Merge pull request #1 from samuelstjean/add_invivo

Add invivo datasets

README.md

@@ -1,22 +1,67 @@
-# NLSAM datasets repository
+NLSAM datasets repository
+-----------------
 
-Synthetic datasets used in the NLSAM paper, for which the main repo can be found [here](https://github.com/samuelstjean/nlsam/).
+Synthetic and *in vivo* datasets used in the NLSAM paper, for which the main repo can be found [here](https://github.com/samuelstjean/nlsam/).
 The synthetic data is based on an earlier version of [phantomas](https://github.com/ecaruyer/phantomas).
-The masks folders contains everything needed for running a tractometer comparison in the proper folders.
 
-The data can be downloaded as zip files on the  [releases](https://github.com/samuelstjean/nlsam_data/releases) page or cloned locally with 
+The data can be downloaded as zip files on the [releases](https://github.com/samuelstjean/nlsam_data/releases) page or cloned locally with
+~~~
+git clone https://github.com/samuelstjean/nlsam_data.git
+~~~
+
+Acknowledgments
+-----------------
+
+If you use the datasets provided therein, please make sure that you quote the following references in any publications:
+
+~~~
+St-Jean, S., Coupé, P., & Descoteaux, M.,
+Non Local Spatial and Angular Matching: Enabling higher spatial resolution diffusion MRI datasets through adaptive denoising.
+Medical Image Analysis, 32(2016), 115–130, 2016.
+~~~
+
+Please also credit (if applicable) the *in vivo* acquisition in the acknowledgments section of relevant papers as
 ~~~
-git clone https://github.com/samuelstjean/nlsam_data.git 
+Datasets were provided (in part) by the Centre d'imagerie moléculaire de Sherbrooke (CIMS)
+and the Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, Québec, Canada.
 ~~~
 
-The phantomas b1000 and phantomas b3000 folders contains the raw datasets that were used. The naming convention goes as follow
+Synthetic data description
+-----------------
 
-+ hardi-scheme contains the bvals/bvecs used by the phantom, which is the same 64 gradient directions for both diffusion weighting.
-+ dwis.nii.gz is the noiseless ground-truth data to which noise was added afterward.
-+ SNR-(10, 15, 20 or 30) is the SNR of the dataset computed as SNR = mean(b0) / sigma, with mean(b0) computed inside white matter (see wm.nii.gz inside the masks folder) and sigma the noise standard deviation.
-+ coils-(1, 4, 8 or 12) defines the noise distribution as outlined in Eq. 6 of the paper.
-+ var-3 means that this dataset has a varying noise profile, which is SNR at the edges and SNR/3 near the center in a linear scale.
+The phantomas b1000 and phantomas b3000 folders contains the raw datasets that were used.
+The masks folders contains everything needed for running a tractometer comparison in the proper folders.
+The naming convention goes as follow
+
+- hardi-scheme contains the bvals/bvecs used by the phantom, which is the same 64 gradient directions for both diffusion weighting.
+- dwis.nii.gz is the noiseless ground-truth data to which noise was added afterward.
+- SNR-(10, 15, 20 or 30) is the SNR of the dataset computed as SNR = mean(b0) / sigma,
+    with mean(b0) computed inside white matter (see wm.nii.gz inside the masks folder) and sigma the noise standard deviation.
+- coils-(1, 4, 8 or 12) defines the noise distribution as outlined in Eq. 6 of the paper.
+- var-3 means that this dataset has a varying noise profile, which is SNR at the edges and SNR/3 near the center in a linear scale.
 
 Note that not all datasets were used in the original paper, but are still provided here for interested users.
 
-If you would like access to the in-vivo diffusion scan, feel free to contact me.
+*In vivo* data description
+-----------------
+
+Two diffusion weighted scans and a T1 weighted scan of a healthy volunteer were acquired on a 3T Philips scanner.
+A SENSE acceleration factor R = 2 (producing spatially varying Rician noise)
+was used with a gradient strength of 45 mT/m and a 32 channels head coil. The following acquisition parameters were used :
+
+For the high resolution dataset :
+
+- 40 gradient directions at b = 1000 s/mm² + 1 b0 image at 1.2 mm voxel size
+- TR/TE = 18.9 s / 104 ms
+- Total acquisition time : 13 mins
+
+For the standard resolution dataset :
+
+- 64 gradient directions at b = 1000 s/mm² + 1 b0 image at 1.8 mm voxel size
+- TR/TE = 11.1 s / 63 ms
+- Total acquisition time : 12 mins
+
+License
+-----------------
+
+All content is available under the [Creative Common Attribution license](https://creativecommons.org/licenses/by/4.0/), see [LICENSE](LICENSE) for more information.

invivo/encoding_1_2.bval

@@ -0,0 +1 @@
+1000 1000 999 1000 1000 999 1000 999 1000 999 999 999 1000 1000 1000 999 999 1000 999 1000 999 1000 1000 999 999 1000 1000 999 1000 999 1000 1000 999 1000 999 1000 1000 1000 1000 1000 0

invivo/encoding_1_2.bvec

@@ -0,0 +1,3 @@
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+-0.03018720 0.28341700 -0.76424500 0.86365800 -0.81365700 0.14531200 0.82400400 -0.46313700 0.53361100 -0.15809700 -0.87300500 0.34588600 0.98546100 0.30786900 0.02483820 -0.40217800 0.53110800 0.25516300 -0.53088300 -0.63898900 -0.51277600 0.63461400 -0.96578600 0.40122600 -0.06981900 -0.04975230 0.61191400 -0.95368700 0.83422600 0.12196900 -0.75207900 -0.27412900 0.31883300 -0.62144400 0.64331600 -0.44499400 -0.24298000 -0.72243300 -0.14794700 0.88602900 0.00000000
+0.99954400 0.56355300 0.34797900 0.49111500 0.33418900 0.42287100 0.47331800 0.75975700 0.42775300 0.67196500 0.45429500 0.17299000 0.16153200 0.93422000 0.02627170 0.91044700 0.70743800 0.73551700 0.46938000 0.06054830 0.65327200 0.77198800 0.22556800 0.07049420 0.90889900 0.35577000 0.68118000 0.04703250 0.30984400 0.85092400 0.62829300 0.26846700 0.91505300 0.75963600 0.35545400 0.32765600 0.90964800 -0.01303420 0.65656300 0.08223400 0.00000000

invivo/encoding_1_8.bval

@@ -0,0 +1 @@
+1000 1000 1000 999 1000 1000 1000 999 1000 999 1000 1000 1000 1000 999 999 1000 1000 999 1000 1000 1000 1000 1000 999 999 1000 999 1000 1000 1000 999 1000 1000 999 1000 1000 999 1000 1000 1000 1000 999 1000 1000 1000 1000 999 1000 1000 1000 1000 1000 1000 1000 999 1000 1000 1000 1000 999 1000 1000 1000 0

invivo/encoding_1_8.bvec

@@ -0,0 +1,3 @@
+-0.00000000 0.69006900 0.78984000 -0.16814100 -0.47888800 -0.78291900 0.38829900 -0.44418100 -0.59205800 0.80018300 0.05635000 -0.96935700 0.20936900 -0.02603990 0.99406400 0.11424000 0.50321000 -0.55411900 -0.88017100 -0.60302000 0.47961900 0.23120000 -0.01603000 -0.80207900 -0.32908000 0.78923400 -0.33325900 0.53582100 -0.43198800 0.32306000 -0.49307000 -0.96678300 -0.27731900 0.34132000 0.67441200 0.91006700 0.25491000 0.72799100 -0.83511900 -0.28829900 -0.13805000 0.94295600 -0.71374100 0.14588000 0.27794900 0.29489900 0.44128000 -0.22826100 0.06716980 -0.93170000 -0.06579990 -0.60567900 0.58138900 0.58300700 -0.70424900 0.72615000 -0.83396700 -0.35570900 -0.61705900 0.91325700 0.01860000 0.57151900 -0.20343000 0.87431000 0.00000000
+-0.03018720 0.56288200 -0.54131100 0.94972300 -0.78465900 0.26985100 0.91200300 -0.29814100 0.74828600 -0.04361700 -0.76145100 -0.24564900 -0.97118700 0.57134500 -0.09320700 -0.49142900 0.52539800 0.28732600 -0.33172000 -0.79045400 -0.39384000 0.71337500 -0.94084400 0.51833100 0.00460668 0.27206600 0.58912600 -0.81653900 0.89782800 0.09802980 -0.56884000 -0.05001950 0.31580100 -0.64715300 0.71586900 -0.23736400 -0.22033200 -0.68558400 -0.03731380 -0.94048500 -0.29619500 0.09786830 -0.58052500 0.91860000 0.42072200 -0.85882400 0.78749000 -0.80259500 0.99671700 0.24473900 0.80724000 0.54919800 0.22310200 -0.64661100 -0.32113000 -0.36005500 -0.54671600 0.79937500 -0.02031610 -0.40674300 0.27006400 -0.09612440 -0.57258500 0.42290200 0.00000000
+0.99954400 0.45493800 0.28833100 0.26411100 0.39367100 0.56055100 0.13219100 0.84487600 0.29922400 0.59816800 0.64576800 -0.00171626 0.11384400 0.82029700 0.05612210 0.86339200 0.68610300 0.78127800 0.33950100 0.10746700 0.78412700 0.66154600 0.33846200 0.29665200 0.94429100 0.55053600 0.73611800 0.21484900 0.08539150 0.94128800 0.65825800 0.25065500 0.90739400 0.68168500 0.18083300 0.33975800 0.94152800 -0.00196671 0.54880300 0.17992000 0.94509800 0.31820800 0.39187500 0.36727700 0.86356100 0.41887400 0.43026800 0.55112500 0.04520860 0.26840000 0.58654400 0.57579000 0.78244000 0.49193200 0.63317400 0.58571800 0.07483990 0.48422100 0.78665400 0.02282190 0.96266300 0.81493900 0.79420600 0.23819500 0.00000000