From a9bda2f672d7f012dd04f957351d9396cca0cc17 Mon Sep 17 00:00:00 2001 From: towzeur Date: Tue, 27 Feb 2024 18:24:17 +0100 Subject: [PATCH] ico --- index.html | 107 +++++++++++++++++++++------------- static/images/favicon.ico | Bin 12014 -> 53742 bytes static/images/favicon_old.ico | Bin 0 -> 12014 bytes static/images/teaser.png | Bin 248383 -> 750414 bytes 4 files changed, 65 insertions(+), 42 deletions(-) create mode 100644 static/images/favicon_old.ico diff --git a/index.html b/index.html index e5c7186..256ae03 100644 --- a/index.html +++ b/index.html @@ -44,6 +44,26 @@ + + @@ -124,22 +144,6 @@

QN-Mixer: A Quasi-Newton MLP-Mixer Mode - -
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- Teaser Image -

- The paper introduces a novel neural network called QN-Mixer, which employs a latent BFGS algorithm - to approximate the Hessian matrix with a deep-net learned regularization term. - It outperforms state-of-the-art methods in terms of quantitative metrics while requiring fewer - iterations than first-order unrolling networks. -

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@@ -176,36 +180,54 @@

Abstract

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+

Sparse-View Reconstruction Challenges

+
+ + problem +

+ Computed tomography (CT) is a widely used imaging modality in medical diagnosis and treatment planning, + delivering intricate anatomical details of the human body with precision. Despite its success, CT is + associated with high radiation doses, which can increase the risk of cancer induction. + Adhering to the ALARA principle (As Low As Reasonably Achievable), the medical community emphasizes + minimizing + radiation exposure to the lowest level necessary for accurate diagnosis. + Numerous approaches have been proposed to reduce radiation doses while maintaining image quality. + Among these, sparse-view CT emerges as a promising solution, effectively lowering radiation doses by + subsampling the projection data, often referred to as the sinogram. + Nonetheless, reconstructed images using the well-known Filtered Back Projection (FBP) algorithm suffer from + pronounced streaking artifacts, which can lead to misdiagnosis. + The challenge of effectively reconstructing high-quality CT images from sparse-view data is + gaining increasing attention in both the computer vision and medical imaging communities. +

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+ + + +
-
-

Sparse-View Reconstruction Challenges

- problem -

- Computed tomography (CT) is a widely used imaging modality in medical diagnosis and treatment planning, - delivering intricate anatomical details of the human body with precision. Despite its success, CT is - associated with high radiation doses, which can increase the risk of cancer induction. - Adhering to the ALARA principle (As Low As Reasonably Achievable), the medical community emphasizes - minimizing - radiation exposure to the lowest level necessary for accurate diagnosis. - Numerous approaches have been proposed to reduce radiation doses while maintaining image quality. - Among these, sparse-view CT emerges as a promising solution, effectively lowering radiation doses by - subsampling the projection data, often referred to as the sinogram. - Nonetheless, reconstructed images using the well-known Filtered Back Projection (FBP) algorithm suffer from - pronounced streaking artifacts, which can lead to misdiagnosis. - The challenge of effectively reconstructing high-quality CT images from sparse-view data is - gaining increasing attention in both the computer vision and medical imaging communities. -

-
+

Proposed method

+ + Proposed method + +

+ The paper introduces a novel neural network called QN-Mixer, which employs a latent BFGS algorithm + to approximate the Hessian matrix with a deep-net learned regularization term. + It outperforms state-of-the-art methods in terms of quantitative metrics while requiring fewer + iterations than first-order unrolling networks. +

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- -
+

Methodology

@@ -228,36 +250,37 @@

Methodology

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+
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Visual Comparison

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