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feat: add CsaHeader.from_dicom() convenience method
Implement direct DICOM integration for CSA header extraction. The new from_dicom() class method automatically locates and extracts CSA headers from DICOM datasets without requiring users to know exact DICOM tag numbers. Features: - Automatic CSA header location using DICOM private tag protocol - Support for both 'image' and 'series' CSA header types - Case-insensitive csa_type parameter - Returns None gracefully when CSA headers not present - Comprehensive test coverage (19 new tests, 96% overall coverage) Implementation inspired by nibabel's get_csa_header() function, adapted to csa_header's API design and coding standards. Changes: - Added CsaHeader.from_dicom() classmethod to csa_header/header.py - Added CsaHeader.CSA_TAGS class constant mapping header types to tags - Added CsaHeader._extract_csa_bytes() private helper method - Created comprehensive test suite in tests/test_dicom_integration.py - Updated README.md with usage examples and Related Projects section - Updated CHANGELOG.md with feature announcement and acknowledgments - Added pydicom to lint environment for proper type checking Documentation: - Updated Quickstart section with from_dicom() examples - Added Related Projects section acknowledging nibabel and PyDICOM - Enhanced Integration with NiBabel section - Comprehensive docstring with examples and nibabel attribution Suggested-by: Matthew Brett <matthew.brett@gmail.com> Closes #16 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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CHANGELOG.md

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@@ -7,6 +7,43 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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## [Unreleased]
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### Added
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#### `CsaHeader.from_dicom()` convenience method (#16)
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A new classmethod for extracting CSA headers directly from DICOM datasets without needing to know the exact DICOM tag numbers.
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**Features:**
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- Automatically locates CSA headers using DICOM private tag protocol
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- Supports both `'image'` and `'series'` CSA header types
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- Returns `None` gracefully when CSA headers are not present
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- Case-insensitive `csa_type` parameter
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**Usage:**
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```python
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import pydicom
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from csa_header import CsaHeader
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dcm = pydicom.dcmread('siemens_scan.dcm')
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# Extract CSA headers easily
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csa_image = CsaHeader.from_dicom(dcm, 'image')
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csa_series = CsaHeader.from_dicom(dcm, 'series')
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if csa_series:
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csa_dict = csa_series.read()
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```
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**Implementation:**
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- Inspired by nibabel's `get_csa_header()` function
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- Added `CSA_TAGS` class constant mapping header types to DICOM tags
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- Includes comprehensive test coverage (19 new tests)
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**Acknowledgments:**
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- Feature suggested by @matthew-brett
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- Implementation inspired by [nibabel's](https://github.com/nipy/nibabel) approach to CSA header extraction
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- Thanks to the nibabel project for pioneering CSA header parsing in Python
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## [2.0.0] - 2025-11-01
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### 🔥 Breaking Changes

README.md

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>>> dicom_path = fetch_example_dicom()
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>>> dcm = pydicom.dcmread(dicom_path)
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>>>
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>>> # Parse CSA Series Header
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>>> # Method 1: Convenience method (easiest!)
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>>> csa_header = CsaHeader.from_dicom(dcm, 'series')
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>>> parsed_csa = csa_header.read()
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>>> len(parsed_csa)
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79
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>>>
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>>> # Method 2: Manual extraction (also works)
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>>> raw_csa = dcm[(0x29, 0x1020)].value
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>>> parsed_csa = CsaHeader(raw_csa).read()
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>>> len(parsed_csa)
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79
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```
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**New in version 2.1.0**: The `from_dicom()` method automatically locates CSA headers without needing to know the exact DICOM tag numbers!
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The example file is an anonymized Siemens MPRAGE scan hosted on Zenodo: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.17482132.svg)](https://doi.org/10.5281/zenodo.17482132)
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### Option 2: Using Your Own DICOM Files
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```python
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>>> import pydicom
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>>> from csa_header import CsaHeader
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>>> dcm = pydicom.dcmread("/path/to/file.dcm")
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```
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Extract a data element containing a CSA header, e.g., for _CSA Series Header Info_:
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**Recommended approach** - Use the `from_dicom()` convenience method:
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```python
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>>> data_element = dcm.get((0x29, 0x1020))
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>>> data_element
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(0029, 1020) [CSA Series Header Info] OB: Array of 180076 elements
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>>> # Extract CSA Series Header (or use 'image' for Image Header)
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>>> csa_header = CsaHeader.from_dicom(dcm, 'series')
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>>> if csa_header:
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... parsed_csa = csa_header.read()
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... print(f"Found {len(parsed_csa)} CSA tags")
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Found 79 CSA tags
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```
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Read the raw byte array from the data element:
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**Alternative approach** - Manual extraction if you know the exact DICOM tags:
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```python
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>>> raw_csa = data_element.value
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>>> raw_csa
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b'SV10\x04\x03\x02\x01O\x00\x00\x00M\x00\x00\x00UsedPatientWeight\x00 <Visible> "true" \n \n <ParamStr\x01\x00\x00\x00IS\x00\x00\x06...'
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>>> # For CSA Series Header Info: (0x0029, 0x1020)
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>>> # For CSA Image Header Info: (0x0029, 0x1010)
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>>> data_element = dcm.get((0x29, 0x1020))
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>>> if data_element:
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... raw_csa = data_element.value
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... parsed_csa = CsaHeader(raw_csa).read()
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```
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Parse the contents of the CSA header with the `CsaHeader` class:
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Example parsed CSA header structure:
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```python
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>>> from csa_header import CsaHeader
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>>> parsed_csa = CsaHeader(raw_csa).read()
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>>> parsed_csa
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{
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'NumberOfPrescans': {'VR': 'IS', 'VM': 1, 'value': 0},
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## Integration with NiBabel
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`csa_header` works seamlessly with [NiBabel](https://nipy.org/nibabel/) for comprehensive neuroimaging workflows:
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`csa_header` works seamlessly with [NiBabel](https://nipy.org/nibabel/) for comprehensive neuroimaging workflows. The `from_dicom()` method provides a nibabel-style API for extracting CSA headers:
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```python
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import nibabel as nib
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dcm = pydicom.dcmread('scan.dcm')
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nib_img = nib.load('scan.dcm')
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# Extract CSA header information
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if (0x0029, 0x1010) in dcm:
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csa = CsaHeader(dcm[0x0029, 0x1010].value)
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csa_info = csa.read()
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# Extract CSA header information (new convenience method!)
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csa_header = CsaHeader.from_dicom(dcm, 'image')
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if csa_header:
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csa_info = csa_header.read()
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# Use NiBabel for image data
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data = nib_img.get_fdata()
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See [examples/nibabel_integration.py](examples/nibabel_integration.py) for complete integration examples.
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## Related Projects
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### NiBabel
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[NiBabel](https://nipy.org/nibabel/) is a comprehensive neuroimaging file format library that pioneered CSA header parsing in Python. The `csa_header` package focuses specifically on CSA header parsing with a lightweight, dependency-minimal approach, while NiBabel provides broader neuroimaging format support.
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The `from_dicom()` method was inspired by nibabel's `get_csa_header()` function, adapted to fit `csa_header`'s focused API design.
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**When to use each:**
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- **Use `csa_header`** when you need fast, lightweight CSA parsing with minimal dependencies
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- **Use NiBabel** when you need comprehensive neuroimaging format support (NIfTI, DICOM, MINC, etc.)
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- **Use both together** for complete neuroimaging workflows (see [Integration with NiBabel](#integration-with-nibabel))
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For more on NiBabel's CSA header support, see: https://nipy.org/nibabel/dicom/siemens_csa.html
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### PyDICOM
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Our DICOM integration is powered by the excellent [PyDICOM](https://github.com/pydicom/pydicom) library, which provides comprehensive DICOM file parsing capabilities. `csa_header` extends PyDICOM by providing specialized parsing for Siemens' proprietary CSA header format.
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## Examples
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The [examples/](examples/) directory contains comprehensive usage examples:

csa_header/header.py

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from __future__ import annotations
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from typing import Any
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from typing import TYPE_CHECKING, Any, ClassVar, Literal
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from csa_header.ascii import CsaAsciiHeader
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from csa_header.exceptions import CsaReadError
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from csa_header.messages import INVALID_CHECK_BIT, READ_OVERREACH, TOO_MANY_ITEMS
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from csa_header.unpacker import Unpacker
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from csa_header.utils import VR_TO_TYPE, decode_latin1, strip_to_null
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if TYPE_CHECKING:
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import pydicom # Only imported for type hints
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class CsaHeader:
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"""
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#: ASCII header tag names.
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ASCII_HEADER_TAGS: frozenset[str] = frozenset({"MrPhoenixProtocol"})
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#: Mapping of CSA header types to their DICOM private tag addresses.
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CSA_TAGS: ClassVar[dict[str, tuple[int, int]]] = {
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"image": (0x0029, 0x1010), # CSA Image Header Info
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"series": (0x0029, 0x1020), # CSA Series Header Info
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}
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def __init__(self, raw: bytes):
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"""
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Initialize a new `CsaHeader` instance.
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CSA type 2 or not
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"""
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return self._csa_type == self.CSA_TYPE_2
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@staticmethod
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def _extract_csa_bytes(
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dcm_data: pydicom.Dataset,
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csa_type: str,
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) -> bytes | None:
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"""
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Extract raw CSA header bytes from a DICOM dataset.
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This is a private helper method that locates and extracts the raw
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CSA header data from a DICOM dataset's private tags.
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Parameters
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----------
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dcm_data : pydicom.Dataset
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DICOM dataset containing Siemens CSA header information
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csa_type : str
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Type of CSA header to extract ('image' or 'series')
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Returns
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-------
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bytes or None
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Raw CSA header bytes, or None if the tag is not present
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"""
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# Get the tag address for the requested CSA type
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tag = CsaHeader.CSA_TAGS[csa_type]
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# Check if the tag exists in the dataset
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if tag not in dcm_data:
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return None
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# Extract and return the value
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element = dcm_data[tag]
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if element.value is None:
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return None
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return bytes(element.value)
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@classmethod
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def from_dicom(
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cls,
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dcm_data: pydicom.Dataset,
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csa_type: Literal["image", "series"] = "image",
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) -> CsaHeader | None:
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"""
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Extract and parse CSA header directly from a DICOM dataset.
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This method implements the DICOM private tag search protocol to locate
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and extract CSA headers from Siemens DICOM files. The implementation is
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inspired by nibabel's ``get_csa_header()`` function, adapted to csa_header's
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API design.
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For more information on nibabel's CSA header support, see:
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https://github.com/nipy/nibabel
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Parameters
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----------
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dcm_data : pydicom.Dataset
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DICOM dataset from a Siemens MRI scanner
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csa_type : {'image', 'series'}, default='image'
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Type of CSA header to extract:
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- ``'image'`` : CSA Image Header Info (0x0029, 0x1010)
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- ``'series'`` : CSA Series Header Info (0x0029, 0x1020)
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Returns
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-------
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CsaHeader or None
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CsaHeader instance containing the raw CSA data, or None if the
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specified CSA header is not present in the dataset. Call ``.read()``
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on the returned instance to get the parsed dictionary.
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Raises
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------
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ValueError
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If ``csa_type`` is not 'image' or 'series'
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Examples
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--------
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>>> import pydicom
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>>> from csa_header import CsaHeader
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>>>
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>>> # Load DICOM file
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>>> dcm = pydicom.dcmread('siemens_scan.dcm')
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>>>
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>>> # Extract image CSA header
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>>> csa_header = CsaHeader.from_dicom(dcm, 'image')
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>>> if csa_header:
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... csa_dict = csa_header.read()
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... print(f"Found {len(csa_dict)} CSA tags")
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>>>
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>>> # Extract series CSA header
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>>> csa_header = CsaHeader.from_dicom(dcm, 'series')
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>>> if csa_header:
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... csa_dict = csa_header.read()
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... protocol = csa_dict.get('MrPhoenixProtocol')
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Notes
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-----
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This is a convenience method that combines DICOM tag extraction with
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CSA header parsing. It is equivalent to:
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>>> raw_bytes = dcm[(0x0029, 0x1010)].value # for 'image'
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>>> csa_header = CsaHeader(raw_bytes)
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>>> csa_dict = csa_header.read()
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See Also
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--------
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CsaHeader.read : Parse CSA header from raw bytes
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"""
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# Validate csa_type parameter
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csa_type_lower = csa_type.lower()
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if csa_type_lower not in cls.CSA_TAGS:
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valid_types = ", ".join(repr(t) for t in cls.CSA_TAGS.keys())
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msg = f"Invalid csa_type: {csa_type!r}. Must be one of: {valid_types}"
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raise ValueError(msg)
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# Extract raw CSA bytes
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raw_bytes = cls._extract_csa_bytes(dcm_data, csa_type_lower)
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if raw_bytes is None:
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return None
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# Create and return CsaHeader instance
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return cls(raw_bytes)

pyproject.toml

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test-cov = "coverage run"
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[tool.hatch.envs.lint]
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dependencies = ["black>=23.1.0", "mypy>=1.0.0", "ruff>=0.0.243"]
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dependencies = [
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"black>=23.1.0",
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"mypy>=1.0.0",
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"ruff>=0.0.243",
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"pydicom>=2.2.0", # Needed for type checking
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]
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detached = true
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[tool.hatch.envs.lint.scripts]
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all = ["style", "typing"]

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