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LpRec

Log-polar based method for tomography reconstruction. For details, see

Andersson, Fredrik, Marcus Carlsson, and Viktor V. Nikitin. "Fast algorithms and efficient GPU implementations for the Radon transform and the back-projection operator represented as convolution operators." SIAM Journal on Imaging Sciences 9.2 (2016): 637-664, (https://epubs.siam.org/doi/10.1137/15M1023762).

The proposed method is based on the fact that the Radon transform and its adjoint, the back-projection operator, can both be expressed as convolutions in log-polar coordinates. Hence, fast algorithms for the application of these operators can be constructed by using the FFT, if data is resampled at log-polar coordinates. In combination with GPU accelearation, the proposed scheme allowed for fastest Filtered Backprojection and Iterative reconstruction procedures.

Reconstruction by LpRec:

lprec.png

Installation

Install from source:

python setup.py install

Dependencies:

cupy, scikit-build
Tests:
See tests/ Run ./tests/run_test.py to perform all unit tests

Wrapper in tomopy

See tomopy/tomopy/recon/wrappers.py file for a wrapper to the lprec library. Also see tomopy/doc/demo/lprec.ipynb jupyter notebook for functionality demonstration. The notebook shows examples of reconstruction by FBP, gradient-descent, conjugate gradient, TV, and EM methods.

FBP and Iterative schemes

lprec/lpmethods.py module contains FBP reconstruciton function and iterative schemes implemented with using the log-polar based method. Iterative schemes are written in python with using cupy module for GPU acceleration of linear algebra operations. Access to gpu data inside the lprec library works via pointers to gpu memory.

Developers

Viktor Nikitin (vnikitin@anl.gov)

About

Log-polar based method for tomography reconstruciton

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LpRec
Log-polar based method for tomography reconstruction. For details, see
Andersson, Fredrik, Marcus Carlsson, and Viktor V. Nikitin. "Fast algorithms and efficient GPU implementations for the Radon transform and the back-projection operator represented as convolution operators." SIAM Journal on Imaging Sciences 9.2 (2016): 637-664, (https://epubs.siam.org/doi/10.1137/15M1023762).
The proposed method is based on the fact that the Radon transform and its adjoint, the back-projection operator, can both be expressed as convolutions in log-polar coordinates. Hence, fast algorithms for the application of these operators can be constructed by using the FFT, if data is resampled at log-polar coordinates. In combination with GPU accelearation, the proposed scheme allowed for fastest Filtered Backprojection and Iterative reconstruction procedures.
Reconstruction by LpRec:
lprec.png
Installation
Install from source:
python setup.py install

Dependencies:
cupy, scikit-build

Tests:
See tests/
Run ./tests/run_test.py to perform all unit tests
Wrapper in tomopy
See tomopy/tomopy/recon/wrappers.py file for a wrapper to the lprec library. Also see tomopy/doc/demo/lprec.ipynb jupyter notebook for functionality demonstration. The notebook shows examples of reconstruction by FBP, gradient-descent, conjugate gradient, TV, and EM methods.
FBP and Iterative schemes
lprec/lpmethods.py module contains FBP reconstruciton function and iterative schemes implemented with using the log-polar based method. Iterative schemes are written in python with using cupy module for GPU acceleration of linear algebra operations. Access to gpu data inside the lprec library works via pointers to gpu memory.
Developers
Viktor Nikitin (vnikitin@anl.gov)
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LpRec
Log-polar based method for tomography reconstruction. For details, see
Andersson, Fredrik, Marcus Carlsson, and Viktor V. Nikitin. "Fast algorithms and efficient GPU implementations for the Radon transform and the back-projection operator represented as convolution operators." SIAM Journal on Imaging Sciences 9.2 (2016): 637-664, (https://epubs.siam.org/doi/10.1137/15M1023762).
The proposed method is based on the fact that the Radon transform and its adjoint, the back-projection operator, can both be expressed as convolutions in log-polar coordinates. Hence, fast algorithms for the application of these operators can be constructed by using the FFT, if data is resampled at log-polar coordinates. In combination with GPU accelearation, the proposed scheme allowed for fastest Filtered Backprojection and Iterative reconstruction procedures.
Reconstruction by LpRec:
lprec.png
Installation
Install from source:
python setup.py install

Dependencies:
cupy, scikit-build

Tests:
See tests/
Run ./tests/run_test.py to perform all unit tests
Wrapper in tomopy
See tomopy/tomopy/recon/wrappers.py file for a wrapper to the lprec library. Also see tomopy/doc/demo/lprec.ipynb jupyter notebook for functionality demonstration. The notebook shows examples of reconstruction by FBP, gradient-descent, conjugate gradient, TV, and EM methods.
FBP and Iterative schemes
lprec/lpmethods.py module contains FBP reconstruciton function and iterative schemes implemented with using the log-polar based method. Iterative schemes are written in python with using cupy module for GPU acceleration of linear algebra operations. Access to gpu data inside the lprec library works via pointers to gpu memory.
Developers
Viktor Nikitin (vnikitin@anl.gov)
About
Log-polar based method for tomography reconstruciton
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var __re = new RegExp('^' + ".*" + '

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LpRec
Log-polar based method for tomography reconstruction. For details, see
Andersson, Fredrik, Marcus Carlsson, and Viktor V. Nikitin. "Fast algorithms and efficient GPU implementations for the Radon transform and the back-projection operator represented as convolution operators." SIAM Journal on Imaging Sciences 9.2 (2016): 637-664, (https://epubs.siam.org/doi/10.1137/15M1023762).
The proposed method is based on the fact that the Radon transform and its adjoint, the back-projection operator, can both be expressed as convolutions in log-polar coordinates. Hence, fast algorithms for the application of these operators can be constructed by using the FFT, if data is resampled at log-polar coordinates. In combination with GPU accelearation, the proposed scheme allowed for fastest Filtered Backprojection and Iterative reconstruction procedures.
Reconstruction by LpRec:
lprec.png
Installation
Install from source:
python setup.py install

Dependencies:
cupy, scikit-build

Tests:
See tests/
Run ./tests/run_test.py to perform all unit tests
Wrapper in tomopy
See tomopy/tomopy/recon/wrappers.py file for a wrapper to the lprec library. Also see tomopy/doc/demo/lprec.ipynb jupyter notebook for functionality demonstration. The notebook shows examples of reconstruction by FBP, gradient-descent, conjugate gradient, TV, and EM methods.
FBP and Iterative schemes
lprec/lpmethods.py module contains FBP reconstruciton function and iterative schemes implemented with using the log-polar based method. Iterative schemes are written in python with using cupy module for GPU acceleration of linear algebra operations. Access to gpu data inside the lprec library works via pointers to gpu memory.
Developers
Viktor Nikitin (vnikitin@anl.gov)
About
Log-polar based method for tomography reconstruciton
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injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
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})();
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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LpRec
Log-polar based method for tomography reconstruction. For details, see
Andersson, Fredrik, Marcus Carlsson, and Viktor V. Nikitin. "Fast algorithms and efficient GPU implementations for the Radon transform and the back-projection operator represented as convolution operators." SIAM Journal on Imaging Sciences 9.2 (2016): 637-664, (https://epubs.siam.org/doi/10.1137/15M1023762).
The proposed method is based on the fact that the Radon transform and its adjoint, the back-projection operator, can both be expressed as convolutions in log-polar coordinates. Hence, fast algorithms for the application of these operators can be constructed by using the FFT, if data is resampled at log-polar coordinates. In combination with GPU accelearation, the proposed scheme allowed for fastest Filtered Backprojection and Iterative reconstruction procedures.
Reconstruction by LpRec:
lprec.png
Installation
Install from source:
python setup.py install

Dependencies:
cupy, scikit-build

Tests:
See tests/
Run ./tests/run_test.py to perform all unit tests
Wrapper in tomopy
See tomopy/tomopy/recon/wrappers.py file for a wrapper to the lprec library. Also see tomopy/doc/demo/lprec.ipynb jupyter notebook for functionality demonstration. The notebook shows examples of reconstruction by FBP, gradient-descent, conjugate gradient, TV, and EM methods.
FBP and Iterative schemes
lprec/lpmethods.py module contains FBP reconstruciton function and iterative schemes implemented with using the log-polar based method. Iterative schemes are written in python with using cupy module for GPU acceleration of linear algebra operations. Access to gpu data inside the lprec library works via pointers to gpu memory.
Developers
Viktor Nikitin (vnikitin@anl.gov)
About
Log-polar based method for tomography reconstruciton
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LpRec
Log-polar based method for tomography reconstruction. For details, see
Andersson, Fredrik, Marcus Carlsson, and Viktor V. Nikitin. "Fast algorithms and efficient GPU implementations for the Radon transform and the back-projection operator represented as convolution operators." SIAM Journal on Imaging Sciences 9.2 (2016): 637-664, (https://epubs.siam.org/doi/10.1137/15M1023762).
The proposed method is based on the fact that the Radon transform and its adjoint, the back-projection operator, can both be expressed as convolutions in log-polar coordinates. Hence, fast algorithms for the application of these operators can be constructed by using the FFT, if data is resampled at log-polar coordinates. In combination with GPU accelearation, the proposed scheme allowed for fastest Filtered Backprojection and Iterative reconstruction procedures.
Reconstruction by LpRec:
lprec.png
Installation
Install from source:
python setup.py install

Dependencies:
cupy, scikit-build

Tests:
See tests/
Run ./tests/run_test.py to perform all unit tests
Wrapper in tomopy
See tomopy/tomopy/recon/wrappers.py file for a wrapper to the lprec library. Also see tomopy/doc/demo/lprec.ipynb jupyter notebook for functionality demonstration. The notebook shows examples of reconstruction by FBP, gradient-descent, conjugate gradient, TV, and EM methods.
FBP and Iterative schemes
lprec/lpmethods.py module contains FBP reconstruciton function and iterative schemes implemented with using the log-polar based method. Iterative schemes are written in python with using cupy module for GPU acceleration of linear algebra operations. Access to gpu data inside the lprec library works via pointers to gpu memory.
Developers
Viktor Nikitin (vnikitin@anl.gov)
About
Log-polar based method for tomography reconstruciton
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injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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LpRec
Log-polar based method for tomography reconstruction. For details, see
Andersson, Fredrik, Marcus Carlsson, and Viktor V. Nikitin. "Fast algorithms and efficient GPU implementations for the Radon transform and the back-projection operator represented as convolution operators." SIAM Journal on Imaging Sciences 9.2 (2016): 637-664, (https://epubs.siam.org/doi/10.1137/15M1023762).
The proposed method is based on the fact that the Radon transform and its adjoint, the back-projection operator, can both be expressed as convolutions in log-polar coordinates. Hence, fast algorithms for the application of these operators can be constructed by using the FFT, if data is resampled at log-polar coordinates. In combination with GPU accelearation, the proposed scheme allowed for fastest Filtered Backprojection and Iterative reconstruction procedures.
Reconstruction by LpRec:
lprec.png
Installation
Install from source:
python setup.py install

Dependencies:
cupy, scikit-build

Tests:
See tests/
Run ./tests/run_test.py to perform all unit tests
Wrapper in tomopy
See tomopy/tomopy/recon/wrappers.py file for a wrapper to the lprec library. Also see tomopy/doc/demo/lprec.ipynb jupyter notebook for functionality demonstration. The notebook shows examples of reconstruction by FBP, gradient-descent, conjugate gradient, TV, and EM methods.
FBP and Iterative schemes
lprec/lpmethods.py module contains FBP reconstruciton function and iterative schemes implemented with using the log-polar based method. Iterative schemes are written in python with using cupy module for GPU acceleration of linear algebra operations. Access to gpu data inside the lprec library works via pointers to gpu memory.
Developers
Viktor Nikitin (vnikitin@anl.gov)
About
Log-polar based method for tomography reconstruciton
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injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
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LpRec
Log-polar based method for tomography reconstruction. For details, see
Andersson, Fredrik, Marcus Carlsson, and Viktor V. Nikitin. "Fast algorithms and efficient GPU implementations for the Radon transform and the back-projection operator represented as convolution operators." SIAM Journal on Imaging Sciences 9.2 (2016): 637-664, (https://epubs.siam.org/doi/10.1137/15M1023762).
The proposed method is based on the fact that the Radon transform and its adjoint, the back-projection operator, can both be expressed as convolutions in log-polar coordinates. Hence, fast algorithms for the application of these operators can be constructed by using the FFT, if data is resampled at log-polar coordinates. In combination with GPU accelearation, the proposed scheme allowed for fastest Filtered Backprojection and Iterative reconstruction procedures.
Reconstruction by LpRec:
lprec.png
Installation
Install from source:
python setup.py install

Dependencies:
cupy, scikit-build

Tests:
See tests/
Run ./tests/run_test.py to perform all unit tests
Wrapper in tomopy
See tomopy/tomopy/recon/wrappers.py file for a wrapper to the lprec library. Also see tomopy/doc/demo/lprec.ipynb jupyter notebook for functionality demonstration. The notebook shows examples of reconstruction by FBP, gradient-descent, conjugate gradient, TV, and EM methods.
FBP and Iterative schemes
lprec/lpmethods.py module contains FBP reconstruciton function and iterative schemes implemented with using the log-polar based method. Iterative schemes are written in python with using cupy module for GPU acceleration of linear algebra operations. Access to gpu data inside the lprec library works via pointers to gpu memory.
Developers
Viktor Nikitin (vnikitin@anl.gov)
About
Log-polar based method for tomography reconstruciton
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