Breast CT Images Research Articles

Deep learning-based segmentation of breast masses in dedicated breast CT imaging: Radiomic feature stability between radiologists and artificial intelligence

A deep learning (DL) network for 2D-based breast mass segmentation in unenhanced dedicated breast CT images was developed and validated, and its robustness in radiomic feature stability and diagnostic performance compared to manual annotations of multiple radiologists was investigated. 93 mass-like lesions were extensively augmented and used to train the network (n = 58 masses), which was then tested (n = 35 masses) against manual ground truth of a qualified breast radiologist with experience in breast CT imaging using the Conformity coefficient (with a value equal to 1 indicating a perfect performance). Stability and diagnostic power of 672 radiomic descriptors were investigated between the computerized segmentation, and 4 radiologists' annotations for the 35 test set cases. Feature stability and diagnostic performance in the discrimination between benign and malignant cases were quantified using intraclass correlation (ICC) and multivariate analysis of variance (MANOVA), performed for each segmentation case (4 radiologists and DL algorithm). DL-based segmentation resulted in a Conformity of 0.85 ± 0.06 against the annotated ground truth. For the stability analysis, although modest agreement was found among the four annotations performed by radiologists (Conformity 0.78 ± 0.03), over 90% of all radiomic features were found to be stable (ICC>0.75) across multiple segmentations. All MANOVA analyses were statistically significant (p ≤ 0.05), with all dimensions equal to 1, and Wilks’ lambda ≤0.35. In conclusion, DL-based mass segmentation in dedicated breast CT images can achieve high segmentation performance, and demonstrated to provide stable radiomic descriptors with comparable discriminative power in the classification of benign and malignant tumors to expert radiologist annotation.

Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction.

Breast CT provides image volumes with isotropic resolution in high contrast, enabling detection of small calcification (down to a few hundred microns in size) and subtle density differences. Since breast is sensitive to x-ray radiation, dose reduction of breast CT is an important topic, and for this purpose, few-view scanning is a main approach. In this article, we propose a Deep Efficient End-to-end Reconstruction (DEER) network for few-view breast CT image reconstruction. The major merits of our network include high dose efficiency, excellent image quality, and low model complexity. By the design, the proposed network can learn the reconstruction process with as few as parameters, where N is the side length of an image to be reconstructed, which represents orders of magnitude improvements relative to the state-of-the-art deep-learning-based reconstruction methods that map raw data to tomographic images directly. Also, validated on a cone-beam breast CT dataset prepared by Koning Corporation on a commercial scanner, our method demonstrates a competitive performance over the state-of-the-art reconstruction networks in terms of image quality. The source code of this paper is available at: https://github.com/HuidongXie/DEER.

Deformable Mapping Method to Relate Lesions in Dedicated Breast CT Images to Those in Automated Breast Ultrasound and Digital Breast Tomosynthesis Images

This work demonstrates the potential for using a deformable mapping method to register lesions between dedicated breast computed tomography (bCT) and both automated breast ultrasound (ABUS) and digital breast tomosynthesis (DBT) images (craniocaudal [CC] and mediolateral oblique [MLO] views). Two multi-modality breast phantoms with external fiducial markers attached were imaged by the three modalities. The DBT MLO view was excluded for the second phantom. The automated deformable mapping algorithm uses biomechanical modeling to determine corresponding lesions based on distances between their centers of mass (dCOM) in the deformed bCT model and the reference model (DBT or ABUS). For bCT to ABUS, the mean dCOM was 5.2 ± 2.6 mm. For bCT to DBT (CC), the mean dCOM was 5.1 ± 2.4 mm. For bCT to DBT (MLO), the mean dCOM was 4.7 ± 2.5 mm. This application could help improve a radiologist's efficiency and accuracy in breast lesion characterization, using multiple imaging modalities.

Propagation-based x-ray phase-contrast tomography of mastectomy samples using synchrotron radiation.

Propagation-based phase-contrast computed tomography (PB-CT) is a method for three-dimensional x-ray imaging that utilizes refraction, as well as absorption, of x rays in the tissues to increase the signal-to-noise ratio (SNR) in the resultant images, in comparison with equivalent conventional absorption-only x-ray tomography (CT). Importantly, the higher SNR is achieved without sacrificing spatial resolution or increasing the radiation dose delivered to the imaged tissues. The present work has been carried out in the context of the current development of a breast CT imaging facility at the Australian Synchrotron. Seven unfixed complete mastectomy samples with and without breast cancer lesions have been imaged using absorption-only CT and PB-CT techniques under controlled experimental conditions. The radiation doses delivered to the mastectomy samples during the scans were comparable to those approved for mammographic screening. Physical characteristics of the reconstructed images, such as spatial resolution and SNR, have been measured and compared with the results of the radiological quality assessment of the complete absorption CT and PB-CT image stacks. Despite the presence of some image artefacts, the PB-CT images have outperformed comparable absorption CT images collected at the same radiation dose, in terms of both the measured objective image characteristics and the radiological image scores. The outcomes of these experiments are shown to be consistent with predictions of the theory of PB-CT imaging and previous reported experimental studies of this imaging modality. The results presented in this paper demonstrate that PB-CT holds a high potential for improving on the quality and diagnostic value of images obtained using existing medical x-ray technologies, such as mammography and digital breast tomosynthesis (DBT). If implemented at suitable synchrotron imaging facilities, PB-CT can be used to complement existing imaging modalities, leading to more accurate breast cancer diagnosis.

Multi-Energy Computed Tomography Breast Imaging with Monte Carlo Simulations: Contrast-to-Noise-Based Image Weighting.

Context:Photon-counting detectors and breast computed tomography imaging have been an active area of research. With these detectors, photons are assigned an equal weight and weighting schemes can be enabled. More weight can be assigned to lower energies, resulting in an increase in the contrast-to-noise ratio (CNR).Aims:The aim of this study is to develop and evaluate an energy weighting imaging technique to improve the CNR of simulated breast phantoms and to improve tumour detection.Materials and Methods:Breast phantoms consisting of adipose, glandular, malignant tissues and iodine contrast were constructed with BreastSimulator software. The phantoms were used in egs_cbct simulations for energies ranging between 20 and 65 keV from which multiple images were reconstructed. A new CNR-based image weighting method was proposed based on the CNR values obtained from the images. This method improves on previous methods and can be applied to complicated phantoms since no structural information is needed.Results:An increase in the CNR can be seen for lower energies. A sharp increase in the CNR is seen just above the K-edge for the phantoms with the iodine contrast. The CNR-based image weighting leads to a 68.47% (1.68-fold) increase in the CNR for the malignant tissue without iodine. For the malignant tissue with iodine contrast, the increase in the CNR was 96.14% (1.96-fold).Conclusions:The new proposed CNR-based image weighting scheme is easy to implement and can be used for complicated phantoms with varying structures. A large increase in the CNR is seen with or without the use of iodine contrast.

Patient-based 4D digital breast phantom for perfusion contrast-enhanced breast CT imaging.

PurposeThe purpose of this study was to develop a realistic patient‐based 4D digital breast phantom including time‐varying contrast enhancement for simulation of dedicated breast CT perfusion imaging.MethodsA 3D static phantom is first created by segmenting a breast CT image from a healthy patient into skin, fibroglandular tissue, adipose tissue, and vasculature. For the creation of abnormal cases, a breast lesion model was developed and can be added to the phantom. After defining the necessary perfusion parameters for each tissue (e.g., arterial input function for vasculature, blood volume and blood flow for the other normal tissues) based on contrast‐enhanced dynamic breast MRI data, the corresponding time‐enhancement curves are computed for each voxel in the phantom, according to tissue type. These curves are calculated by convolution between the arterial input function and a shifted exponential function. This exponential depends on the perfusion parameters associated with each tissue voxel, and, to incorporate normal biological variability, a uniform random distribution is used to vary the perfusion parameters on a voxel‐basis. Finally, a 4D array is produced by sampling the continuous time‐enhancement curves at the desired sampling rate. Beside modeling different enhancement dynamics according to the given input perfusion parameters, the phantom also includes the possibility to realistically simulate different spatial enhancement patterns for the breast parenchyma, taking into account the arterial sources supplying the breast. Finally, different patterns of contrast medium uptake can also be simulated for the tumor models (homogeneous and rim enhancement).ResultsAs an example, a typical 4D phantom has dimensions of 426 × 421 × 260 × 559 (x, y, z, t), with a voxel size of 273 μm and a sampling time of 1 s. The characteristics of the tumor model can be modified at will to evaluate perfusion in different types of breast lesions. Results show the expected enhancement of tissues, consistent with the given input parameters. Moreover, the tumor models evaluated in this work show different enhancement dynamics according to the tumor type (defined by different input perfusion parameters), and also present a higher enhancement compared to the other healthy tissues, as expected.ConclusionsThe proposed digital phantom can model the breast tissue perfusion during 4D breast CT image acquisition, displaying the different enhancement dynamics that could be found in a real patient breast. This phantom can be used during the development of dynamic contrast‐enhanced dedicated breast CT imaging, for optimization of image acquisition, image reconstruction, and image analysis. This modality could provide functional information of the breast, resulting in detection, diagnosis, and treatment improvements of breast cancer with breast CT.

Advances in digital and physical anthropomorphic breast phantoms for x-ray imaging.

With the advent of three-dimensional (3D) breast imaging modalities such as digital breast tomosynthesis (DBT) and dedicated breast CT (bCT), research into new anthropomorphic breastphantoms has accelerated. These breast phantoms are important for the optimization of new breast imaging systems, assessing new regulatory submissions to prove safety and effectiveness, and for developing new approaches to acceptance and constancy testing of 3D breast imaging systems. This paper provides a review of current research investigating both digital and physical breast phantom development for use in x-ray based imaging. Two approaches for designing anthropomorphic, digital breast phantoms are discussed, procedural model-based phantom generation, where breast features are expressed using mathematical models, and patient-based generation, where breast structures from tissue specimens or patient-based breast MR or CT volumes are segmented. Following this discussion, a review of physical anthropomorphic phantoms is given, with emphasis on the advantages and disadvantages present with each approach. This paper provides a summary of the state-of-the-art in anthropomorphic breast phantom development for x-ray breast imaging. The primary advantage of model-based digital phantoms is that an unlimited number of phantoms with varying size, shape, and density can be generated. Current research on model-based breast phantoms is producing more and more realistic breast models; however, they probably are not yet able to pass the so-called "fool the radiologist" visualization test. Empirical patient-based breast phantoms are typically based on clinical breast CT data and look more realistic. However, clinical breast CT images have limited spatial resolution and thus do not always portray the finer details in the breast. A number of innovative solutions have been proposed for fabricating physical anthropomorphic breast phantoms based on digital phantom models; however, a number of challenges remain, including realistic modeling of x-ray attenuation properties and accurately representing high-frequency structures within breast.

Investigation of contrast-enhanced subtracted breast CT images with MAP-EM based on projection-based weighting imaging.

Contrast-enhanced subtracted breast computer tomography (CESBCT) images acquired using energy-resolved photon counting detector can be helpful to enhance the visibility of breast tumors. In such technology, one challenge is the limited number of photons in each energy bin, thereby possibly leading to high noise in separate images from each energy bin, the projection-based weighted image, and the subtracted image. In conventional low-dose CT imaging, iterative image reconstruction provides a superior signal-to-noise compared with the filtered back projection (FBP) algorithm. In this paper, maximum a posteriori expectation maximization (MAP-EM) based on projection-based weighting imaging for reconstruction of CESBCT images acquired using an energy-resolving photon counting detector is proposed, and its performance was investigated in terms of contrast-to-noise ratio (CNR). The simulation study shows that MAP-EM based on projection-based weighting imaging can improve the CNR in CESBCT images by 117.7%-121.2% compared with FBP based on projection-based weighting imaging method. When compared with the energy-integrating imaging that uses the MAP-EM algorithm, projection-based weighting imaging that uses the MAP-EM algorithm can improve the CNR of CESBCT images by 10.5%-13.3%. In conclusion, MAP-EM based on projection-based weighting imaging shows significant improvement the CNR of the CESBCT image compared with FBP based on projection-based weighting imaging, and MAP-EM based on projection-based weighting imaging outperforms MAP-EM based on energy-integrating imaging for CESBCT imaging.

2 Milisievert Low Dose Dedicated Breast CT with Iterative Model Reconstruction Technique in Evaluation of Breast Mass: Feasibility Study in Comparison with MRI

Objective To investigate the feasibility of low dose dedicated computed tomography (CT) with IMR technique compared with magnetic resonance imaging (MRI) in preoperative evaluation of breast cancer. Methods Dedicated CT and breast MRI were performed in 21 patients with diagnosed breast mass for preoperative evaluation. A dedicated protocol combined with chest diagnostic CT and dynamic scans of breast was developed with the use of IMR technique as well as optimization of scan parameters to ensure acceptable radiation dose. Image quality evaluations of CT images were performed using a five-point scale. The number, site and size (maximum diameter) of breast lesion were recorded respectively in CT and MRI images. The enhancement patterns of breast lesion were classified to 3 types (washout, plateau, and persistence) according to time intensity curve derived from CT and time signal curve derived from MRI, respectively. Pearson’s correlation, Bland-Altman analysis, and Cohen’s kappa test were used for statistical assessment. Results The mean effective radiation dose of dedicated breast CT, with the image quality diagnostic acceptable for evaluation of breast lesion and pulmonary structures, was (2.15±0.39) mSv, which was no more than a routine chest diagnostic CT. Results of breast lesion number and lesion site obtained by CT and MRI was consistent, with a total of 24 lesions detected in 21 patients. The mean lesion size was (26.5±12.1) mm in CT and (26.1±12.9) mm in MR, respectively. CT showed an excellent correlation with MRI for lesion size (r=0.99, n=24, p<0.0001). Limits of agreement determined by Bland-Altman analysis for lesion extent was (-3.0mm, 3.8mm). Good agreement was observed between CT and MRI for lesion enhancement patterns (kappa value = 0.936, p=0.000). Conclusion Dedicated breast CT imaging with acceptable radiation dose enabled diagnostic image quality and showed good agreement with breast MR imaging in preoperative evaluation of breast cancer.

Kernel-based anatomically-aided diffuse optical tomography reconstruction

Image reconstruction in diffuse optical tomography (DOT) is a challenging task because its inverse problem is nonlinear, ill-posed, and ill-conditioned. Anatomical guidance from imaging modalities with high spatial resolution can substantially improve the quality of reconstructed DOT images. In this paper, inspired by the kernel methods in machine learning, we proposed a kernel method to introduce anatomical guidance into the DOT image reconstruction. In this kernel method, the optical absorption coefficient at each finite element node is represented as a function of a set of features obtained from anatomical images such as computed tomography (CT) images. Compared with Laplacian approaches that include structural priors, the proposed method does not require image segmentation. The proposed kernel method is validated with numerical simulations of 3D DOT reconstruction using synthetic CT data. 5% Gaussian noise was added to both the numerical DOT measurements and the simulated CT images. The proposed method was also validated by an agar phantom experiment with the anatomical guidance from a cone beam CT scan. The effects of voxel size and number of nearest neighbors in the kernel method on the reconstructed DOT images were studied. The results indicate that the spatial resolution and the accuracy of the reconstructed DOT images have been improved substantially after applying the anatomical guidance with the proposed kernel method comparing to the case without guidance. Furthermore, we demonstrated that the kernel method was able to utilize clinical breast CT images as anatomical guidance without segmentation. In addition, we found that the proposed kernel method was robust to the false positive guidance in the anatomical image.

Evaluation of the BreastSimulator software platform for breast tomography

The aim of this work was the evaluation of the software BreastSimulator, a breast x-ray imaging simulation software, as a tool for the creation of 3D uncompressed breast digital models and for the simulation and the optimization of computed tomography (CT) scanners dedicated to the breast. Eight 3D digital breast phantoms were created with glandular fractions in the range 10%–35%. The models are characterised by different sizes and modelled realistic anatomical features. X-ray CT projections were simulated for a dedicated cone-beam CT scanner and reconstructed with the FDK algorithm. X-ray projection images were simulated for 5 mono-energetic (27, 32, 35, 43 and 51 keV) and 3 poly-energetic x-ray spectra typically employed in current CT scanners dedicated to the breast (49, 60, or 80 kVp). Clinical CT images acquired from two different clinical breast CT scanners were used for comparison purposes. The quantitative evaluation included calculation of the power-law exponent, β, from simulated and real breast tomograms, based on the power spectrum fitted with a function of the spatial frequency, f, of the form S(f) = α/f β. The breast models were validated by comparison against clinical breast CT and published data. We found that the calculated β coefficients were close to that of clinical CT data from a dedicated breast CT scanner and reported data in the literature. In evaluating the software package BreastSimulator to generate breast models suitable for use with breast CT imaging, we found that the breast phantoms produced with the software tool can reproduce the anatomical structure of real breasts, as evaluated by calculating the β exponent from the power spectral analysis of simulated images. As such, this research tool might contribute considerably to the further development, testing and optimisation of breast CT imaging techniques.

Potential Use of American College of Radiology BI-RADS Mammography Atlas for Reporting and Assessing Lesions Detected on Dedicated Breast CT Imaging: Preliminary Study

Dedicated breast computed tomography (DBCT) is an emerging and promising modality for breast lesions. The objective of this study was to evaluate the potential use of applying the BI-RADS Mammography Atlas 5th Edition for reporting and assessing breast lesions on DBCT. Currently, no atlas exists for DBCT. Four radiologists trained in breast imaging were recruited in this institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study. The enrolled radiologists, who were blinded to mammographic and histopathologic findings, individually reviewed 30 randomized DBCT cases that contained marked lesions. Thirty-four lesions were included in this study: 24 (70.6%) masses, 7 (20.6%) calcifications, and 3 (8.8%) architectural distortions. Eight (23.5%) lesions were malignant and 26 (76.5%) were benign. The reader was asked to specify according to the BI-RADS Mammography Atlas for each marked DBCT lesion: primary findings, features, breast density, and final assessment. We calculated readers' diagnostic performances for differentiating between benign and malignant lesions and interobserver variability for reporting and assessing lesions using a generalized estimating equation and the Fleiss kappa (κ) statistic. The estimated overall sensitivity of the readers was 0.969, and the specificity was 0.529. There were no significant differences in the sensitivity and the specificity between lesion types. For reporting the presence of a primary finding, the overall substantial agreement (κ = 0.70) was seen. In assigning the breast density and the final assessment, the overall agreement was moderate (κ = 0.53) and fair (κ = 0.30). The use of the BI-RADS Mammography Atlas 5th Edition for DBCT showed high performance and good agreement among readers.

Towards breast cancer rotational radiotherapy with synchrotron radiation

PurposeWe performed the first investigations, via measurements and Monte Carlo simulations on phantoms, of the feasibility of a new technique for synchrotron radiation rotational radiotherapy for breast cancer (SR3T). MethodsA Monte Carlo (MC) code based on Geant4 toolkit was developed in order to simulate the irradiation with the SR3T technique and to evaluate the skin sparing effect in terms of centre-to-periphery dose ratio at different energies in the range 60–175keV. Preliminary measurements were performed at the Australian Synchrotron facility. Radial dose profiles in a 14-cm diameter polyethylene phantom were measured with a 100-mm pencil ionization chamber for different beam sizes and compared with the results of MC simulations. Finally, the dose painting feasibility was demonstrated with measurements with EBT3 radiochromic films in a phantom and collimating the SR beam at 1.5cm in the horizontal direction. ResultsMC simulations showed that the SR3T technique assures a tumour-to-skin absorbed dose ratio from about 7:1 (at 60keV photon energy) to about 10:1 (at 175keV), sufficient for skin sparing during radiotherapy. The comparison between the results of MC simulations and measurements showed an agreement within 5%. Two off-centre foci were irradiated shifting the rotation centre in the horizontal direction. ConclusionsThe SR3T technique permits to obtain different dose distributions in the target with multiple rotations and can be guided via synchrotron radiation breast computed tomography imaging, in propagation based phase-contrast conditions. Use of contrast agents like iodinated solutions or gold nanoparticles for dose enhancement (DE-SR3T) is foreseen and will be investigated in future work.

Lack of agreement between radiologists: implications for image-based model observers.

We tested the agreement of radiologists' rankings of different reconstructions of breast computed tomography images based on their diagnostic (classification) performance and on their subjective image quality assessments. We used 102 pathology proven cases (62 malignant, 40 benign), and an iterative image reconstruction (IIR) algorithm to obtain 24 reconstructions per case with different image appearances. Using image feature analysis, we selected 3 IIRs and 1 clinical reconstruction and 50 lesions. The reconstructions produced a range of image quality from smooth/low-noise to sharp/high-noise, which had a range in classifier performance corresponding to AUCs of 0.62 to 0.96. Six experienced Mammography Quality Standards Act (MQSA) radiologists rated the likelihood of malignancy for each lesion. We conducted an additional reader study with the same radiologists and a subset of 30 lesions. Radiologists ranked each reconstruction according to their preference. There was disagreement among the six radiologists on which reconstruction produced images with the highest diagnostic content, but they preferred the midsharp/noise image appearance over the others. However, the reconstruction they preferred most did not match with their performance. Due to these disagreements, it may be difficult to develop a single image-based model observer that is representative of a population of radiologists for this particular imaging task.

Cross-sectional imaging to evaluate the extent of regional nodal disease in breast cancer patients undergoing neoadjuvant systemic therapy

PurposeCross-sectional imaging often is performed in breast cancer patients undergoing neoadjuvant systemic therapy (NST) and may identify level III axillary and extra-axillary nodal disease. Our aim was to investigate associations of radiologic nodal staging with pathological N (pN) stage at operation and to explore how this might aid surgical and radiotherapy treatment planning. Materials and methodsWith IRB approval, we reviewed pre-treatment breast MRI, PET/CT, and CT imaging and clinicopathologic data on 348 breast cancer patients with imaging available for review undergoing NST followed by operation at our institution 1/2008-9/2013. We defined abnormal lymph node findings on MRI, CT, and PET/CT to include cortical thickening, FDG-avidity and loss of fatty hilum. Patients were assigned a radiologic nodal (rN) stage based on imaging findings. Statistical analysis was performed using JMP 10.1 software ResultsPre-NST imaging included axillary ultrasound in 338 patients (97%), breast MRI in 305 (88%) and PET/CT or CT in 215 (62%). 213 patients (61%) were biopsy-proven axillary lymph node-positive (LN+) pre-treatment. cT stage was T1 in 9%, T2 in 49%, T3 in 29%, T4 in 12%; median tumor size was 4cm. Pre-treatment rN stage across all the patients was rN0 in 86 (25%), rN1 in 173 (50%), and rN3 in 89 (26%). rN3 disease included level III axillary, supraclavicular and suspicious internal mammary lymph nodes in 47 (53%), 32 (37%) and 45 (52%), respectively. Of patients LN+ at diagnosis, 78 (37%) were rN3. After NST, 162 patients (47%) were node-positive at operation with a median (mean) of 3 (5.9±0.4) positive lymph nodes including 128 of 213 (60%) LN+ at diagnosis. Pre-NST rN stage correlated with the likelihood and extent of axillary disease at operation, p=0.002. Fifty four of 89 rN3 patients (61%) were node-positive at operation with a median (mean) of 5 (8±1) positive nodes. rN3 patients had larger nodal metastases (median 9 vs 6mm) and more frequent extranodal extension (61% vs 43%) than rN0/rN1 patients, both p<0.03. ConclusionsInformation on rN stage from pre-NST cross-sectional imaging informs the likelihood and extent of axillary nodal disease at operation. This information may be used for surgical and radiotherapy treatment planning and to inform patient expectations.

Initial Experience with a Cone-beam Breast Computed Tomography-guided Biopsy System.

Objective:To evaluate our initial experience with a cone-beam breast computed tomography (BCT)-guided breast biopsy system for lesion retrieval in phantom studies for use with a cone-beam BCT imaging system.Materials and Methods:Under the Institutional Review Board approval, a phantom biopsy study was performed using a dedicated BCT-guided biopsy system. Fifteen biopsies were performed on each of the small, medium, and large anthropomorphic breast phantoms with both BCT and stereotactic guidance for comparison. Each set of the 45 phantoms contained masses and calcification clusters of varying sizes. Data included mass/calcium retrieval rate and dose and length of procedure time for phantom studies.Results:Phantom mass and calcium retrieval rate were 100% for BCT and stereotactic biopsy. BCT dose for small and medium breast phantoms was found to be equivalent to or less than the corresponding stereotactic approach. Stereotactic-guided biopsy dose was 34.2 and 62.5 mGy for small and medium breast phantoms, respectively. BCT-guided biopsy dose was 15.4 and 30.0 mGy for small and medium breast phantoms, respectively. Both computed tomography biopsy and stereotactic biopsy study time ranged from 10 to 20 min.Conclusion:Initial experience with a BCT-guided biopsy system has shown to be comparable to stereotactic biopsy in phantom studies with equivalent or decreased dose.

Areas of breast tissue covered in cone beam breast CT imaging.

The value of cone beam breast computed tomography (CBBCT) imaging on covered areas of breast tissue, which is the relation between imaging quality and CT dose were studied. Multi-energy spectrum was used to radiate same-size built-in calcifications and lump breast motifs under the condition of the same number of particles by utilizing the Monte Carlo-based GATE simulation software; breast motif images were restructured by using FBP restructuration algorithm to gain the distribution of radiation dose in the breast motif; radiation dose was calculated and signal-to-noise ratio (SNR) to define how quality factor M and dose efficiency η reflect the relations between radiation dose and imaging quality. Based on the comparison of the calcification number, diameter, and the diameter of tumor among head side, foot side, inner side, outer side and rear side, the difference was meaningless in terms of statistics. Based on the comparison between SNR and contrast-to-noise ratio and between dose efficiency η and quality factor M in different areas, the difference was not statistically significant. In conclusion, the imaging quality of CBBCT was good in the head, foot, inner, outer and rear sides of breast, with acceptable CT dose.

Potential Use of American College of Radiology BI-RADS Mammography Atlas for Reporting and Assessing Lesions Detected on Dedicated Breast CT Imaging: Preliminary Study

Potential Use of American College of Radiology BI-RADS Mammography Atlas for Reporting and Assessing Lesions Detected on Dedicated Breast CT Imaging: Preliminary Study

Temporal subtraction contrast-enhanced dedicated breast CT

The development of a framework of deformable image registration and segmentation for the purpose of temporal subtraction contrast-enhanced breast CT is described.An iterative histogram-based two-means clustering method was used for the segmentation. Dedicated breast CT images were segmented into background (air), adipose, fibroglandular and skin components. Fibroglandular tissue was classified as either normal or contrast-enhanced then divided into tiers for the purpose of categorizing degrees of contrast enhancement. A variant of the Demons deformable registration algorithm, intensity difference adaptive Demons (IDAD), was developed to correct for the large deformation forces that stemmed from contrast enhancement. In this application, the accuracy of the proposed method was evaluated in both mathematically-simulated and physically-acquired phantom images. Clinical usage and accuracy of the temporal subtraction framework was demonstrated using contrast-enhanced breast CT datasets from five patients. Registration performance was quantified using normalized cross correlation (NCC), symmetric uncertainty coefficient, normalized mutual information (NMI), mean square error (MSE) and target registration error (TRE).The proposed method outperformed conventional affine and other Demons variations in contrast enhanced breast CT image registration. In simulation studies, IDAD exhibited improvement in MSE (0–16%), NCC (0–6%), NMI (0–13%) and TRE (0–34%) compared to the conventional Demons approaches, depending on the size and intensity of the enhancing lesion. As lesion size and contrast enhancement levels increased, so did the improvement. The drop in the correlation between the pre- and post-contrast images for the largest enhancement levels in phantom studies is less than 1.2% (150 Hounsfield units). Registration error, measured by TRE, shows only submillimeter mismatches between the concordant anatomical target points in all patient studies. The algorithm was implemented using a parallel processing architecture resulting in rapid execution time for the iterative segmentation and intensity-adaptive registration techniques.Characterization of contrast-enhanced lesions is improved using temporal subtraction contrast-enhanced dedicated breast CT. Adaptation of Demons registration forces as a function of contrast-enhancement levels provided a means to accurately align breast tissue in pre- and post-contrast image acquisitions, improving subtraction results. Spatial subtraction of the aligned images yields useful diagnostic information with respect to enhanced lesion morphology and uptake.

TH-AB-209-08: Next Generation Dedicated 3D Breast Imaging with XACT

Purpose: Exposure to radiation increases the risk of cancer. We have designed a new imaging paradigm, X-ray induced acoustic computed tomography (XACT). Applying this innovative technology to breast imaging, an X-ray exposure can generate a 3D acoustic image, which dramatically reduces the radiation dose to patients when compared to conventional breast CT. Methods: Theoretical calculations are done to determine the appropriate X-ray energy and ultrasound frequency in breast XACT imaging. A series of breast CT image along the coronal plane from a patient with calcifications in the breast tissue are used as the source image. HU value based segmentation is done to distinguish the skin, adipose tissue, glandular tissue, breast calcification, and chest bone from each CT image. X-ray dose deposition in each pixel is calculated based on the tissue type by using GEANT4 Monte Carlo toolkits. The initial pressure rise caused by X-ray energy deposition is calculated according to tissue properties. Then, the X-ray induced acoustic wave propagation is simulated by K-WAVE toolkit. Breast XACT images are reconstructed from the recorded time-dependent ultrasound waves. Results: For imaging a breast with large size (16cm in diameter at chest wall), the photon energy of X-ray source and the central frequency of ultrasound detector is determined as 20keV and 5.5MHz. Approximately 10 times contrast between a calcification and the breast tissue can be acquire from XACT image. The calcification can be clearly identified from the reconstructed XACT image. Conclusion: XACT technique takes the advantages of X-ray absorption contrast and high ultrasonic resolution. With the proposed innovative technology, one can potentially reduce radiation dose to patient in 3D breast imaging as compared with current x-ray modalities, while still maintaining high imaging contrast and spatial resolution.