forward op• 데이터 종류: image2d → sinogram
• 호출: import fullseye as fs; fs.ledger.radon_transform(image, angles_deg=None, n_detectors=None, oversample=1)(구현을 직접 호출하려면 import tomography; tomography.radon_transform(image, angles_deg=None, n_detectors=None, oversample=1), 원장에서 가져오려면 opstomography.get("radon_transform"))
평행빔 순투영: 슬라이스를 넣고 사이노그램을 냅니다.
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
Rows of the result are projection angles and columns are detector bins. The convention is fixed here and never negotiated again; every other operator in this module reads it the same way, and a transposed sinogram is structurally indistinguishable from a valid one (see opstomography for the measured consequences of not giving it its own sort).
The ray at detector bin j and angle theta is the line x cos(theta) + y sin(theta) = j - (n_det-1)/2, with x the column offset from the image centre and y the row offset — so +y runs *down* the array, matching the rest of Fullseye's image indexing rather than a textbook's upward y. The transform is the same either way (the sinogram is mirrored in the angle axis), but only one of the two agrees with ellipse_phantom, and the tests hold them together.
Accuracy against the closed form (a disc of radius 60 px in a 256-px grid, 180 views), measured in tests/test_tomography.py: interior RMS error 0.073 % of the peak line integral, whole-sinogram RMS 0.402 % — the difference between the two being the partial-volume edge, where the phantom's own anti-aliased boundary is what is being sampled.
:param image: 2-D slice, at least 2x2. :param angles_deg: 1-D view angles in degrees; None ->
linspace(0, 180, 180, endpoint=False).
:param n_detectors: bins; None -> odd count covering the diagonal. :param oversample: ray samples per pixel, 1 .. 8. The default is 1
because 4 measures no better (0.073 % against 0.070 %).
:returns: (n_angles, n_detectors) float64 sinogram. :raises ValueError: on non-finite input, an empty angle list, a detector count
under 4, or a sinogram over MAX_SINOGRAM_ELEMENTS.
• 샘플 데이터 카탈로그(DL URL / 라이선스) —— 2-D 는 skimage.data(BSD/public)+ 합성, 3-D 는 실데이터 소스(Stanford/PDS 등)의 DL URL.
• 연산자의 내력·참고문헌 —— 이 연산자 족의 바탕이 된 연구/기법의 출처.
• 알고리즘의 정전(저자·연도)과 용도는 위의 패밀리 사용 가이드에 적혀 있습니다.
• ct_reconstruction — py -3.11 examples/ct_reconstruction.py
• poc_ct_fidelity — py -3.11 examples/poc_ct_fidelity.py
sinogram 를 입력으로 받는 것)backproject_sinogram · filtered_backprojection · sart_reconstruct · beam_hardening_apply · beam_hardening_correct · ring_artifact_apply · ring_artifact_remove · metal_trace_interpolate
forward)ellipse_phantom · ellipse_sinogram
*Provenance: tomography.py — TOMOGRAPHY 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.