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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) <a href="https://doi.org/10.1117/1.JBO.28.6.066002" rel="nofollow" target="_blank">https://doi.org/10.1117/1.JBO.28.6.066002</a> <hr> 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 hemispherical 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 (<a href="https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Bi-Rads" rel="nofollow" target="_blank">BI-RAD&#174;</a>): <ul> <li>A: Breast is almost entirely fatty; </li> <li>B: Breast has scattered areas of fibroglandular density; </li> <li>C: Breast is heterogeneously dense; </li> <li>D: Breast is extremely dense. </li> </ul> Each NBP set consists of <ol> <li>A tissue label map (anatomical NBP);</li> <li>Functional properties and chromophore concentrations maps (functional NBPs);</li> <li>Optical properties maps (optical absorption and scattering coefficients, scattering anisotropy, and refractive indexes) at multiple illumination wavelengths (optical NBPs);</li> <li>Acoustic properties (speed of sound, density, and acoustic attenuation) maps (acoustic NBPs);</li> <li>Simulated multi-wavelength optical fluence distributions;</li> <li>Simulated multi-wavelength induced initial pressure distributions;</li> <li>Simulated multi-wavelength acoustic measurements.</li> </ol> The tissue label map (anatomical NBP) was created by use of <a href="https:GITHUB_CODE_ADDRESS" rel="nofollow" target="_blank">our adaptation</a> of tools from the Virtual Imaging Clinical Trials for Regulatory Evaluation (<a href="https://github.com/DIDSR/VICTRE" rel="nofollow" target="_blank">VICTRE</a>) project at the U.S. Food and Drug Administration (FDA) and <a href="https:GITHUB_CODE_ADDRESS" rel="nofollow" target="_blank">our python library</a> 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. <ul> <li>Background: 0</li> <li>Fat: 1</li> <li>Skin: 2</li> <li>Glandular: 29</li> <li>Nipple: 33</li> <li>Ligament: 88</li> <li>Terminal duct lobular unit (TDLU): 95</li> <li>Duct: 125</li> <li>Artery: 150</li> <li>Vein: 225</li> <li>Peripheral angiogenesis: 190</li> <li>Viable tumor cell: 200</li> <li>Necrotic core: 210</li> </ul> The functional, optical, and acoustic properties maps were produced employing <a href="https:GITHUB_CODE_ADDRESS" rel="nofollow" target="_blank">our python libraries</a> to assign corresponding properties to each breast tissue type. The optical fluence distributions were simulated using the <a href="https://mcx.space/wiki/index.cgi?Home" rel="nofollow" target="_blank">MCX software</a> <a href="https://doi.org/10.1364/OE.17.020178" rel="nofollow" target="_blank">[Fang2009]</a>, <a href="https://doi.org/10.1117/1.JBO.23.1.010504" rel="nofollow" target="_blank">[Yu2018]</a>. The induced initial pressure distributions were calculated via voxelwise multiplication of the optical absorption coefficient distributions and the simulated optical fluence distributions, assuming a Gr&#252;neisen &Gamma;=1 as commonly done as constant for soft tissues. The acoustic measurements were simulated employing the <a href="http://www.k-wave.org/" rel="nofollow" target="_blank">k-wave</a> software <a href="https://doi.org/10.1117/1.3360308" rel="nofollow" target="_blank">[Treeby2010]</a>. 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 <a href="https://doi.org/DOI_NUMBER" rel="nofollow" target="_blank">[Park2023]</a>. The file naming convention of files in this dataset is {breast type}{seed number}{lesion presence}_{contained data}.mat, where: <ul> <li>Breast type: A, B, C, or D;</li> <li>Seed number, i.e., a number randomly generated when producing each tissue label map;</li> <li>Lesion presence: absent (healthy, h) or present (l);</li> <li>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).</li> </ul> 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. <ol>  <li>{breast type}{seed number}{lesion presence}_label.mat</li> <ul> <li>Data</li> <ul> <li>label: 1360 x 1360 x 680 unit8 tissue label map that includes healthy tissues, viable tumor cell region, and necrotic core</li> </ul> <li>Metadata</li> <ul> <li>origin: 3 x 1 float32 values that specify x, y, and z coordinates of origin, (-85, -85, -85) mm</li> <li>voxel_size: float32 value that specifies voxel size, 0.125 mm</li> <li>tissue_type_label: 13 x 2 cell that specifies tissue type names and the corresponding uint8 values in label</li> <li>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</li> <li>lesion_presence: Lesion presence, h: Lesion-absent numerical breast phantom (healthy) or l: Lesion-present numerical breast phantom</li> <li>lesion_loc: 4 x 3 uint16 values that specify x, y, and z locations (voxel) of four lesions in the first to fourth rows, respectively</li> <li>unit: 3 x 2 cell that specifies units of origin, voxel_size, and lesion_loc</li> </ul> </ul>  <li>{breast type}{seed number}{lesion presence}_func.mat</li> <ul> <li>Data</li> <ul> <li>c_thbb: float32 value of total hemoglobin concentration (&micro;M) of blood</li> <li>s: 1360 x 1360 x 680 float32 oxygen saturation map (unitless)</li> <li>f_b: 1360 x 1360 x 680 float32 blood volume fraction map (unitless)</li> <li>f_w: 1360 x 1360 x 680 float32 water volume fraction map (unitless)</li> <li>f_f: 1360 x 1360 x 680 float32 fat volume fraction map (unitless)</li> <li>f_m: 1360 x 1360 x 680 float32 melanosome volume fraction map (unitless)</li> </ul> <li>Metadata</li> <ul> <li>origin: 3 x 1 float32 values that specify x, y, and z coordinates of origin, (-85, -85, -85) mm</li> <li>voxel_size: float32 value that specifies voxel size, 0.125 mm</li> <li>unit: 8 x 2 cell that specifies units of origin, voxel_size, c_thbb, s, f_b, f_w, f_f, and f_m</li> </ul> </ul>  <li>{breast type}{seed number}{lesion presence}_opt.mat</li> <ul> <li>Data</li> <ul> <li>mu_a_w{757, 800, 850}: 1360 x 1360 x 680 float32 optical absorption coefficient maps (mm-1) at illumination wavelengths of 757 nm, 800 nm, and 850 nm</li> <li>mu_s_w{757, 800, 850}: 1360 x 1360 x 680 float32 optical scattering coefficient maps (mm-1) at illumination wavelengths of 757 nm, 800 nm, and 850 nm</li> <li>g: 1360 x 1360 x 680 float32 optical scattering anisotropy map (unitless)</li> <li>n: 1360 x 1360 x 680 float32 refractive index map (unitless)</li> </ul> <li>Metadata</li> <ul> <li>origin: 3 x 1 float32 values that specify x, y, and z coordinates of origin, (-85, -85, -85) mm</li> <li>voxel_size: float32 value that specifies voxel size, 0.125 mm</li> <li>unit: 7 x 2 cell that specifies units of origin, voxel_size, wavelength, mu_a, mu_s, g, and n</li> </ul> </ul>  <li>{breast type}{seed number}{lesion presence}_acou.mat</li> <ul> <li>Data</li> <ul> <li>sound_speed: 1360 x 1360 x 680 float32 sound speed map (mm/&micro;s)</li> <li>density: 1360 x 1360 x 680 float32 density map (g/mm3)</li> <li>alpha_coeff: 1360 x 1360 x 680 float32 acoustic attenuation coefficient map (dB/MHzymm)</li> <li>alpha_power: float32 acoustic attenuation power-law exponent value (unitless)</li> </ul> <li>Metadata</li> <ul> <li>origin: 3 x 1 float32 values that specify x, y, and z coordinates of origin, (-85, -85, -85) mm</li> <li>voxel_size: float32 value that specifies voxel size, 0.125 mm</li> <li>unit: 6 x 2 cell that specifies units of origin, voxel_size, sound_speed, density, alpha_coeff, and alpha_power</li> </ul> </ul>  <li>{breast type}{seed number}{lesion presence}_phi.mat</li> <ul> <li>Data</li> <ul> <li>phi_w{757, 800, 850}: 680 x 680 x 340 float32 optical fluence distributions (mJ/mm2) simulated based on mu_a_w{757, 800, 850}, mu_s_w{757, 800, 850}, g, and n</li> </ul> <li>Metadata</li> <ul> <li>origin: 3 x 1 float32 values that specify x, y, and z coordinates of origin, (-85, -85, -85) mm</li> <li>voxel_size: float32 value that specifies voxel size, 0.25 mm</li> <li>unit: 4 x 2 cell that specifies units of origin, voxel_size, wavelength, and phi</li> </ul> </ul>  <li>{breast type}{seed number}{lesion presence}_p0.mat</li> <ul> <li>Data</li> <ul> <li>p0_w{757, 800, 850}: 680 x 680 x 340 float32 initial pressure distributions (mJ/mm3) calculated via voxelwise multiplication of mu_a_w{757, 800, 850} and phi_w{757, 800, 850} (&Gamma;=1)</li> </ul> <li>Metadata</li> <ul> <li>origin: 3 x 1 float32 values that specify x, y, and z coordinates of origin, (-85, -85, -85) mm</li> <li>voxel_size: float32 value that specifies voxel size, 0.25 mm</li> <li>unit: 4 x 2 cell that specifies units of origin, voxel_size, wavelength, and p0</li> </ul> </ul>  <li>{breast type}{seed number}{lesion presence}_p.mat</li> <ul> <li>Data</li> <ul> <li>p_w{757, 800, 850}: 51472 x 3710 float32 acoustic measurements (mJ/mm3) simulated based on sound_speed, density, alpha_coeff, alpha_power, and p0_w{757, 800, 850}</li> </ul> <li>Metadata</li> <ul> <li>sampling_frequency: float32 value that specifies sampling frequency, 20 MHz</li> <li>transducer_loc: 3 x 51472 float32 values that specify x, y, and z locations (mm) of virtual transducer elements in the image domain of sound_speed, density, alpha_coeff, alpha_power, and p0_w{757, 800, 850}</li> <li>unit: 4 x 2 cell that specifies units of sampling_frequency, transducer_loc, wavelength, and p</li> </ul> </ul> </ol> These data were saved as MATLAB binary files version 7.3 (extension .mat). They can be imported in MATLAB using the load function. The code of 1) our modified version of the VICTRE project by the FDA and 2) python libraries to introduce blood vasculature under the skin layer and to assign corresponding properties to each breast tissue type is available from the GitHub repository: <a href="https://github.com/comp-imaging-sci/breast-phantom-oat" rel="nofollow" target="_blank">comp-imaging-sci/breast-phantom-oat</a>; <a href="https://github.com/comp-imaging-sci/soa-nbp" rel="nofollow" target="_blank">comp-imaging-sci/soa-nbp.

应用场景：

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