dicomConversionToNiftiHCC

This python script facilitates standardized conversion of DICOM studies into nifti format by doing the following:

• Near autonomous conversion of DICOM images to medical image processing-friendly nifti format
• Deidentification
• Assignment of a standardized naming convention relevant to a given annotation routine, for example, labling liver lesions on T1 and ADC sequences before and after treatment (easily customized for other uses)

Currently can be used for conversion of pre- and post-treatment MRI abdomen studies AND CT angiography studies by using the appropriate config file

Dependencies

SimpleITK
Numpy
PyDicom
NiPype
Slugify

Usage

Script assumes a nested folder eg tree study-->series-->DICOM (standard when exportingw with Osirix or Horos). This should easily by adapted for other folder structures if working with exported images from other DICOM viewers/PACS servers.

There are two script calls needed:.

1. Grab Metadata: create a .csv with relevant metadata in table for tagging of desired series by domain expert (or algorithm in future)

2. Convert From Table: conversion, deidentification and standardized naming of converted sequences using the annotated tag column from 1) Metadata table.

Grab Metadata into Table

python osirix_dicom_to_nifti.py --grabMetadata --tablePath ./path/to/metadata/table.csv --dicomDir ./path/to/folder/containing/study/folders

This will generate a .csv file with columns of metadata for each series of an MRI study meant to allow inference of which series' are desired for conversion.

Heads up: this can be a lot of rows as modern MRI studies generate a lot of series!

An empty tag column will be generated and with some domain knowledge the user must fill-in with the appropriate tag number.

Example is as follows (take note of the number inputted in the tag column):

MRN	ACC	Machine	Series Path	Acq Time	Series Number	Series Desc	Tag(0=pre,1=ea,2=ea_sub,3=la,4=la_sub,5=pv,6=pv_sub,7=ev,8=ev_sub,9=adc)
9999999	000001	Avanto	./Y90_Seg/name/body-mt-sinai-protocols-abdomen/trufisp-loc-1	110028.625	1	Trufisp_Loc
9999999	000001	Avanto	./Y90_Seg/name/body-mt-sinai-protocols-abdomen/cor-haste-2	110142.875	2	COR HASTE
9999999	000001	Avanto	./Y90_Seg/name/body-mt-sinai-protocols-abdomen/3point-dixon-t2-star-031511-fa10-modified-6611-opp-6	110544.7325	6	3-point Dixon_T2 star_03-15-11_FA10_modified 6/6/11_opp
9999999	000001	Avanto	./Y90_Seg/name/body-mt-sinai-protocols-abdomen/3point-dixon-t2-star-031511-fa10-modified-6611-in-5	110544.735	5	3-point Dixon_T2 star_03-15-11_FA10_modified 6/6/11_in
9999999	000001	Avanto	./Y90_Seg/name/body-mt-sinai-protocols-abdomen/ax-vibe-pre-62612-13	110828.9625	13	AX VIBE PRE (6/26/12)	0
9999999	000001	Avanto	./Y90_Seg/name/body-mt-sinai-protocols-abdomen/ax-vibe-post-2art-1min-3min-equ-14	111014.0575	14	AX VIBE POST 2ART,1MIN,3MIN EQU	1
9999999	000001	Avanto	./Y90_Seg/name/body-mt-sinai-protocols-abdomen/ax-vibe-post-2art-1min-3min-equ-sub-15	111014.0575	15	AX VIBE POST 2ART,1MIN,3MIN EQU_SUB	2
9999999	000001	Avanto	./Y90_Seg/name/body-mt-sinai-protocols-abdomen/ax-vibe-post-2art-1min-3min-equ-16	111035.2275	16	AX VIBE POST 2ART,1MIN,3MIN EQU	3
9999999	000001	Avanto	./Y90_Seg/name/body-mt-sinai-protocols-abdomen/ax-vibe-post-2art-1min-3min-equ-sub-17	111035.2275	17	AX VIBE POST 2ART,1MIN,3MIN EQU_SUB	4
9999999	000001	Avanto	./Y90_Seg/name/body-mt-sinai-protocols-abdomen/ax-vibe-post-2art-1min-3min-equ-18	111114.47	18	AX VIBE POST 2ART,1MIN,3MIN EQU	5
9999999	000001	Avanto	./Y90_Seg/name/body-mt-sinai-protocols-abdomen/ax-vibe-post-2art-1min-3min-equ-sub-19	111114.47	19	AX VIBE POST 2ART,1MIN,3MIN EQU_SUB	6
9999999	000001	Avanto	./Y90_Seg/name/body-mt-sinai-protocols-abdomen/ax-vibe-post-2art-1min-3min-equ-500	111131.799	500	AX VIBE POST 2ART,1MIN,3MIN EQU
9999999	000001	Avanto	./Y90_Seg/name/body-mt-sinai-protocols-abdomen/ax-vibe-post-2art-1min-3min-equ-20	111316.04	20	AX VIBE POST 2ART,1MIN,3MIN EQU	7
9999999	000001	Avanto	./Y90_Seg/name/body-mt-sinai-protocols-abdomen/ax-vibe-post-2art-1min-3min-equ-sub-21	111316.04	21	AX VIBE POST 2ART,1MIN,3MIN EQU_SUB	8
9999999	000001	Avanto	./Y90_Seg/name/body-mt-sinai-protocols-abdomen/cor-vibe-post-3-min-22	111407.52	22	000001	Avanto
9999999	000001	Avanto	./Y90_Seg/name/body-mt-sinai-protocols-abdomen/pace-diffusion-50400800-10113-adc-29	112810.835	29	PACE Diffusion 50-400-800 10-1-13_ADC	9
9999999

Convert DICOM to Nifti Using Tagged Metadata Table

Once the table above is properly annotated with the correct tags for the desired series, the second step is conversion.

python osirix_dicom_to_nifti.py --convertFromTable --tablePath ./path/to/metadata/table.csv --dicomDir ./path/to/folder/containing/study/folders --niftiDir ./path/to/nifti/output/folder

Study folders within designated nifti output folder will be named according to accession number for study, so conversion of these folder names to random strings using a secure look-up table is encouraged for thorough de-identification.