artifact op• Data kinds: sinogram → sinogram
• Call: import fullseye as fs; fs.ledger.metal_trace_interpolate(sinogram, angles_deg=None, image_threshold=None, mask=None, size=None) (implementation को सीधे call करना हो तो import tomography; tomography.metal_trace_interpolate(sinogram, angles_deg=None, image_threshold=None, mask=None, size=None), registry से लेना हो तो opstomography.get("metal_trace_interpolate"))
linear interpolation से metal artefact reduction (LI-MAR)।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
A metal implant attenuates so strongly that its detector bins carry almost no photons; the measured line integrals there are dominated by scatter and by the noise floor of the logarithm, and back-projecting them lays down the dark bands and bright streaks between dense objects that make a slice unreadable.
The oldest working answer, and still the baseline every newer method is compared against: declare the affected bins *missing* and fill them by linear interpolation along the detector axis, per angle. The result is not the truth — it is a smooth guess with the streaks removed — and it blurs whatever was genuinely behind the metal.
How the affected bins are found matters more than the interpolation, and the obvious way is measurably worse than doing nothing. The metal trace is located the way LI-MAR actually does it: reconstruct once with filtered_backprojection, threshold the *image* at mean + 3*std, forward-project that binary mask, and treat the bins it touches as missing. Thresholding the sinogram directly is the shortcut that suggests itself, and on the Shepp-Logan phantom with a 6-px implant it goes wrong in the direction that is hardest to notice — it flags the densest *legitimate* structure (the skull, seen edge-on) and interpolates it away:
implant density uncorrected sinogram threshold image threshold
x8 0.0487 0.0626 0.0255
x30 0.1583 0.2249 0.0255
x100 0.5214 0.7127 0.0257
(normalised RMS error against the metal-free truth, outside the implant footprint; the metal-free reconstruction itself scores 0.0250.) The image route recovers essentially all of the damage at every density. The sinogram route makes it 1.3-1.4x *worse* than not correcting at all, at every density, while still producing a picture with fewer visible streaks — which is why the shortcut is not offered as an option here rather than merely discouraged.
Inside the implant footprint the reconstruction is now wrong on purpose: the metal is gone. Clinical practice puts it back from the thresholded reconstruction afterwards; that step is not implemented.
:param sinogram: (n_angles, n_detectors). :param angles_deg: view angles; None -> uniform [0, 180). Used for the
internal reconstruction and re-projection, so a wrong angle set here
misplaces the trace.
:param image_threshold: image-domain metal threshold; None ->
mean + 3*std of the internal reconstruction.
:param mask: explicit boolean (n_angles, n_detectors) metal trace. When
given, no reconstruction is done and *image_threshold* is ignored — this
is the route to use when the trace comes from a segmentation you trust.
:param size: side of the internal reconstruction; None -> the inscribed
square.
:returns: (n_angles, n_detectors) float64 sinogram. :raises ValueError: if the mask shape disagrees with the sinogram, if the mask
is not boolean, if nothing is left to interpolate from, or if any *row* is
entirely masked.
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• ct_reconstruction — py -3.11 examples/ct_reconstruction.py
sinogram को input के रूप में लेते हैं)backproject_sinogram · filtered_backprojection · sart_reconstruct · beam_hardening_apply · beam_hardening_correct · ring_artifact_apply · ring_artifact_remove · sinogram_center_of_rotation
artifact)beam_hardening_apply · beam_hardening_correct · ring_artifact_apply · ring_artifact_remove
*Provenance: tomography.py — TOMOGRAPHY operator registry. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.