用学习方法实现半球形探测的光声成像快速重建,精度高且泛化强。
Learning a Filtered Backprojection Reconstruction Method for Photoacoustic Computed Tomography with Hemispherical Measurement Geometries
- 通过学习逼近半球采集数据的滤波反投影算子
- 在3D乳腺模型上重建误差低于5%,实验数据验证有效
- 适合临床半球探测场景,对数据差异鲁棒
在某些三维光声计算机断层扫描(PACT)应用中,如活体乳腺成像,采用包裹物体凸包的半球形测量孔径进行数据采集,此类数据称为半扫数据。尽管已有研究证明半扫数据可唯一且稳定地重建目标,但尚未有适用于该数据的闭式重建公式。为此,本文提出一种半解析的滤波反投影(FBP)方法,即半扫FBP方法。由于该方法中滤波操作的显式形式未知,本文采用基于学习的方法近似其滤波器。通过使用包含多个数值乳腺体模和物理数据采集模型的虚拟成像研究系统评估该方法,并进一步应用于活体乳腺PACT实验数据。结果表明,半扫FBP方法能准确重构三维图像。尤为重要的是,由于所求逆映射是适定的,该方法即使应用于与训练数据差异较大的数据仍保持高精度。
原文摘要 · Abstract (English)
In certain three-dimensional (3D) applications of photoacoustic computed tomography (PACT), including \textit{in vivo} breast imaging, hemispherical measurement apertures that enclose the object within their convex hull are employed for data acquisition. Data acquired with such measurement geometries are referred to as \textit{half-scan} data, as only half of a complete spherical measurement aperture is employed. Although previous studies have demonstrated that half-scan data can uniquely and stably reconstruct the sought-after object, no closed-form reconstruction formula for use with half-scan data has been reported. To address this, a semi-analytic reconstruction method in the form of filtered backprojection (FBP), referred to as the half-scan FBP method, is developed in this work. Because the explicit form of the filtering operation in the half-scan FBP method is not currently known, a learning-based method is proposed to approximate it. The proposed method is systematically investigated by use of virtual imaging studies of 3D breast PACT that employ ensembles of numerical breast phantoms and a physics-based model of the data acquisition process. The method is subsequently applied to experimental data acquired in an \textit{in vivo} breast PACT study. The results confirm that the half-scan FBP method can accurately reconstruct 3D images from half-scan data. Importantly, because the sought-after inverse mapping is well-posed, the reconstruction method remains accurate even when applied to data that differ considerably from those employed to learn the filtering operation.
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