DICOM and Medical Imaging Data Formats

DICOM, Digital Imaging and Communications in Medicine, specifies ways to store and exchange medical imaging objects. A DICOM file commonly contains a data set of attributes, such as identifiers, acquisition details, geometry, and image pixel data, alongside file-level encoding information. The standard also covers communication between imaging systems. DICOM is therefore more than an image file extension: it carries context that may be necessary to interpret, route, or relate images. A study may contain multiple series, and each series may contain multiple instances. A CT series often consists of many slices, but file order alone should not be assumed to define spatial order. Geometry attributes such as image position and orientation help determine how pixels map into patient coordinates. Pixel values may also need interpretation using modality-specific attributes such as rescale slope and intercept. Compression and transfer syntax determine how encoded pixel data are represented and decoded. NIfTI is commonly used in neuroimaging to store multidimensional arrays with a header describing dimensions, datatype, voxel sizes, and spatial transforms. A single .nii file or a paired header-and-image arrangement may be used. The affine mapping connects voxel indices to spatial coordinates, which is important when aligning scans or overlays. NIfTI often supports analysis workflows with volumetric arrays more directly than a collection of separate DICOM instances. Conversion is not merely copying pixel arrays. Coordinate conventions, orientation, scaling, timing, acquisition parameters, and identifiers may be represented differently or omitted. Validate dimensions, voxel spacing, orientation, and image intensity after conversion, and retain provenance. De-identification must consider both structured attributes and pixel content, where burned-in text may appear. For machine learning, define the unit of analysis and split by patient or another meaningful group so slices from one person do not leak across training and evaluation. Confirm labels, preprocessing, and intended clinical context with qualified domain experts. An image-format parser does not establish that measurements or predictions are clinically valid.