Typos found by codespell

BIDS/README.md

@@ -328,6 +328,5 @@ Fields specific to United Imaging Healthcare systems (e.g. uMR 770).
 
 | Field                          | Unit | Comments                 | Defined By |
 |--------------------------------|------|--------------------------|------------|
-| BandwidthPerPixelPhaseEncode   | Hz   | DICOM tag 0019,1028      | B          |      
+| BandwidthPerPixelPhaseEncode   | Hz   | DICOM tag 0019,1028      | B          |
 | ParallelReductionFactorInPlane |      | DICOM tag 0065,100D      | B          |
-

COMPILE.md

@@ -159,4 +159,3 @@ Once the installation is completed, you can revert these changes:
 git config --global --unset-all url.https://github.com/.insteadof
 git config --global --unset-all url.https://.insteadof
 ```
-

CONTRIBUTE.md

@@ -26,4 +26,4 @@ dcm2niix is written in C. Different programmers prefer different styles of inden
 
 ```
 clang-format -i -style="{BasedOnStyle: LLVM, IndentWidth: 4, IndentCaseLabels: false, TabWidth: 4, UseTab: Always, ColumnLimit: 0}" *.cpp *.h
-```
+```

Canon/README.md

@@ -17,7 +17,7 @@ Since the acquisition by Canon, these public tags are no longer populated for im
  (0020,4000) LT [b=1500(0.445,0.000,0.895)]              #  26, 1 ImageComments
 ```
 
-In contrast, when exporting images as enhanced (4D) DICOM, information is stored in public tags and does appear to compensate for phase encode polarity. These coordinates are with respect to the scanner bore, not image space. A Canon classic DICOM DWI image may report: 
+In contrast, when exporting images as enhanced (4D) DICOM, information is stored in public tags and does appear to compensate for phase encode polarity. These coordinates are with respect to the scanner bore, not image space. A Canon classic DICOM DWI image may report:
 
 ```
 (0018,9087) FD 1500                                     #   8, 1 DiffusionBValue
@@ -36,4 +36,4 @@ The [BIDS format](https://bids.neuroimaging.io) can record several sequence prop
 
  - [Toshiba Aquilion](https://www.aliza-dicom-viewer.com/download/datasets).
  - [Toshiba 3T Galan Diffusion Dataset](https://github.com/neurolabusc/dcm_qa_toshiba).
- - [Canon 3T Galan Diffusion Dataset](https://github.com/neurolabusc/dcm_qa_canon).
+ - [Canon 3T Galan Diffusion Dataset](https://github.com/neurolabusc/dcm_qa_canon).

ERRORS.md

@@ -26,4 +26,3 @@ Below is a list of possible return values from running dcm2niix.
 | 7           | Unable to write to output folder (check file permissions)   |
 | 8           | Converted some but not all of the input DICOMs              |
 | 9           | Unable to rename files (result of `dcm2niix -r y ~/in`)     |
-

FILENAMING.md

@@ -38,7 +38,7 @@ In general dcm2niix creates images with 3D dimensions, or 4 dimensions when the
 
  - _cNx.._cNz  where C* refers to the coil name (typically only seen for uncombined data, where a separate image is generated for each antenna)
  - _e1..eN echo number for multi-echo sequences
- - _Eq is commonly seen in [CT scans](https://github.com/neurolabusc/dcm_qa_ct). For example, CT scans of the brain often have many slices closely packed near the brain stem and only a few slices spread far apart near the top of the head. Variable between-slice spacing is rarer in MRI, and if you see this from a MRI sequence you should ensure that [all of the acquired slices have been provided to dcm2niix](https://neurostars.org/t/field-mapping-siemens-scanners-dcm2niix-output-2-bids/2075/7). NIfTI asumes all 2D slices that form a 3D stack are equidistant. Therefore, dcm2niix reslices the input data to generate an equidistant volume.
+ - _Eq is commonly seen in [CT scans](https://github.com/neurolabusc/dcm_qa_ct). For example, CT scans of the brain often have many slices closely packed near the brain stem and only a few slices spread far apart near the top of the head. Variable between-slice spacing is rarer in MRI, and if you see this from a MRI sequence you should ensure that [all of the acquired slices have been provided to dcm2niix](https://neurostars.org/t/field-mapping-siemens-scanners-dcm2niix-output-2-bids/2075/7). NIfTI assumes all 2D slices that form a 3D stack are equidistant. Therefore, dcm2niix reslices the input data to generate an equidistant volume.
  - _ph phase map
  - _iN appended image number for non-parallel slices
  - _imaginary imaginary component of complex image
@@ -87,4 +87,4 @@ dcm2niix will attempt to write your image using the naming scheme you specify wi
 
 [Control characters](https://en.wikipedia.org/wiki/ASCII#Control_characters) like backspace and tab are also forbidden.
 
-Be warned that dcm2niix will copy all allowed characters verbatim, which can cause problems for some other tools. Consider this [sample dataset](https://github.com/neurolabusc/dcm_qa_nih/tree/master/In/20180918GE/mr_0004) where the DICOM Protocol Name (0018,1030) is 'Axial_EPI-FMRI_(Interleaved_I_to_S)'. The parentheses ("round brackets") may cause other tools issues. Consider converting this series with the command 'dcm2niix -f %s_%p ~/DICOM' to create the file '4_Axial_EPI-FMRI_(Interleaved_I_to_S).nii'.If you now run the command 'fslhd 4_Axial_EPI-FMRI_(Interleaved_I_to_S).nii' you will get the error '-bash: syntax error near unexpected token `(''. Therefore, it is often a good idea to use double quotes to specify the names of files. In this example 'fslhd "4_Axial_EPI-FMRI_(Interleaved_I_to_S).nii"' will work correctly.
+Be warned that dcm2niix will copy all allowed characters verbatim, which can cause problems for some other tools. Consider this [sample dataset](https://github.com/neurolabusc/dcm_qa_nih/tree/master/In/20180918GE/mr_0004) where the DICOM Protocol Name (0018,1030) is 'Axial_EPI-FMRI_(Interleaved_I_to_S)'. The parentheses ("round brackets") may cause other tools issues. Consider converting this series with the command 'dcm2niix -f %s_%p ~/DICOM' to create the file '4_Axial_EPI-FMRI_(Interleaved_I_to_S).nii'.If you now run the command 'fslhd 4_Axial_EPI-FMRI_(Interleaved_I_to_S).nii' you will get the error '-bash: syntax error near unexpected token `(''. Therefore, it is often a good idea to use double quotes to specify the names of files. In this example 'fslhd "4_Axial_EPI-FMRI_(Interleaved_I_to_S).nii"' will work correctly.

GE/README.md

@@ -110,4 +110,5 @@ Anatomical localizers (e.g. scout images) are quick-and-dirty scans used to posi
  - [Slice Timing and Phase Encoding examples](https://github.com/jannikadon/cc-dcm2bids-wrapper/tree/main/dicom-qa-examples)
  - [Slice timing validation](https://github.com/neurolabusc/dcm_qa_stc) for different varieties of GE EPI sequences.
  - [Examples of phase encoding polarity, slice timing and diffusion gradients](https://github.com/neurolabusc/dcm_qa_ge).
- - The dcm2niix [wiki](https://www.nitrc.org/plugins/mwiki/index.php/dcm2nii:MainPage) includes examples of diffusion data, slice timing, and other variations.
+ - The dcm2niix [wiki](https://www.nitrc.org/plugins/mwiki/index.php/dcm2nii:MainPage) includes examples of diffusion data, slice timing, and other variations.
+

PARREC/README.md

@@ -1,9 +1,9 @@
 ## About
 
-dcm2niix attempts to convert Philips PAR/REC format images to NIfTI. While this format remains popular with users, it is slowly being superceded by Philips enhanced DICOM format, an XML/REC format as well as the direct NIfTI export. Note that dcm2niix does not support the XML/REC format.
+dcm2niix attempts to convert Philips PAR/REC format images to NIfTI. While this format remains popular with users, it is slowly being superseded by Philips enhanced DICOM format, an XML/REC format as well as the direct NIfTI export. Note that dcm2niix does not support the XML/REC format.
 
 
-According to [Matthew Clemence](https://www.nitrc.org/forum/forum.php?thread_id=9319&forum_id=4703) DICOM (classic and enhanced) and XML/REC are supported in the base product, NIFTI forms part of a Neuroscience commercial option from release 5 onwards. PAR/REC requires a research agreement to obtain. For the two formats XML/REC and PAR/REC, the "REC" part is identical but instead of a plain text file of the "par" format, the same information is now available as an XML file. This descision has been taken to allow the information to be more easily extended as the PAR file was getting increasingly limited.
+According to [Matthew Clemence](https://www.nitrc.org/forum/forum.php?thread_id=9319&forum_id=4703) DICOM (classic and enhanced) and XML/REC are supported in the base product, NIFTI forms part of a Neuroscience commercial option from release 5 onwards. PAR/REC requires a research agreement to obtain. For the two formats XML/REC and PAR/REC, the "REC" part is identical but instead of a plain text file of the "par" format, the same information is now available as an XML file. This decision has been taken to allow the information to be more easily extended as the PAR file was getting increasingly limited.
 
 ## Detecting, Reporting and Fixing the V4 Image offcentre Bug
 
@@ -43,5 +43,3 @@ Note that for Philips (unlike DICOM) the For PAR/REC the acquisition (%u) and se
 ## dcm2niix Limitations
 
 Be aware that dcm2niix assumes that the data is stored in complete 3D volumes. It will not convert datasets where the scan is interrupted mid-volume (e.g. where the number of 2D slices is not divisible by the number of slices in a volume). This can occur if the user aborts a sequence part way through acquisition. If dcm2niix detects this situation it will suggest you use [dicm2nii](https://www.mathworks.com/matlabcentral/fileexchange/42997-dicom-to-nifti-converter--nifti-tool-and-viewer) which can handle these files.
-
-

Philips/README.md

@@ -18,9 +18,9 @@ Therefore, dcm2niix will ignore the IPP enclosed in 2005,140F unless no alternat
 
 ## Image Scaling
 
-How data is represented in DICOM for MR has several challenges and the technology and standard has evolved over the years to accommodate new uses. Unlike CT, where the signal is naturally displayed in Hounsfield units, MR has no natural signal units and the magnitude is influenced by the electronics and the software processing required to bring this to the final image. Secondly most of the original DICOM implementations used small bit number integers to store the underlying images for economy of storage. As a result it is necessary to apply scaling from the internal DICOM storage to a form suitable for radiographic display or quantitative measurement. There remain several challenges with this process, ensuring that the mapping to the integer values makes best use of the available bit depth for images with large dynamic range, or large changes between images, without clipping the data while also preserving the appearance of the noise field which is demanded by the needs of radiographic visual review. Note that for most MRI modalities these concerns do not impact analyses: the intensity is assumed arbitrary, the statistics treat signal offset and scaling as nuisance regressors when fitting models, and cacluations are computed with high precision floating point numbers. However, there are some situations such as arterial spin labeling where image scaling is important. In these situations, scaling is a crucial aspect to be aware of for quantitative methods and which representation is used depends upon your needs.
+How data is represented in DICOM for MR has several challenges and the technology and standard has evolved over the years to accommodate new uses. Unlike CT, where the signal is naturally displayed in Hounsfield units, MR has no natural signal units and the magnitude is influenced by the electronics and the software processing required to bring this to the final image. Secondly most of the original DICOM implementations used small bit number integers to store the underlying images for economy of storage. As a result it is necessary to apply scaling from the internal DICOM storage to a form suitable for radiographic display or quantitative measurement. There remain several challenges with this process, ensuring that the mapping to the integer values makes best use of the available bit depth for images with large dynamic range, or large changes between images, without clipping the data while also preserving the appearance of the noise field which is demanded by the needs of radiographic visual review. Note that for most MRI modalities these concerns do not impact analyses: the intensity is assumed arbitrary, the statistics treat signal offset and scaling as nuisance regressors when fitting models, and calculations are computed with high precision floating point numbers. However, there are some situations such as arterial spin labeling where image scaling is important. In these situations, scaling is a crucial aspect to be aware of for quantitative methods and which representation is used depends upon your needs.
 
-At its simplest image scaling requires a rescale slope and intercept defined by the DICOM standard tags [0028,1053](http://dicomlookup.com/lookup.asp?sw=Tnumber&q=(0028,1053)) and [0028,1052](http://dicomlookup.com/lookup.asp?sw=Tnumber&q=(0028,1053)). Whether these values are the same for all images, or image specific depends upon the implementation and potentially the location of these tags withing the DICOM tag structure. For manufacturers other than Philips, these are the only intensity scaling values provided, so there is no concern regarding which scaling values should be used.
+At its simplest image scaling requires a rescale slope and intercept defined by the DICOM standard tags [0028,1053](http://dicomlookup.com/lookup.asp?sw=Tnumber&q=(0028,1053)) and [0028,1052](http://dicomlookup.com/lookup.asp?sw=Tnumber&q=(0028,1053)). Whether these values are the same for all images, or image specific depends upon the implementation and potentially the location of these tags within the DICOM tag structure. For manufacturers other than Philips, these are the only intensity scaling values provided, so there is no concern regarding which scaling values should be used.
 
 However, the DICOM standard introduced the concept of [`real world units`](http://dicom.nema.org/dicom/2013/output/chtml/part03/sect_A.46.html). This allows the storage of one or more mappings to allow selective viewing of the data mapped into different value ranges (which may also be non-linear mappings).
 
@@ -33,7 +33,7 @@ Philips thinks in terms of three different representations (using the terminolog
 | Floating Point   | FP            | An internal value at a point earlier in the reconstruction chain before the conversion to DICOM/integer for image presentation.  |
 | Real World Value | WV            | DICOM defined real world units|
 
-In general SV should not be used for quantitative measurements as it is an integer format. In practice, if the Rescale values are the same for all images (the typical case, but not guaranteed) SV can be used to compare signal intensities between images from the same scan. Note that the NIfTI format only provides a single `scl_slope` and `scl_inter` for the entire file, whereas in DICOM rescale values can in theory differ across 2D slices. Therefore, in situations where the rescale values do differ across slices, dcm2niix will apply the requested rescale to each slice and save the scaled data as the 32-bit float NIfTI dataset. This preserves the varibility reported by the rescale tags, at the cost of disk space. 
+In general SV should not be used for quantitative measurements as it is an integer format. In practice, if the Rescale values are the same for all images (the typical case, but not guaranteed) SV can be used to compare signal intensities between images from the same scan. Note that the NIfTI format only provides a single `scl_slope` and `scl_inter` for the entire file, whereas in DICOM rescale values can in theory differ across 2D slices. Therefore, in situations where the rescale values do differ across slices, dcm2niix will apply the requested rescale to each slice and save the scaled data as the 32-bit float NIfTI dataset. This preserves the variability reported by the rescale tags, at the cost of disk space. 
 
 DV can be used for quantitative comparison of signal intensities between images in the same scan as long as the relevant rescale values are taken into account. These rescale values may come from the tags standard tags 0028,1053 and 0028,1052 or from a relevant RealWorld block if present. If the DV is derived from a RealWorld block with defined units (tag (0008,0104) such as Hz or ms rather than “no units”) or a RescaleType (0028,1054) with a non-US type (not defined by the standard), then the DV is already quantitative and cross scan comparison may be done.
 
@@ -146,7 +146,7 @@ Research users may want to explore the direct NIfTI export provided by Philips.
 
 Likewise, the BIDS tag "PhaseEncodingDirection" allows tools like [eddy](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/eddy) and [TOPUP](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup) to undistort images. While the Philips DICOM header distinguishes the phase encoding axis (e.g. anterior-posterior vs left-right) it does not encode the polarity (A->P vs P->A).
 
-Another value desirable for TOPUP is the "TotalReadoutTime". Again, one can not confidently calculate this from Philips DICOMs (though on can [appoximate it if you make a few assumptions](https://github.com/nipreps/sdcflows/issues/5)). If you do decide to calculate this using values from the MRI console, be aware that the [FSL definition](https://github.com/rordenlab/dcm2niix/issues/130) is not intuitive for scans with interpolation, partial Fourier, parallel imaging, etc. However, it should be pointed out that the "TotalReadoutTime" only influences TOPUP's calibrated validation images that are typically ignored. The data used in subsequent steps will not be influenced by this value.
+Another value desirable for TOPUP is the "TotalReadoutTime". Again, one can not confidently calculate this from Philips DICOMs (though on can [approximate it if you make a few assumptions](https://github.com/nipreps/sdcflows/issues/5)). If you do decide to calculate this using values from the MRI console, be aware that the [FSL definition](https://github.com/rordenlab/dcm2niix/issues/130) is not intuitive for scans with interpolation, partial Fourier, parallel imaging, etc. However, it should be pointed out that the "TotalReadoutTime" only influences TOPUP's calibrated validation images that are typically ignored. The data used in subsequent steps will not be influenced by this value.
 
 ## Partial Volumes
 
@@ -178,4 +178,4 @@ Prior versions of dcm2niix used different methods to sort images. However, these
  - [Diffusion Examples](https://www.nitrc.org/plugins/mwiki/index.php/dcm2nii:MainPage#Diffusion_Tensor_Imaging)
  - [Additional Diffusion Examples](https://github.com/neurolabusc/dcm_qa_philips)
  - Classic and enhanced [ASL Examples](https://github.com/neurolabusc/dcm_qa_philips_asl)
- - [Enhanced DICOMs](https://github.com/neurolabusc/dcm_qa_enh)
+ - [Enhanced DICOMs](https://github.com/neurolabusc/dcm_qa_enh)

README.md

@@ -36,7 +36,7 @@ dcm2niix is developed by the community for the community and everybody can becom
 
 ## Running
 
-Command line usage is described in the [NITRC wiki](https://www.nitrc.org/plugins/mwiki/index.php/dcm2nii:MainPage#General_Usage). The minimal command line call would be `dcm2niix /path/to/dicom/folder`. However, you may want to invoke additional options, for example the call `dcm2niix -z y -f %p_%t_%s -o /path/ouput /path/to/dicom/folder` will save data as gzip compressed, with the filename based on the protocol name (%p) acquisition time (%t) and DICOM series number (%s), with all files saved to the folder "output". For more help see help: `dcm2niix -h`.
+Command line usage is described in the [NITRC wiki](https://www.nitrc.org/plugins/mwiki/index.php/dcm2nii:MainPage#General_Usage). The minimal command line call would be `dcm2niix /path/to/dicom/folder`. However, you may want to invoke additional options, for example the call `dcm2niix -z y -f %p_%t_%s -o /path/output /path/to/dicom/folder` will save data as gzip compressed, with the filename based on the protocol name (%p) acquisition time (%t) and DICOM series number (%s), with all files saved to the folder "output". For more help see help: `dcm2niix -h`.
 
 [See the BATCH.md file for instructions on using the batch processing version](./BATCH.md).