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berkanlafci · Aug 21, 2023 · 373f60e · 373f60e
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diff --git a/docs/_sources/_documentation/arrays.rst.txt b/docs/_sources/_documentation/arrays.rst.txt
@@ -3,7 +3,7 @@
 Arrays
 =================
 
-Transducer arrays used in data acquisitions. The files in "arrays" folder contain the position of individual transducer elements that are needed for image reconstruction.
+Transducer arrays used in data acquisitions are explained in this section. The files in "arrays" folder contain the position of individual transducer elements that are needed for image reconstruction.
 
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 Semi Circle

diff --git a/docs/_sources/_documentation/benchmarks.rst.txt b/docs/_sources/_documentation/benchmarks.rst.txt
@@ -3,7 +3,7 @@
 Benchmarks
 ===================================================
 
-We define 18 experiments based on 3 tasks (sparse reconstructions, limited view corrections and segmentation). Sparse sampling and limited view corrections are grouped under image translation task.
+We define 44 experiments based on 3 tasks (sparse reconstructions, limited view corrections and segmentation). Sparse sampling and limited view corrections are grouped under image translation task.
 
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 Image Translation

diff --git a/docs/_sources/_documentation/datasets.rst.txt b/docs/_sources/_documentation/datasets.rst.txt
@@ -2,15 +2,15 @@
 =================
 Datasets
 =================
-Datasets will be available in `ETH Zurich Research Collection <https://www.research-collection.ethz.ch/handle/20.500.11850/551512>`_ upon paper acceptance.
+Datasets are available in `ETH Zurich Research Collection <https://www.research-collection.ethz.ch/handle/20.500.11850/551512>`_.
 
 We present three datasets (two experimental, one simulated) where each has several subcategories for the purpose of tackling different challenges present in the domain. Raw signal acquisition data that is used to reconstruct all images are also provided with the datasets.
 
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 Multispectral Forearm Dataset
 ------------------------------------------------------------
 
-Multispectral forearm dataset (MSFD) is collected using multisegment array from nine volunteers at six different wavelengths (700, 730, 760, 780, 800, 850 nm) for both arms. Selected wavelengths are particularly aimed for spectral decomposition aiming to separate oxy- and deoxy-hemoglobin. All wavelengths are acquired consecutively, yielding almost identical scene being captured for a given slice across different wavelengths with slight displacement errors. For each of the mentioned category 1,400 slices are captured, creating a sum of 9 x 6 x 2 x 1400 = 151 200 unique signal matrices.
+Multispectral forearm dataset (MSFD) is collected using multisegment array from nine volunteers at six different wavelengths (700, 730, 760, 780, 800, 850 nm) for both arms. Selected wavelengths are particularly aimed for spectral decomposition to separate oxy- and deoxy-hemoglobin. All wavelengths are acquired consecutively, yielding almost identical scene being captured for a given slice across different wavelengths with slight displacement errors. For each of the mentioned category 1,400 slices are captured, creating a sum of 9 x 6 x 2 x 1400 = 151 200 unique signal matrices.
 
 ------------------------------------------------------------
 Single Wavelength Forearm Dataset

diff --git a/docs/_sources/_documentation/reconstruction.rst.txt b/docs/_sources/_documentation/reconstruction.rst.txt
@@ -9,4 +9,4 @@ Backprojection algorithm is used to reconstruct images presented in OADAT.
 Backprojection
 ------------------------------------------------------------
 
-We use backprojection algorithm in this study to generate OA images from the acquired signals presented in `pyoat <https://github.com/berkanlafci/pyoat>`_. This algorithm is based on delay and sum beamforming approach. First, a mesh grid is created to represent the imaged field of view. Then, the distance between the points of the mesh grid and transducer elements are calculated based on the known locations of the array elements. Time of flight is obtained through dividing distance by the SoS values that are assigned based on the temperature of the imaging medium and tissue properties. The clinical and simulated data are reconstructed with SoS of 1510 m/s in this study as the simulations and the experiments were done at the corresponding imaging medium temperature.
+We use backprojection algorithm in this study to generate OA images from the acquired signals presented in `pyoat <https://github.com/berkanlafci/pyoat>`_. This algorithm is based on delay and sum beamforming approach. First, a mesh grid is created to represent the imaged field of view. Then, the distance between the points of the mesh grid and transducer elements are calculated based on the known locations of the array elements. Time of flight is obtained through dividing distance by the speed of sound values that are assigned based on the temperature of the imaging medium and tissue properties. The clinical and simulated data are reconstructed with speed of sound of 1510 m/s in this study as the simulations and the experiments were done at the corresponding imaging medium temperature.