2D numerical mouse phantom for dynamic photoacoustic tomography virtual imaging studies of small animal models
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Companion data set of manuscript: Luke Lozenski, Mark Anastasio, and Umberto Villa. A Memory-Efficient Dynamic Image Reconstruction Method using Neural Fields. Arxiv-2205.05585, 2022  This dataset provides anatomically realistic spatiotemporal maps of optical absorption coefficient and photoacoustically induced pressure distribution for virtual imaging studies of dynamic photoacoustic tomography of small animal models. Physical dimensions of the 2D-plus-time object are 2.9 cm x 2.9 cm and a 5 seconds time horizon (corresponding to a full breathing cycle). The object is rendered on a spatiotemporal Cartesian grid with 400 x 400 pixels and 180 time frames. The anatomical information of the spatiotemporal object is derived from a two-dimensional (2D) cardio-torso s' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Dynamic imaging","Numerical phantoms","Photoacoustic computed tomography","Small animal models"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Companion data set of manuscript: Luke Lozenski, Mark Anastasio, and Umberto Villa. A Memory-Efficient Dynamic Image Reconstruction Method using Neural Fields. Arxiv-2205.05585, 2022  This dataset provides anatomically realistic spatiotemporal maps of optical absorption coefficient and photoacoustically induced pressure distribution for virtual imaging studies of dynamic photoacoustic tomography of small animal models. Physical dimensions of the 2D-plus-time object are 2.9 cm x 2.9 cm and a 5 seconds time horizon (corresponding to a full breathing cycle). The object is rendered on a spatiotemporal Cartesian grid with 400 x 400 pixels and 180 time frames. The anatomical information of the spatiotemporal object is derived from a two-dimensional (2D) cardio-torso s' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Companion data set of manuscript: Luke Lozenski, Mark Anastasio, and Umberto Villa. A Memory-Efficient Dynamic Image Reconstruction Method using Neural Fields. Arxiv-2205.05585, 2022  This dataset provides anatomically realistic spatiotemporal maps of optical absorption coefficient and photoacoustically induced pressure distribution for virtual imaging studies of dynamic photoacoustic tomography of small animal models. Physical dimensions of the 2D-plus-time object are 2.9 cm x 2.9 cm and a 5 seconds time horizon (corresponding to a full breathing cycle). The object is rendered on a spatiotemporal Cartesian grid with 400 x 400 pixels and 180 time frames. The anatomical information of the spatiotemporal object is derived from a two-dimensional (2D) cardio-torso s
Dynamic imaging; Numerical phantoms; Photoacoustic computed tomography; Small animal models