talk between the activity and attenuation estimation limits the usefulness of MLAA for PET data without time-of-flight (TOF) information. This work introduces dynamic MLAA (dMLAA), an extension of the MLAA algorithm for dynamic data, to jointly reconstruct the activity distributions and a single attenuation map. The hypothesis is that using information from multiple dynamic emission frames may improve the estimated attenuation map compared to using static PET data. Preliminary results using dMLAA algorithm showed that use of multiple dynamic emission frames slightly improves the reconstructed attenuation map (especially in bones, cavities and lesion area) compared to using a single emission frame. However, without TOF, the reconstructed map still suffers from ill-posedness of the problem despite the additional dynamic information. The reconstruction may be improved for tracers that present a higher inter- and intra- dynamic frame contrast and edge variability.