{ "id": "epfh9gypdb", "slug": "filtered-back-projection", "title": "Filtered back projection", "updated": "2026-10-25", "topic_path": [ { "id": "technology", "label": "Technology and the built world", "api_url": "https://www.edgepedia.org/api/v1/topics/technology" }, { "id": "technology.computing", "label": "Computing and digital systems", "api_url": "https://www.edgepedia.org/api/v1/topics/technology.computing" }, { "id": "technology.computing.ai", "label": "Artificial intelligence and data", "api_url": "https://www.edgepedia.org/api/v1/topics/technology.computing.ai" }, { "id": "technology.computing.ai.algorithms", "label": "Algorithms and computational methods", "api_url": "https://www.edgepedia.org/api/v1/topics/technology.computing.ai.algorithms" }, { "id": "technology.computing.ai.numerical_string_geometry", "label": "Numerical, string, and geometric algorithms", "api_url": "https://www.edgepedia.org/api/v1/topics/technology.computing.ai.numerical_string_geometry" } ], "geo": [ { "id": "geo.nongeo.t1946.technology.computing.ai.numerical_string_geometry", "label": "Non-geographic · 1946 to 2000: Numerical, string, and geometric algorithms", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.technology.computing.ai.numerical_string_geometry", "path": [ { "id": "geo.nongeo", "label": "Non-geographic", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo" }, { "id": "geo.nongeo.t1946", "label": "Non-geographic · 1946 to 2000", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946" }, { "id": "geo.nongeo.t1946.technology", "label": "Technology and the built world", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.technology" }, { "id": "geo.nongeo.t1946.technology.computing", "label": "Computing and digital systems", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.technology.computing" }, { "id": "geo.nongeo.t1946.technology.computing.ai", "label": "Artificial intelligence and data", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.technology.computing.ai" }, { "id": "geo.nongeo.t1946.technology.computing.ai.algorithms", "label": "Algorithms and computational methods", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.technology.computing.ai.algorithms" }, { "id": "geo.nongeo.t1946.technology.computing.ai.numerical_string_geometry", "label": "Numerical, string, and geometric algorithms", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.technology.computing.ai.numerical_string_geometry" } ] } ], "excerpt": "Filtered back projection (FBP) is a tomographic reconstruction algorithm that filters each projection with a ramp and back-projects it into a cross-sectional image, long the standard method in CT.", "snippet": "Filtered back projection (FBP) is a tomographic reconstruction algorithm that filters each projection with a ramp and back-projects it into a cross-sectional image, long the standard method in CT.", "node": "technology.computing.ai.numerical_string_geometry", "markdown": "# Filtered back projection\n\nFiltered back projection (FBP) is a tomographic reconstruction algorithm that converts measured projections, one-dimensional line integrals through an object, into a cross-sectional image by applying a high-pass ramp filter to each projection and then back-projecting, or smearing, the filtered data over the image plane and summing over angles. Its speed, which comes from one FFT-based filtering pass plus a single back-projection, together with few tunable parameters and well-understood behavior, made it the standard reconstruction method in computed tomography (CT) for decades.[1](https://www2.compute.dtu.dk/~pcha/HDtomo/SC/Week1Days1and2.pdf) Commercial EMI scanners adopted an FBP-style method in the 1970s, reconstructing a 100 × 100 image in 30 s on a minicomputer,[2](https://pmc.ncbi.nlm.nih.gov/articles/PMC8492478/) [Iterative reconstruction](https://www.edgepedia.org/iterative-reconstruction) had a longer history, since the first CT scanners in the early 1970s already used iterative algorithms, but commercial iterative methods reemerged in 2009 when the FDA approved Siemens' IRIS.[2](https://pmc.ncbi.nlm.nih.gov/articles/PMC8492478/) • [26](https://pmc.ncbi.nlm.nih.gov/articles/PMC6443602/) • [2](https://pmc.ncbi.nlm.nih.gov/articles/PMC8492478/) Its popularity rests mainly on computational efficiency and numerical stability.[3](https://www.imagewisely.org/Imaging-Modalities/Computed-Tomography/Image-Reconstruction-Techniques)\n\n| Property | Detail |\n|---|---|\n| Output | Cross-sectional image from filtered, back-projected line integrals; one FFT filtering pass plus one back-projection [1](https://www2.compute.dtu.dk/~pcha/HDtomo/SC/Week1Days1and2.pdf) |\n| Ram-Lak discrete kernel | \\( h(0) = 1/(4\\Delta p^{2}) \\), \\( h(n\\Delta p) = 0 \\) for even nonzero \\( n \\), \\( h(n\\Delta p) = -1/(n^{2}\\pi^{2}\\Delta p^{2}) \\) for odd \\( n \\) [4](https://gray.mgh.harvard.edu/media/com_dpattachments/attachments/com_content.article/Image-Reconstruction-Part-I-Planar-Reconstruction-from-Projections.pdf) |\n| Shepp-Logan discrete kernel | \\( h(n\\Delta p) = -2/(\\pi^{2}\\Delta p^{2}(4n^{2}-1)) \\), the ramp multiplied by \\( \\mathrm{sinc}(\\nu\\Delta p) \\) [4](https://gray.mgh.harvard.edu/media/com_dpattachments/attachments/com_content.article/Image-Reconstruction-Part-I-Planar-Reconstruction-from-Projections.pdf) |\n| Ramp filter | \\( |\\omega| \\) in the frequency domain; non-bounded and not realizable, so a window \\( h(\\omega) \\) enforces zero response outside a chosen band [5](https://doi.org/10.1109/access.2024.3357355) |\n| Historical status | Standard CT method until the 2009 FDA approval of iterative reconstruction (Siemens IRIS) [2](https://pmc.ncbi.nlm.nih.gov/articles/PMC8492478/) |\n| Speed | Single GPU runs FBP 23× faster than a 64-core multi-threaded CPU for a 1024 × 1024 image [6](http://christian.mendl.net/science/publications/FBP_Mendl_Eliuk_Noga_Boulanger_ELCVIA2013.pdf) |\n| Cone-beam form | The approximate FDK algorithm, with weighting, filtering, weighting, back-projection steps [1](https://www2.compute.dtu.dk/~pcha/HDtomo/SC/Week1Days1and2.pdf) • [7](https://doi.org/10.1364/josaa.1.000612) |\n\n## How it works\n\nThe Fourier slice theorem underpins the method: the one-dimensional [Fourier transform](https://www.edgepedia.org/fourier-transform) of a projection at angle \\( \\theta \\) equals the two-dimensional Fourier transform of the object along a line through the origin of frequency space, \\( \\hat{p}_{\\theta}(\\omega) = F(\\omega \\cos\\theta, \\omega \\sin\\theta) \\).[1](https://www2.compute.dtu.dk/~pcha/HDtomo/SC/Week1Days1and2.pdf) As projections accumulate over many angles they sample the two-dimensional spectrum, so the image can in principle be recovered by Fourier inversion. Back-projection alone, however, suppresses higher spatial frequencies by a factor \\( 1/|\\nu| \\), producing low-pass blurring; multiplying each projection by the ramp filter \\( |\\nu| \\) before back-projection corrects exactly this loss.[4](https://gray.mgh.harvard.edu/media/com_dpattachments/attachments/com_content.article/Image-Reconstruction-Part-I-Planar-Reconstruction-from-Projections.pdf) The algorithm then has four steps: Fourier transform each projection, multiply by \\( |\\nu| \\), inverse transform, and back-project, giving \\( f(x,y) = \\int_{0}^{\\pi} q_{\\theta}(x \\cos\\theta + y \\sin\\theta) \\, d\\theta \\).[4](https://gray.mgh.harvard.edu/media/com_dpattachments/attachments/com_content.article/Image-Reconstruction-Part-I-Planar-Reconstruction-from-Projections.pdf) • [1](https://www2.compute.dtu.dk/~pcha/HDtomo/SC/Week1Days1and2.pdf)\n\nThe ideal ramp is non-bounded and therefore not realizable, so in practice a window function \\( h(\\omega) \\) is multiplied with it to enforce zero response outside a chosen frequency range.[5](https://doi.org/10.1109/access.2024.3357355) Because the ramp is a high-pass filter, additional low-pass windows are needed whenever noise is present.[1](https://www2.compute.dtu.dk/~pcha/HDtomo/SC/Week1Days1and2.pdf) An equivalent formulation, backproject-then-filter, first back-projects the raw data and then applies a two-dimensional ramp filter.[8](https://unm.lf1.cuni.cz/trnka/IAEA/Chapter_13_Image_Reconstruction_text.pdf) Written as \\( f(x,y) = \\tfrac{1}{2} B\\, F^{-1}[|S|\\, F(Rf)(S,\\theta)](x,y) \\), the formula shows why FBP is numerically unstable and noise-sensitive: the \\( |S| \\) factor amplifies high frequencies.[9](https://doi.org/10.48550/arxiv.2312.02393)\n\n## How it is done\n\nA practitioner's pipeline runs as follows. Preprocessing converts measured intensities to line integrals with flat- and dark-field correction, \\( Z = -\\log((I - I_{D})/(I_{F} - I_{D})) \\), and applies a center-of-rotation correction, since standard FBP assumes a perfectly centered object.[1](https://www2.compute.dtu.dk/~pcha/HDtomo/SC/Week1Days1and2.pdf) Each projection is then Fourier transformed, multiplied by the windowed ramp, and inverse transformed; the filtered values are interpolated onto the ray positions and back-projected over all angles.[4](https://gray.mgh.harvard.edu/media/com_dpattachments/attachments/com_content.article/Image-Reconstruction-Part-I-Planar-Reconstruction-from-Projections.pdf)\n\nDiscretization needs care. A straightforward discrete ramp sets the DC component to zero, causing significant negative bias in the image; zero padding before the FFT, or implementing the ramp as a spatial-domain convolution kernel, reduces the problem.[8](https://unm.lf1.cuni.cz/trnka/IAEA/Chapter_13_Image_Reconstruction_text.pdf) Geometry changes the details. A fan-beam back-projection operator requires integration over 360°, not the 180° sufficient for parallel-beam geometry.[10](https://www.osti.gov/servlets/purl/7329565) For circular cone-beam data the approximate FDK algorithm is standard: it weights, filters, weights again, and back-projects, is exact in the mid-plane perpendicular to the rotation axis, has relatively small errors in many practical instances, and, unlike exact methods, handles data truncated in the longitudinal direction.[1](https://www2.compute.dtu.dk/~pcha/HDtomo/SC/Week1Days1and2.pdf) • [7](https://doi.org/10.1364/josaa.1.000612) Helical scanning is served by approximate Feldkamp-type extensions that reconstruct each transaxial slice from an arbitrary helix segment and apply redundancy weighting after filtering.[11](https://iopscience.iop.org/article/10.1088/0031-9155/49/13/011)\n\n## Origin\n\nThe first successful tomographic reconstruction, Hounsfield's EMI scanner, used a relaxed iterative algorithm that updated a density estimate with each projection measurement, reconstructing 100 × 100 pixel images from 400 views of 100 line integrals each; the first clinical brain tumor image was acquired in the early 1970s at Atkinson Morley Hospital.[2](https://pmc.ncbi.nlm.nih.gov/articles/PMC8492478/) • [12](https://faculty.wharton.upenn.edu/wp-content/uploads/2012/04/New-medical-xray-technology.pdf) That iterative procedure was too slow for minicomputers, so commercial EMI scanners shifted to an FBP-style method covered by an EMI patent.[2](https://pmc.ncbi.nlm.nih.gov/articles/PMC8492478/) It is now generally agreed that the convolutional algorithms are not only faster but give reconstructions with much better accuracy and spatial resolution than the earlier iterative procedures.[12](https://faculty.wharton.upenn.edu/wp-content/uploads/2012/04/New-medical-xray-technology.pdf) Shepp and Logan introduced the systematic study of filter choice in 1974 in \"The Fourier reconstruction of a head section,\" published in IEEE Transactions on Nuclear Science, which expressed the trade-off between spatial and density resolution in terms of that choice.[13](https://doi.org/10.1109/tns.1974.6499235) In 1979 the Nobel Prize for Medicine and [Physiology](https://www.edgepedia.org/physiology) was jointly awarded to Cormack and Hounsfield.[9](https://doi.org/10.48550/arxiv.2312.02393)\n\n## Variants\n\nThe main variants differ by filter kernel and by geometry. Ram-Lak is the ramp multiplied by a rectangular window; it can produce the [Gibbs phenomenon](https://www.edgepedia.org/gibbs-phenomenon), leading to ring artifacts. Smoother windows such as Hamming, Hann, Cosine, and Sine reduce noise and ringing but introduce blurring that lowers resolution.[5](https://doi.org/10.1109/access.2024.3357355) The Shepp-Logan filter applies a sinc window to the ramp.[4](https://gray.mgh.harvard.edu/media/com_dpattachments/attachments/com_content.article/Image-Reconstruction-Part-I-Planar-Reconstruction-from-Projections.pdf) In fan-beam work the Parzen filter was used to eliminate ringing from a sharp cut-off, and of the filters studied the Hann filter appeared to give the best results.[10](https://www.osti.gov/servlets/purl/7329565) Cone-beam and helical variants follow FDK and its extensions.[7](https://doi.org/10.1364/josaa.1.000612) • [11](https://iopscience.iop.org/article/10.1088/0031-9155/49/13/011)\n\nSince 2017 the filtering step itself has become trainable. Syben and colleagues showed in 2017 that a neural network initialized with the ideal ramp learns the proper discrete filter discretization, converging toward the Ram-Lak solution.[14](https://doi.org/10.48550/arxiv.1710.06287) DeepFBP, reported by Xi Tan and colleagues in 2024 in [IEEE Access](https://www.edgepedia.org/ieee-access), learns an optimized filter and nonlinear interpolation while keeping FBP's efficiency; it outperforms a TV-based statistical iterative algorithm with about two orders of magnitude less compute time and beats FBPConvNet and RED-CNN post-processing.[5](https://doi.org/10.1109/access.2024.3357355) Noise2Filter, by Lagerwerf and colleagues in 2020, provides self-supervised, real-time 3D reconstruction.[15](https://doi.org/10.1088/2632-2153/abbd4d)\n\n## Applications\n\nFBP and its modified versions such as FDK have been used in almost all fields of straight-ray tomography, including X-ray CT and PET.[16](https://link.springer.com/article/10.1186/1475-925X-12-50) Its speed is well documented: a single GPU runs FBP 23× faster than a 64-core multi-threaded CPU for a 1024 × 1024 image, with a DirectX implementation reaching an average 1531× speedup over single-threaded execution, and single-precision floating point is sufficient for the task.[6](http://christian.mendl.net/science/publications/FBP_Mendl_Eliuk_Noga_Boulanger_ELCVIA2013.pdf)\n\nClinically, FBP is noisier than iterative methods. In abdominal CT, objective noise was 14%–68% higher for FBP than for ASIR, while MBIR was up to 47% lower than ASIR and 58% lower than FBP, though MBIR needed 15–30 minutes per scan.[17](https://pubs.rsna.org/doi/10.1148/radiol.12112707) Deep-learning reconstruction showed dose reductions of 30%–71% versus hybrid iterative and more than 50% versus FBP.[18](https://pubs.rsna.org/doi/10.1148/radiol.221257) The statistical difference is structural: MBIR weights high-SNR samples more heavily, whereas analytical reconstruction treats all samples equally, and fully modeled iterative reconstruction is computationally intensive.[19](https://engineering.purdue.edu/~bouman/publications/orig-pdf/CRR-2013.pdf) The filtering step in FBP enhances noise, while iterative images are less noisy.[20](https://www.sciencedirect.com/science/article/abs/pii/S1934592519300607)\n\n## Limitations and alternatives\n\nThe main problems of FBP are noise and streak artifact.[21](https://elsevier-elibrary.com/contents/fullcontent/15189520/epubcontent_v2/OEBPS/xhtml/CHP0005_102-118_B9780323790635000148.xhtml) Small metallic objects induce streaking, and compared with algebraic reconstruction techniques FBP adapts poorly to missing data and partial occlusion, though those iterative alternatives cost more computation.[22](https://people.csail.mit.edu/bkph/courses/papers/Exact_Conebeam/Turbell_Thesis_FBP_2001.pdf) FBP needs many projections over the full angular range, tolerates noise only modestly, and cannot use prior knowledge such as non-negativity.[1](https://www2.compute.dtu.dk/~pcha/HDtomo/SC/Week1Days1and2.pdf) It is not designed for irregularly sampled data, where its application leads to severe artifacts.[23](https://link.springer.com/article/10.1007/s11220-026-00834-3) It also relies on simplifying assumptions, a point detector, and a restricted geometry, so detector cross-talk and beam hardening cannot be modeled, which worsens noise and artifacts at low dose or in large patients.[18](https://pubs.rsna.org/doi/10.1148/radiol.221257) • [27](https://www5.cs.fau.de/Forschung/Publikationen/2007/Dennerlein07-FFR.pdf)\n\nHow much dose iterative methods save over FBP is disputed. One coronary study reports iterative dose reductions of 32%–65% depending on body weight, and 55% for iDose4,[24](https://www.ajronline.org/doi/epdfplus/10.2214/AJR.11.7557) while phantom and human-observer studies of multiple commercial iterative methods found only marginal or small allowable dose reductions for low-contrast detection tasks.[3](https://www.imagewisely.org/Imaging-Modalities/Computed-Tomography/Image-Reconstruction-Techniques) Iterative images also carry their own trade-offs: MBIR's low-frequency \"plastic\" or \"blotchy\" noise texture can hamper detection of low-contrast tissue interfaces,[18](https://pubs.rsna.org/doi/10.1148/radiol.221257) and its local spatial resolution depends on the contrast and noise of surrounding structures.[3](https://www.imagewisely.org/Imaging-Modalities/Computed-Tomography/Image-Reconstruction-Techniques) FBP remains in use where its speed and stability matter, and filter choice still sets the noise-resolution balance.[25](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0349360)\n\n## References\n\n1. [X-ray Computed Tomography: Forward problem and FBP reconstruction (DTU course notes, J. S. Jørgensen)](https://www2.compute.dtu.dk/~pcha/HDtomo/SC/Week1Days1and2.pdf)\n2. [From EMI to AI: a brief history of commercial CT reconstruction algorithms](https://pmc.ncbi.nlm.nih.gov/articles/PMC8492478/)\n3. [Image Reconstruction Techniques | Image Wisely](https://www.imagewisely.org/Imaging-Modalities/Computed-Tomography/Image-Reconstruction-Techniques)\n4. [Image Reconstruction 1 – Planar reconstruction from projections (MGH lecture notes)](https://gray.mgh.harvard.edu/media/com_dpattachments/attachments/com_content.article/Image-Reconstruction-Part-I-Planar-Reconstruction-from-Projections.pdf)\n5. [Xi Tan and colleagues (2024). Deep Filtered Back Projection for CT Reconstruction. IEEE Access.](https://doi.org/10.1109/access.2024.3357355)\n6. [Comprehensive Analysis of High-Performance Computing Methods for Filtered Back-Projection](http://christian.mendl.net/science/publications/FBP_Mendl_Eliuk_Noga_Boulanger_ELCVIA2013.pdf)\n7. [Practical cone-beam algorithm (Feldkamp, Davis, Kress 1984), abstract record](https://doi.org/10.1364/josaa.1.000612)\n8. [IAEA Chapter 13: Image Reconstruction](https://unm.lf1.cuni.cz/trnka/IAEA/Chapter_13_Image_Reconstruction_text.pdf)\n9. [Lecture Notes on Computerized Tomography](https://doi.org/10.48550/arxiv.2312.02393)\n10. [Lawrence Berkeley Laboratory report on fan-beam back-projection filters](https://www.osti.gov/servlets/purl/7329565)\n11. [Exact and approximate algorithms for helical cone-beam CT (Physics in Medicine & Biology, 2004)](https://iopscience.iop.org/article/10.1088/0031-9155/49/13/011)\n12. [Computerized Tomography: The New Medical X-Ray Technology](https://faculty.wharton.upenn.edu/wp-content/uploads/2012/04/New-medical-xray-technology.pdf)\n13. [L. A. Shepp, B. F. Logan (1974). The Fourier reconstruction of a head section. IEEE Transactions on Nuclear Science.](https://doi.org/10.1109/tns.1974.6499235)\n14. [Syben, Christopher and colleagues (2017). Precision Learning: Reconstruction Filter Kernel Discretization. arXiv (Cornell University).](https://doi.org/10.48550/arxiv.1710.06287)\n15. [Lagerwerf, Rien and colleagues (2020). Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D computed tomography. Data Archiving and Networked Services (DANS).](https://doi.org/10.1088/2632-2153/abbd4d)\n16. [A novel scheme to design the filter for CT reconstruction using FBP algorithm](https://link.springer.com/article/10.1186/1475-925X-12-50)\n17. [Filtered Back Projection, ASIR, and MBIR in Abdominal CT: An Experimental Clinical Study](https://pubs.rsna.org/doi/10.1148/radiol.12112707)\n18. [Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects](https://pubs.rsna.org/doi/10.1148/radiol.221257)\n19. [Recent Advances in CT Image Reconstruction (chapter, Bouman & Pan)](https://engineering.purdue.edu/~bouman/publications/orig-pdf/CRR-2013.pdf)\n20. [Image reconstruction: Part 1 – understanding filtered back projection, noise and image acquisition](https://www.sciencedirect.com/science/article/abs/pii/S1934592519300607)\n21. [CT image reconstruction chapter (Elsevier e-library)](https://elsevier-elibrary.com/contents/fullcontent/15189520/epubcontent_v2/OEBPS/xhtml/CHP0005_102-118_B9780323790635000148.xhtml)\n22. [Cone-Beam Reconstruction Using Filtered Backprojection (Turbell thesis, 2001)](https://people.csail.mit.edu/bkph/courses/papers/Exact_Conebeam/Turbell_Thesis_FBP_2001.pdf)\n23. [Filter-Free Two-Stage Reconstruction for Low-Dose CT Across Regular and Irregular Sparse-Angle Sampling](https://link.springer.com/article/10.1007/s11220-026-00834-3)\n24. [Comparisons of Image Quality and Radiation Dose Between Iterative Reconstruction and Filtered Back Projection Reconstruction Algorithms in 256-MDCT Coronary Angiography](https://www.ajronline.org/doi/epdfplus/10.2214/AJR.11.7557)\n25. [Resolution–noise characteristics of common FDK filter kernels: A practical reference for preclinical cone-beam micro-CT (PLOS One, 2025)](https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0349360)\n26. [PMC6443602 (pmc.ncbi.nlm.nih.gov)](https://pmc.ncbi.nlm.nih.gov/articles/PMC6443602/)\n27. [www5.cs.fau.de](https://www5.cs.fau.de/Forschung/Publikationen/2007/Dennerlein07-FFR.pdf)\n\n---\n*Topic: Encyclopedia › Technology and the built world › Computing and digital systems › Artificial intelligence and data › Algorithms and computational methods › Numerical, string, and geometric algorithms*\n\n*Initially written Sep 29, 2026 · Reviewed: Sep 30, 2026 · Edited: Sep 30, 2026; Oct 25, 2026 · Last review: Sep 30, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n", "same_as": [ "https://engineering.purdue.edu/~bouman/publications/orig-pdf/CRR-2013.pdf" ], "url": "https://www.edgepedia.org/filtered-back-projection", "markdown_url": "https://www.edgepedia.org/filtered-back-projection.md", "license": { "name": "Edgepedia Community License 1.0", "url": "https://www.edgepedia.org/edgepedia/license", "summary": "Free with credit, commercial use included. AI training is open to everyone. For other uses, organizations over USD 100M in revenue or 100M monthly users license separately.", "spdx": "LicenseRef-Edgepedia-Community-1.0" }, "credit": "\"Filtered back projection\", Edgepedia (EdgeChat), https://www.edgepedia.org/filtered-back-projection. Edgepedia Community License 1.0.", "credit_md": "\"[Filtered back projection](https://www.edgepedia.org/filtered-back-projection)\", Edgepedia (EdgeChat), [https://www.edgepedia.org/filtered-back-projection](https://www.edgepedia.org/filtered-back-projection). [Edgepedia Community License 1.0](https://www.edgepedia.org/edgepedia/license).", "credit_html": "\"Filtered back projection\", Edgepedia (EdgeChat), https://www.edgepedia.org/filtered-back-projection. Edgepedia Community License 1.0.", "speakable": "Filtered back projection is a tomographic reconstruction algorithm that filters each projection with a ramp and back-projects it into a cross-sectional image, long the standard method in CT." }