3D optoacoustic numerical breast phantoms and simulated OAT measurement data (natural shape, 4 lesions)

Companion dataset of the manuscript: Seonyeong Park, Umberto Villa, Fu Li, Refik Mert Cam, Alexander A. Oraevsky, Mark A. Anastasio, \"Stochastic three-dimensional numerical phantoms to enable computational studies in quantitative optoacoustic computed tomography of breast cancer,\" J. Biomed. Opt. 28(6) 066002 (20 June 2023) https://doi.org/10.1117/1.JBO.28.6.066002 This dataset contains 40 sets of three-dimensional (3D) numerical breast phantoms (NBPs) for use in virtual imaging trials (VITs) of optoacoustic tomography (OAT) and the corresponding simulated multi-wavelength optical fluence distributions, induced initial pressure distributions, and OAT measurement data. The NBPs are in natural shapes, and four different-sized lesions, that are composed solely of a viable tumor cell region, were inserted into each of 40 NBPs. The NBPs correspond to one of the following four breast density types defined in Breast Imaging Reporting and Data System (BI-RAD®): A: Breast is almost entirely fatty; B: Breast has scattered areas of fibroglandular density; C: Breast is heterogeneously dense; D: Breast is extremely dense. Each NBP set consists of A tissue label map (anatomical NBP); Functional properties and chromophore concentrations maps (functional NBPs); Optical properties maps (optical absorption and scattering coefficients, scattering anisotropy, and refractive indexes) at multiple illumination wavelengths (optical NBPs); Acoustic properties (speed of sound, density, and acoustic attenuation) maps (acoustic NBPs); Simulated multi-wavelength optical fluence distributions; Simulated multi-wavelength induced initial pressure distributions; Simulated multi-wavelength acoustic measurements. The tissue label map (anatomical NBP) was created by use of our adaptation of tools from the Virtual Imaging Clinical Trials for Regulatory Evaluation (VICTRE) project at the U.S. Food and Drug Administration (FDA) and our python library to introduce blood vasculature under the skin layer. In each NBP, four different-sized (<10 mm in diameter) numerical lesion phantoms (NLPs) were inserted at locations randomly selected from those predicted by the VICTRE tools based on the duct and terminal duct lobular unit (TDLU) structures that are well-known sites for lesion formation. The considered tissue types and their unsigned 8-bit integer (uint8) labels are as belows. Background: 0 Fat: 1 Skin: 2 Glandular: 29 Nipple: 33 Ligament: 88 Terminal duct lobular unit (TDLU): 95 Duct: 125 Artery: 150 Vein: 225 Peripheral angiogenesis: 190 Viable tumor cell: 200 Necrotic core: 210 The functional, optical, and acoustic properties maps were produced employing our python libraries to assign corresponding properties to each breast tissue type. The optical fluence distributions were simulated using the MCX software [Fang2009], [Yu2018]. The induced initial pressure distributions were calculated via voxelwise multiplication of the optical absorption coefficient distributions and the simulated optical fluence distributions, assuming a Grüneisen Γ=1 as commonly done as constant for soft tissues. The acoustic measurements were simulated employing the k-wave software [Treeby2010]. Further details on 1) modifications and adaptations of the VICTRE NBPs for use in VITs of OAT; 2) virtual OAT imaging system and data acquisition are in the accompanying paper [Park2023]. The file naming convention of files in this dataset is {breast type}{seed number}{lesion presence}_{contained data}.mat, where: Breast type: A, B, C, or D; Seed number, i.e., a number randomly generated when producing each tissue label map; Lesion presence: absent (healthy, h) or present (l); Contained data: tissue label map (label), functional properties and chromophore concentrations maps (func), optical properties maps (opt), acoustic properties maps(acou), simulated optical fluence distributions (phi), simulated induced initial pressure distributions (p0), or simulated acoustic measurements (p). For example, the file name of a tissue label map of the type A breast with no lesion inserted (healthy breast) that was created using the seed number 123456 is A12345678h_label.mat. The actual data and metadata contained in each file are as below. {breast type}{seed number}{lesion presence}_label.mat Data label: 1360 x 1360 x 680 unit8 tissue label map that includes healthy tissues, viable tumor cell region, and necrotic core Metadata origin: 3 x 1 float32 values that specify x, y, and z coordinates of origin, (-85, -85, -85) mm voxel_size: float32 value that specifies voxel size, 0.125 mm tissue_type_label: 13 x 2 cell that specifies tissue type names and the corresponding uint8 values in label breast_type: Breast type, A: Breast is almost entirely fatty; B: Breast has scattered areas of fibroglandular density; C: Breast is heterogeneously dense; or D: Breast is extremely dense lesion_presence: Lesion presence, h: Lesion-absent numerical breast phantom (healthy) or l: Lesion-present...

本数据集对应下述学术论文：Seonyeong Park、Umberto Villa、Fu Li、Refik Mert Cam、Alexander A. Oraevsky、Mark A. Anastasio，题为《用于乳腺癌定量光声计算机断层扫描计算研究的随机三维数值乳腺体模》，发表于J. Biomed. Opt. 28(6) 066002（2023年6月20日），DOI: 10.1117/1.JBO.28.6.066002。

本数据集包含40组三维（3D）数值乳腺体模（numerical breast phantoms, NBPs），用于光声层析成像（optacoustic tomography, OAT）的虚拟成像试验（virtual imaging trials, VITs），以及对应的模拟多波长光通量分布、诱导初始压力分布与OAT测量数据。40组NBPs均植入了仅由活性肿瘤细胞区域构成的四种不同尺寸的病变。上述NBPs对应乳腺影像报告和数据系统（Breast Imaging Reporting and Data System, BI-RAD®）定义的四种乳腺密度类型：A：乳腺几乎完全为脂肪组织；B：乳腺存在散在的纤维腺体密度区域；C：乳腺为不均匀致密型；D：乳腺为极致密型。

每组NBPs包含以下数据：1. 组织标签图（解剖型NBPs）；2. 功能特性与生色团浓度分布图（功能型NBPs）；3. 多种照明波长下的光学特性分布图（含光学吸收系数、散射系数、散射各向异性与折射率）（光学型NBPs）；4. 声学特性（声速、密度、声衰减）分布图（声学型NBPs）；5. 模拟多波长光通量分布；6. 模拟多波长诱导初始压力分布；7. 模拟多波长声学测量数据。

解剖型NBPs通过改编美国食品药品监督管理局（U.S. Food and Drug Administration, FDA）监管评估虚拟成像临床试验（Virtual Imaging Clinical Trials for Regulatory Evaluation, VICTRE）项目的工具，并结合自研Python库在皮肤层下构建血管网络而生成。每组NBPs中，四种直径小于10mm的数值病变体模（numerical lesion phantoms, NLPs）被随机植入至由VICTRE工具基于导管和终末导管小叶单位（terminal duct lobular unit, TDLU）结构预测的位点——这些结构是已知的病变好发部位。

本次数据集涉及的组织类型及其无符号8位整数（unsigned 8-bit integer, uint8）标签如下：背景：0；脂肪：1；皮肤：2；腺体：29；乳头：33；韧带：88；终末导管小叶单位（TDLU）：95；导管：125；动脉：150；静脉：225；外周血管生成：190；活性肿瘤细胞：200；坏死核心：210。

功能、光学与声学特性分布图通过自研Python库为每种乳腺组织类型分配对应特性生成。光通量分布使用MCX软件[Fang2009]、[Yu2018]进行模拟。诱导初始压力分布通过将光学吸收系数分布与模拟光通量分布逐体素相乘计算得到，采用软组织通用的格鲁内森常数Γ=1。声学测量数据使用k-wave软件[Treeby2010]模拟。

关于1）适配VICTRE NBPs用于OAT的VITs；2）虚拟OAT成像系统与数据采集的更多细节，请参见随刊论文[Park2023]。

本数据集的文件命名格式为{乳腺类型}{随机种子号}{病变标识}_{包含数据类型}.mat，各部分含义如下：
- 乳腺类型：A、B、C或D；
- 随机种子号：生成每张组织标签图时随机生成的编号；
- 病变标识：无病变（健康，h）或有病变（l）；
- 包含数据类型：组织标签图（label）、功能特性与生色团浓度分布图（func）、光学特性分布图（opt）、声学特性分布图（acou）、模拟光通量分布（phi）、模拟诱导初始压力分布（p0）或模拟声学测量数据（p）。

示例：使用种子编号12345678生成的无病变健康A型乳腺的组织标签图文件名为A12345678h_label.mat。

每个文件包含的实际数据与元数据详情如下：
1. {乳腺类型}{随机种子号}{病变标识}_label.mat：
- 数据标签：1360×1360×680的uint8组织标签图，包含健康组织、活性肿瘤细胞区域与坏死核心；
- 元数据：
- origin：3×1的float32数组，指定坐标原点为(-85, -85, -85) mm；
- voxel_size：float32值，指定体素尺寸为0.125 mm；
- tissue_type_label：13×2的元胞数组，指定组织类型名称与对应uint8标签值；
- breast_type：乳腺密度类型，A：几乎完全脂肪型；B：散在纤维腺体型；C：不均匀致密型；D：极致密型；
- lesion_presence：病变存在标识，h：无病变健康型数值乳腺体模，l：有病变型数值乳腺体模。