Comparison of reconstruction algorithms for digital breast tomosynthesis
I. Reiser, J. Bian, R. M. Nishikawa, E. Y. Sidky, X. Pan
备注：
This article appeared in the proceedings of: The 9th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine July 9 - 13, 2007, Lindau, Germany The full proceedings are available at: http://www.fully3d.org/2007/Fully3D_HPIR_Proceedings.pdf
Digital breast tomosynthesis (DBT) is an emerging modality for breast imaging. A typical tomosynthesis image is reconstructed from projection data acquired at a limited number of views over a limited angular range. In general, the quantitative accuracy of the image can be significantly compromised by severe artifacts and non-isotropic resolution resulting from the incomplete data. Nevertheless, it has been demonstrated that DBT may yield useful information for detection/classification tasks and thus is considered a promising breast imaging modality currently undergoing pre-clinical evaluation trials. The purpose of this work is to conduct a preliminary, but systematic, investigation and evaluation of the properties of reconstruction algorithms that have been proposed for DBT. We use a breast phantom designed for DBT evaluation to generate analytic projection data for a typical DBT configuration, which is currently undergoing pre-clinical evaluation. The reconstruction algorithms under comparison include (i) filtered backprojection (FBP), (ii) expectation maximization (EM), and (iii) TV-minimization algorithms. Results of our studies indicate that FBP reconstructed images are generally noisier and demonstrate lower in-depth resolution than those obtained through iterative reconstruction and that the TV-minimization reconstruction yield images with reduced artifacts as compared to that obtained with other algorithms under study.
In this paper, we would like to quantitatively measure the tumor volume contained in the breast imaged by the Digital Breast Tomosynthesis (DBT), a reconstructed 3D image. The estimated volume will add to the prognostic value of risk classification of breast cancer. We develop an algorithm that offers an alternative way of estimating the volume of the tumor in a breast. We segment the region of interest by expressing the ratio of tumor region to normal region as a function of the threshold value in the image. Next, we determine the volume of the tumor region as a function of the threshold. We …
There are a number of clinically relevant tasks in digital breast tomosynthesis (DBT) involving the detection and visual assessment of fiber-like structures such as Cooper's ligaments, blood vessels, and spiculated lesions. Such structures can exhibit orientation dependent variations in conspicuity. This study demonstrates the presence of in-plane orientation-dependent signal conspicuity for fiber-like signals in DBT and shows how reconstruction algorithm design can mitigate this phenomenon. We uncover a tradeoff between minimizing orientation-dependence and preserving depth resolution that is…
PURPOSE: We develop a practical, iterative algorithm for image-reconstruction in under-sampled tomographic systems, such as digital breast tomosynthesis (DBT). METHOD: The algorithm controls image regularity by minimizing the image total $p$-variation (TpV), a function that reduces to the total variation when $p=1.0$ or the image roughness when $p=2.0$. Constraints on the image, such as image positivity and estimated projection-data tolerance, are enforced by projection onto convex sets (POCS). The fact that the tomographic system is under-sampled translates to the mathematical property that m…
Fiber-like features are an important aspect of breast imaging. Vessels and ducts are present in all breast images, and spiculations radiating from a mass can indicate malignancy. Accordingly, fiber objects are one of the three types of signals used in the American College of Radiology digital mammography (ACR-DM) accreditation phantom. This work focuses on the image properties of fiber-like structures in digital breast tomosynthesis (DBT) and how image reconstruction can affect their appearance. The impact of DBT image reconstruction algorithm and regularization strength on the conspicuity of …
We present the first evaluation of a recently developed silicon-strip detector for photon-counting dual-energy breast tomosynthesis. The detector is well suited for tomosynthesis with high dose efficiency and intrinsic scatter rejection. A method was developed for measuring the spatial resolution of a system based on the detector in terms of the three-dimensional modulation transfer function (MTF). The measurements agreed well with theoretical expectations, and it was seen that depth resolution was won at the cost of a slightly decreased lateral resolution. This may be a justifiable trade-off …
Radboud University Medical Center（拉德堡德大学医学中心）；Delft University of Technology（代尔夫特理工大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Elekta（医科达）；Dutch Expert Centre for Screening（荷兰筛查专家中心）
Digital breast tomosynthesis is rapidly replacing digital mammography as the basic x-ray technique for evaluation of the breasts. However, the sparse sampling and limited angular range gives rise to different artifacts, which manufacturers try to solve in several ways. In this study we propose an extension of the Learned Primal-Dual algorithm for digital breast tomosynthesis. The Learned Primal-Dual algorithm is a deep neural network consisting of several `reconstruction blocks', which take in raw sinogram data as the initial input, perform a forward and a backward pass by taking projections a…
Mammography is a specific type of breast imaging that uses low-dose X-rays to detect cancer in early stage. During the exam, the women breast is compressed between two plates until a nearly uniform breast thickness is obtained. This technique improves image quality and reduces dose but can also be the source of discomfort and sometimes pain for the patient. Therefore, alternative techniques allowing reduced breast compression is of potential interest. The aim of this work is to develop a 3D biomechanical Finite Element (FE) breast model in order to analyze various breast compression strategies…
Measurements of breast density have the potential to improve the efficiency and reduce the cost of screening mammography through personalized screening. Breast density has traditionally been evaluated from the dense area in a mammogram, but volumetric assessment methods, which measure the volumetric fraction of fibro-glandular tissue in the breast, are potentially more consistent and physically sound. The purpose of the present study is to evaluate a method for measuring the volumetric breast density using photon-counting spectral tomosynthesis. The performance of the method was evaluated usin…
Purpose: The goal of this study is to develop a novel deep learning (DL) based reconstruction framework to improve the digital breast tomosynthesis (DBT) imaging performance. Methods: In this work, the DIR-DBTnet is developed for DBT image reconstruction by unrolling the standard iterative reconstruction algorithm within the deep learning framework. In particular, such network learns the regularizer and the iteration parameters automatically through network training with a large amount of simulated DBT data. Afterwards, both numerical and experimental data are used to evaluate its performance.…
Reconstruction of digital breast tomosynthesis is a challenging problem due to the limited angle data available in such systems. Due to memory limitations, deep learning-based methods can help improve these reconstructions, but can not (yet) attain sufficiently high resolution. In addition to this practical issue, questions remain on the possibility of such models introducing 'ghost' information from the training data that is not compatible with the projection data. To take advantage of some of the benefits of deep learning-based reconstructions while avoiding these limitations, we propose to …
Digital Breast Tomosynthesis (DBT) provides an insight into the fine details of normal fibroglandular tissues and abnormal lesions by reconstructing a pseudo-3D image of the breast. In this respect, DBT overcomes a major limitation of conventional X-ray mammography by reducing the confounding effects caused by the superposition of breast tissue. In a breast cancer screening or diagnostic context, a radiologist is interested in detecting change, which might be indicative of malignant disease. To help automate this task image registration is required to establish spatial correspondence between t…
Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation
Radboud University Medical Center（拉德堡德大学医学中心）；Netherlands Cancer Institute（荷兰癌症研究所）；The University of Chicago（芝加哥大学）；University of Pennsylvania（宾夕法尼亚大学）；Lund University（隆德大学）；Vall d’Hebron Institute of Oncology（巴尔德希伯伦肿瘤研究所）；University of Barcelona（巴塞罗那大学）；Dutch Expert Centre for Screening (LRCB)（荷兰筛查专家中心）
Simulation-based image quality metrics are adapted and investigated for characterizing the parameter dependences of linear iterative image reconstruction for DBT. Three metrics based on 2D DBT simulation are investigated: (1) a root-mean-square-error (RMSE) between the test phantom and reconstructed image, (2) a gradient RMSE where the comparison is made after taking a spatial gradient of both image and phantom, and (3) a region-of-interest (ROI) Hotelling observer (HO) for signal-known-exactly/background-known-exactly (SKE/BKE) and signal-known-exactly/background-known-statistically (SKE/BKS)…
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Kitware, Inc.（Kitware公司）
Registration is widely used in image-guided therapy and image-guided surgery to estimate spatial correspondences between organs of interest between planning and treatment images. However, while high-quality computed tomography (CT) images are often available at planning time, limited angle acquisitions are frequently used during treatment because of radiation concerns or imaging time constraints. This requires algorithms to register CT images based on limited angle acquisitions. We, therefore, formulate a 3D/2D registration approach which infers a 3D deformation based on measured projections a…
Towards order of magnitude X-ray dose reduction in breast cancer imaging using phase contrast and deep denoising
The University of Melbourne（墨尔本大学）；The University of Sydney（悉尼大学）；Eastern Health（东部健康）；Monash Health（莫纳什健康）；Monash University（莫纳什大学）；Australian Synchrotron, ANSTO（澳大利亚同步辐射中心，ANSTO）；Commonwealth Scientific and Industrial Research Organisation（澳大利亚联邦科学与工业研究组织）
Fluorescence molecular tomography (FMT) has potential of providing high contrast images for breast tumor detection. Computational phantom provides a convenient way to a wide variety of fluorophore distribution configurations in patients and perform comprehensive evaluation of the imaging systems and methods for FMT. In this study, a digital breast phantom was used to compare the performance of a novel sparsity-based reconstruction method and Tikhonov regularization method for resolving tumors with different amount of separation. The results showed that the proposed sparse reconstruction method…
In this work the metabolic heat generated by breast tumors was estimated indirectly and noninvasively from digital infrared images and numerically simulating a simplified breast model and a cancerous tumor, this parameter can be of clinical importance since it has been related to the doubling volume's time and malignancy for that particular tumor. The results indicate that digital infrared imaging has the potential to estimate in a non-invasive way the malignancy of a tumor by calculating its metabolic heat generation from bioheat thermal transfer models.
Changes in Visual Attention Patterns for Detection Tasks due to Dependencies on Signal and Background Spatial Frequencies
Department of Biomedical Engineering, University of Houston（生物医学工程系，休斯顿大学）；Department of Physics, University of Houston（物理系，休斯顿大学）；Department of Electrical and Computer Engineering, University of Houston（电气与计算机工程系，休斯顿大学）
Our interest has been to study the effect that scattered radiation has on contrast, signal-to-noise ratio and thickness reconstruction in digital mammographies. Using the GEANT code we have performed Monte-Carlo simulations of 25 kVp Mo/Mo photons, through a breast phantom which contains a 0.2-1.0 mm thick microcalcifications incident on a 20x106 $mm^{2}$ pixelized detector. The data have been analyzed assuming 6 different shapes of the incident beam: a 0.2x0.2 $mm^{2}$ ``narrow'' beam, 4 different 20 mm long scanning beams of various widths, and a 20x100 $mm^{2}$ beam with no scatter reductio…
Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Fudan University（复旦大学）；Shanghai Center for Brain Science and Brain-inspired Technology（上海脑科学与类脑研究中心）；São Carlos School of Engineering, University of São Paulo（圣保罗大学圣卡洛斯工程学院）；Ribeirão Preto School of Medicine, University of São Paulo（圣保罗大学里贝朗普雷图医学院）
College of Computer Science, Sichuan University（四川大学计算机学院）；Laboratory of Image Science and Technology, Southeast University（东南大学图像科学与技术实验室）；Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences（中国科学院高能物理研究所北京市射线成像技术与装备工程研究中心）；School of Electrical Engineering Information, Sichuan University（四川大学电气信息学院）；Department of Radiology, West China Hospital of Sichuan University（四川大学华西医院放射科）
Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis and interior tomography. Traditional compressed sensing focuses on the design of handcrafted prior regularizers, which are usually image-dependent and time-consuming. Inspired by recently proposed deep learning-based CT reconstruction models, we extend the state-of-the-art LEARN model to a dual-domain version, dubbed LEARN++. Different from existing iteration unrolling methods, which only involve projection data in th…
im3D S.p.A.（im3D股份公司）；APSS；KTH（瑞典皇家理工学院）；ISPO；Centro prevenzione oncologica（肿瘤预防中心）；IRCCS Policlinico San Donato（圣多纳托IRCCS医院）；ASLBi；IRCC；Regional Reference Centre for Breast Cancer Screening（乳腺癌筛查区域参考中心）；Ospedale Valduce（瓦尔杜切医院）；Ospedale S. Martino（圣马蒂诺医院）；C.d.C. Paideia；IRCCS, National Cancer Institute "G. Pascale" Foundation of Naples（那不勒斯G.帕斯卡尔国家癌症研究所IRCCS基金会）；AUSL；Ospedale Regionale（地区医院）；OIRM S.Anna（OIRM圣安娜医院）；Università degli Studi di Milano（米兰大学）
Objectives: To compare breast density (BD) assessment provided by an automated BD evaluator (ABDE) with that provided by a panel of experienced breast radiologists, on a multivendor dataset. Methods: Twenty-one radiologists assessed 613 screening/diagnostic digital mammograms from 9 centers and 6 different vendors, using the BI-RADS a, b, c, and d density classification. The same mammograms were also evaluated by an ABDE providing the ratio between fibroglandular and total breast area on a continuous scale and, automatically, the BI-RADS score. Panel majority report (PMR) was used as reference…
Automated HER2 scoring with uncertainty quantification using lensfree holography and deep learning
Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA（加州大学洛杉矶分校电气与计算机工程系）；Bioengineering Department, University of California, Los Angeles, CA, 90095, USA（加州大学洛杉矶分校生物工程系）；California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA（加州大学洛杉矶分校加州纳米系统研究所）；Department of Computer Science, University of California, Los Angeles, CA, 90095, USA（加州大学洛杉矶分校计算机科学系）
