fix typos

including incorrect `proj` array shapes described in docstrings

Author: rozenn1

Common/CUDA/GD_TV.cu

@@ -307,12 +307,12 @@ do { \
             size_t mem_free=mem_GPU_global-mem_auxiliary;
 
             splits=(unsigned int)(ceil(((float)(3*mem_size_image)/(float)(deviceCount))/mem_free));
-            // Now, there is an overhead here, as each splits should have 2 slices more, to accoutn for overlap of images.
+            // Now, there is an overhead here, as each splits should have 2 slices more, to account for overlap of images.
             // lets make sure these 2 slices fit, if they do not, add 1 to splits.
             slices_per_split=(image_size[2]+deviceCount*splits-1)/(deviceCount*splits);
             mem_img_each_GPU=(mem_slice_image*(slices_per_split+buffer_length*2));
 
-            // if the new stuff does not fit in the GPU, it measn we are in the edge case where adding that extra slice will overflow memory
+            // if the new stuff does not fit in the GPU, it means we are in the edge case where adding that extra slice will overflow memory
             if (mem_GPU_global< 3*mem_img_each_GPU+mem_auxiliary){
                 // one more split should do the job, as its an edge case.
                 splits++;

MATLAB/Demos/d16_2Dtomography.m

@@ -62,7 +62,7 @@
 angles=linspace(0,2*pi,100);
 phantom=single(phantom('Modified Shepp-Logan',geo.nVoxel(1)));
 projections=Ax(phantom,geo,angles);
-%% recosntruct
+%% reconstruct
 
 imgOSSART=OS_SART(projections,geo,angles,40);
 imgASDPOCS=ASD_POCS(projections,geo,angles,40);
@@ -107,7 +107,7 @@
 phantom=single(phantom('Modified Shepp-Logan',geo.nVoxel(1)));
 % phantom=cat(3,phantom,phantom);
 projections=Ax(phantom,geo,angles,'interpolated');
-%% recosntruct
+%% reconstruct
 
 imgOSSART=OS_SART(projections,geo,angles,40);
 % in 2D, the TV norm minimization happens in CPU, this causes the

MATLAB/Demos/d24_Algorithms06.m

@@ -2,7 +2,7 @@
 %
 %
 %  This demo presents the TIGRE capability of using image priors for
-%  CT recosntruction. The most known algorithm for this is PICCS
+%  CT reconstruction. The most known algorithm for this is PICCS
 % 
 %--------------------------------------------------------------------------
 %--------------------------------------------------------------------------

Python/demos/d16_2Dtomography.py

@@ -64,7 +64,7 @@
 head = sample_loader.load_head_phantom(geo.nVoxel)
 projections = tigre.Ax(head, geo, angles)
 tigre.plotSinogram(projections, 0)
-#%% recosntruct
+#%% reconstruct
 
 imgOSSART = algs.ossart(projections, geo, angles, 40)
 imgCGLS = algs.cgls(projections, geo, angles, 40)
@@ -105,7 +105,7 @@
 angles = np.linspace(0, 2 * np.pi, 100)
 head = sample_loader.load_head_phantom(geo.nVoxel)
 projections = tigre.Ax(head, geo, angles)
-#%% recosntruct
+#%% reconstruct
 
 imgOSSART = algs.ossart(projections, geo, angles, 40)
 imgCGLS = algs.cgls(projections, geo, angles, 40)

Python/tigre/algorithms/ista_algorithms.py

@@ -19,7 +19,7 @@ class FISTA(IterativeReconAlg):
     Parameters
     ----------
     :param proj: (np.ndarray, dtype=np.float32)
-    Input data, shape = (geo.nDector, nangles)
+    Input data, shape = (nangles, geo.nDetector)
 
     :param geo: (tigre.geometry)
     Geometry of detector and image (see examples/Demo code)

Python/tigre/algorithms/iterative_recon_alg.py

@@ -36,7 +36,7 @@ class IterativeReconAlg(object):
     Parameters
     ----------
     :param proj: (np.ndarray, dtype=np.float32)
-    Input data, shape = (geo.nDector, nangles)
+    Input data, shape = (nangles, geo.nDetector)
 
     :param geo: (tigre.geometry)
     Geometry of detector and image (see examples/Demo code)

Python/tigre/algorithms/pocs_algorithms.py

@@ -20,7 +20,7 @@ class initASD_POCS(IterativeReconAlg):
     Parameters
     ----------
     :param proj: (np.ndarray, dtype=np.float32)
-    Input data, shape = (geo.nDector, nangles)
+    Input data, shape = (nangles, geo.nDetector)
 
     :param geo: (tigre.geometry)
     Geometry of detector and image (see examples/Demo code)
@@ -192,7 +192,7 @@ class initPICCS(IterativeReconAlg):
     Parameters
     ----------
     :param proj: (np.ndarray, dtype=np.float32)
-    Input data, shape = (geo.nDector, nangles)
+    Input data, shape = (nangles, geo.nDetector)
 
     :param geo: (tigre.geometry)
     Geometry of detector and image (see examples/Demo code)
@@ -439,7 +439,7 @@ class initPCSD(IterativeReconAlg):
     Parameters
     ----------
     :param proj: (np.ndarray, dtype=np.float32)
-    Input data, shape = (geo.nDector, nangles)
+    Input data, shape = (nangles, geo.nDetector)
 
     :param geo: (tigre.geometry)
     Geometry of detector and image (see examples/Demo code)