Improve Parker weighting detection robustness for near-360° scans#743
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This PR improves the automatic Parker weighting detection in the FDK reconstruction. The previous condition could incorrectly classify a nearly complete 360° scan as a short scan due to small numerical errors or slight angular sampling inconsistencies. This modification introduces a tolerance of half an angular interval. In other words, if the missing angular coverage is within 0.5× the maximum angular step, the scan is still treated as a full 360° scan rather than enabling Parker weighting unnecessarily. This improves robustness while preserving correct short-scan detection. This issue was observed during 16× and higher sparse-view reconstructions using the Walnut dataset in WalnutPCCTReconCodes（https://github.com/zezisme/WalnutPCCTReconCodes）, where near-360° scans could be incorrectly identified as short scans, unintentionally triggering Parker weighting.
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This PR improves the automatic Parker weighting detection in the FDK reconstruction.
The previous condition could incorrectly classify a nearly complete 360° scan as a short scan due to small numerical errors or slight angular sampling inconsistencies.
This modification introduces a tolerance of half an angular interval. In other words, if the missing angular coverage is within 0.5× the maximum angular step, the scan is still treated as a full 360° scan rather than enabling Parker weighting unnecessarily.
This improves robustness while preserving correct short-scan detection.
This issue was observed during 16× and higher sparse-view reconstructions using the photon-counting CT walnut dataset in WalnutPCCTReconCodes（built on TIGRE）, where 360° scans could be incorrectly identified as short scans, unintentionally triggering Parker weighting.