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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Traverse angle computations are a critical component of surveying, used to compute the internal angles within a closed traverse. A traverse consists of a series of connected lines forming a closed loop, often used for land boundary delineation or mapping. Calculating the internal angles ensures accuracy in the traverse geometry and is essential for checking survey data integrity.The process begins with known azimuths and bearings of the traverse sides. Internal angles at each vertex are...
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Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

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The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
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The Role of Ion Channels in Neuronal Computation01:19

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A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
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Imaging Studies I: CT and MRI01:14

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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
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コンピュータ用MRIにおける物理駆動型AIのためのエッジコンピューティング:実現可能性研究

Yaşar Utku Alçalar1, Yu Cao1, Mehmet Akçakaya1

  • 1College of Science and Engineering, University of Minnesota, Minneapolis, USA.

International Conference on Future Internet of Things and Cloud : FiCloud. International Conference on Future Internet of Things and Cloud
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PubMed
まとめ
この要約は機械生成です。

物理的に駆動されたAI MRI再構築はスキャンを加速しますが,大規模なデータを作成します. この新しい方法は,8ビット定量化を使用してエッジデバイスのためのAIを最適化し,より高速で高解像度の画像の品質を損なうことなく効率を改善します.

キーワード:
人工知能 (AI) は,人工知能 (AI) を利用する.MRI (MRI) とはMRI (MRI) を意味する.コンピューティング・イメージングエッジコンピューティング (Edge Computing) とは定量化・量化化について

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科学分野:

  • メディカルイマージング (医学イメージング)
  • 人工知能 (AI) とは,人工知能 (AI) のことです.
  • エッジコンピューティング (Edge Computing) とは

背景:

  • 物理的に駆動されたAI (PD-AI) はMRIスキャンを加速し,より高い解像度を可能にします.
  • 高解像度MRIは大量のデータを生成し,特に機能MRIでは,伝送,保存,処理を困難にします.
  • FPGAによるエッジコンピューティングは,近接センサーのPD-AI再構築のためのソリューションを提供していますが,ハードウェア効率の良いモデルが必要です.

研究 の 目的:

  • FPGAベースのエッジコンピューティングに最適化された新しいPD-AI計算式MRIアプローチを提案する.
  • 8ビットの複雑なデータの定量化とFFT/IFFT操作の排除を通じてハードウェアの効率を高めること.

主な方法:

  • FPGAエッジデバイスに合わせたPD-AI計算式MRIアプローチを開発しました.
  • モデル最適化のための8ビット複合データ定量化を実装.
  • 冗余のFast Fourier Transform (FFT) とInverse FFT (IFFT) 操作を削除しました.

主要な成果:

  • 従来の PD-AI 方法と比較して,計算効率が向上しました.
  • 既存のPD-AI技術と比較できる再構築品質を維持しました.
  • 再建の品質と効率において標準的な臨床MRI方法を上回った.

結論:

  • 提案されたPD-AIアプローチは,リソースが制限されたエッジデバイスの高解像度MRI再構築を可能にします.
  • この戦略は,高解像度のMRIにおけるデータボトルネックに対処し,現実世界の展開を容易にします.
  • 最適化されたPD-AIモデルは,高度な医療イメージングにおける効率的なエッジコンピューティングに不可欠です.