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Numerical methods for coupled reconstruction and registration in digital breast tomosynthesis.

机译:数字乳腺断层合成中耦合重建和配准的数值方法。

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摘要

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 mam- mography 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 time points. Typically, images, such as MRI or CT, are first reconstructed and then registered. This approach can be effective if reconstructing using a complete set of data. However, for ill-posed, limited-angle problems such as DBT, estimating the deformation is com- plicated by the significant artefacts associated with the reconstruction, leading to severe inaccuracies in the registration. This paper presents a mathematical framework, which couples the two tasks and jointly estimates both image intensities and the parameters of a transformation. Under this framework, we compare an iterative method and a simultaneous method, both of which tackle the problem of comparing DBT data by combining reconstruction of a pair of temporal volumes with their registration. We evaluate our methods using various computational digital phantoms, uncom- pressed breast MR images, and in-vivo DBT simulations. Firstly, we compare both iter- ative and simultaneous methods to the conventional, sequential method using an affine transformation model. We show that jointly estimating image intensities and parametric transformations gives superior results with respect to reconstruction fidelity and regis- tration accuracy. Also, we incorporate a non-rigid B-spline transformation model into our simultaneous method. The results demonstrate a visually plausible recovery of the deformation with preservation of the reconstruction fidelity.
机译:数字乳房断层合成(DBT)通过重建乳房的伪3D图像,可以深入了解正常的纤维腺组织和异常病变。在这方面,DBT通过减少由乳腺组织重叠引起的混杂效应,克服了传统X射线乳腺摄影的主要限制。在乳腺癌筛查或诊断的背景下,放射科医生对检测可能表示恶性疾病的变化感兴趣。为了帮助实现此任务的自动化,需要注册图像以建立时间点之间的空间对应关系。典型地,诸如MRI或CT的图像首先被重建然后被配准。如果使用完整的数据集进行重构,则此方法可能有效。但是,对于不适定的,有限角度的问题(例如DBT),估计变形的复杂性在于与重建相关的大量伪像,从而导致套准的严重不准确。本文提出了一个数学框架,将两个任务结合在一起,共同估算图像强度和变换参数。在此框架下,我们比较了迭代方法和同时方法,两者都通过将一对时间量的重建与它们的配准相结合来解决比较DBT数据的问题。我们使用各种计算数字体模,未压缩的乳房MR图像以及体内DBT模拟来评估我们的方法。首先,我们将仿射变换模型的迭代和同时方法与传统的顺序方法进行比较。我们表明,联合估计图像强度和参数变换可在重建保真度和对位精度方面提供出色的结果。此外,我们将非刚性B样条曲线转换模型并入我们的同时方法中。结果表明在视觉上看似合理的变形恢复并保持了重建的保真度。

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