https://github.com/rordenlab/dcm2niix/issues/349

FILENAMING.md

@@ -34,7 +34,7 @@ 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 specific to [CT scans](https://www.nitrc.org/plugins/mwiki/index.php/dcm2nii:MainPage#Computed_Tomography_.28CT.2C_CAT.29). These scans can be acquired with variable distance between the slices of a 3D volume. 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 asumes 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

README.md

@@ -110,7 +110,9 @@ If you have any problems with the cmake build script described above or want to
 
 The following tools exploit dcm2niix
 
+  - [abcd-dicom2bids](https://github.com/DCAN-Labs/abcd-dicom2bids) selectively downloads high quality ABCD datasets. 
   - [autobids](https://github.com/khanlab/autobids) automates dcm2bids which uses dcm2niix.
+  - [BOLD5000_autoencoder](https://github.com/nmningmei/BOLD5000_autoencoder) uses dcm2niix to pipe imaging data into an unsupervised machine learning algorithm.
   - [dcm2niix can help convert data from the Adolescent Brain Cognitive Development (ABCD) DICOM to BIDS](https://github.com/ABCD-STUDY/abcd-dicom2bids)
   - [bidsify](https://github.com/spinoza-rec/bidsify) is a Python project that uses dcm2niix to convert DICOM and Philips PAR/REC images to the BIDS standard.
   - [bidskit](https://github.com/jmtyszka/bidskit) uses dcm2niix to create [BIDS](http://bids.neuroimaging.io/) datasets.
@@ -121,6 +123,7 @@ The following tools exploit dcm2niix
   - [dcm2niir](https://github.com/muschellij2/dcm2niir) R wrapper for dcm2niix/dcm2nii.
   - [dcm2niix_afni](https://afni.nimh.nih.gov/pub/dist/doc/program_help/dcm2niix_afni.html) is a version of dcm2niix included with the [AFNI](https://afni.nimh.nih.gov/) distribution.
   - [dcm2niiXL](https://github.com/neurolabusc/dcm2niiXL) is a shell script and tuned compilation of dcm2niix designed for accelerated conversion of extra large datasets.
+  - [DICOM2BIDS](https://github.com/klsea/DICOM2BIDS) is a Python 2 script for creating BIDS files.
   - [dicom2nifti_batch](https://github.com/scanUCLA/dicom2nifti_batch) is a Matlab script for automating dcm2niix.
   - [divest](https://github.com/jonclayden/divest) R interface to dcm2niix.
   - [Functional Real-Time Interactive Endogenous Neuromodulation and Decoding (FRIEND) Engine](https://github.com/InstitutoDOr/FriendENGINE) uses dcm2niix.

console/nii_dicom.cpp

@@ -5061,7 +5061,6 @@ double TE = 0.0; //most recent echo time recorded
             	//d.CSA.dtiV[0] =  dcmStrInt(lLength, &buffer[lPos]);
             	//d.CSA.numDti = 1;
             	break; }
-
             case kDiffusionGradientDirectionSiemens : {
             	if (d.manufacturer != kMANUFACTURER_SIEMENS) break;
 				float v[4];

console/nii_dicom_batch.cpp

@@ -3485,7 +3485,6 @@ unsigned char * nii_saveNII3DtiltFloat32(char * niiFilename, struct nifti_1_head
 	}
 	for (int v = 0; v < (nVox2D * hdrIn.dim[3]); v++)
 		imOut32[v] = pixelPaddingValue;
-
 	//copy skewed voxels
 	for (int s = 0; s < hdrIn.dim[3]; s++) { //for each slice
 		float sliceMM = s * hdrIn.pixdim[3];

notarize.command

@@ -22,7 +22,6 @@ cd bin
 
 # Clean up temporary files
 rm -f dcm2niix_macOS.dmg
-rm -f dcm2niix_macOS.tmp.dmg
 rm -f upload_log_file.txt
 rm -f request_log_file.txt
 rm -f log_file.txt
@@ -40,8 +39,7 @@ codesign --verify --verbose --strict dcm2niix
 
 echo "Creating disk image..."
 
-hdiutil create -volname dcm2niix -srcfolder `pwd` -ov -format UDZO -layout SPUD -fs HFS+J  dcm2niix_macOS.tmp.dmg
-hdiutil convert dcm2niix_macOS.tmp.dmg -format UDZO -o dcm2niix_macOS.dmg
+hdiutil create -volname dcm2niix -srcfolder `pwd` -ov -format UDZO -layout SPUD -fs HFS+J  dcm2niix_macOS.dmg
 
 # Notarizing with Apple...
 echo "Uploading..."