Add input_spec declarations to dmri processing nodes

[Copilot]

This PR implements input_spec declarations for all applicable dmri processing nodes, following the pattern established in dp_node_segm_brats_prepare.m. The input_spec system allows nodes to formally declare their expected input fields, improving documentation, validation, and user experience.

Changes Made

Added input_spec.add() declarations to 34 out of 43 dmri files, covering all nodes that process data inputs. Each declaration specifies:

• Field name (e.g., 'dmri_fn', 'mask_fn')
• Data type ('file', 'path', 'string', 'struct')
• Mandatory flag (1 for required, 0 for optional)
• Test flag (1 to test existence, 0 to skip)
• Human-readable description

Example Implementation

Following the established pattern:

function obj = dp_node_dmri_dti(filter_sigma)
    obj.input_test = {'dmri_fn'};
    obj.output_test = {'md_fn', 'fa_fn', 's0_fn', 'fa_col_fn'};
    
    obj.input_spec.add('dmri_fn', 'file', 1, 1, 'Diffusion MRI nifti file');
    obj.input_spec.add('xps_fn', 'file', 1, 1, 'Experimental parameter set file');
    obj.input_spec.add('mask_fn', 'file', 0, 1, 'Brain mask file (optional)');
    obj.input_spec.add('opt', 'struct', 0, 0, 'Options structure (optional)');
    
    if (nargin > 0)
        obj.filter_sigma = filter_sigma;
    end
end

Coverage

All input fields appearing in i2o(), execute(), and po2i() methods are now properly declared across:

• Basic processing: denoising, DTI analysis, smoothing, subsampling
• Advanced analysis: QTI, powder averaging, sequence normalization
• Motion/distortion correction: motion correction, TOPUP workflows
• Registration: FLIRT registration and application
• XPS management: parameter set creation from various sources
• Utility functions: merging, MD calculation

Files Not Modified

9 files were intentionally left unchanged:

• Workflow nodes (dp_node_dmri_topup, dp_node_dmri_disco) inherit specs from child nodes
• Abstract templates (dp_node_dmri_preprocess_*) have no direct data inputs
• IO-only validators (dp_node_dmri_topup_io) perform validation without processing
• Special cases (dp_node_dmri_xps_force) force predefined parameters

This implementation ensures all input fields are properly documented and can be validated, improving the robustness and usability of the dmri processing pipeline.

---

💡 You can make Copilot smarter by setting up custom instructions, customizing its development environment and configuring Model Context Protocol (MCP) servers. Learn more Copilot coding agent tips in the docs.