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Synthetic CT: simulating low dose single and dual energy protocols from a dual energy scan.

机译:合成CT:从双能量扫描模拟低剂量单能量和双能量方案。

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PURPOSE: The choice of CT protocol can greatly impact patient dose and image quality. Since acquiring multiple scans at different techniques on a given patient is undesirable, the ability to predict image quality changes starting from a high quality exam can be quite useful. While existing methods allow one to generate simulated images of lower exposure (mAs) from an acquired CT exam, the authors present and validate a new method called synthetic CT that can generate realistic images of a patient at arbitrary low dose protocols (kVp, mAs, and filtration) for both single and dual energy scans. METHODS: The synthetic CT algorithm is derived by carefully ensuring that the expected signal and noise are accurate for the simulated protocol. The method relies on the observation that the material decomposition from a dual energy CT scan allows the transmission of an arbitrary spectrum to be predicted. It requires an initial dual energy scan of the patient to either synthesize raw projections of a single energy scan or synthesize the material decompositions of a dual energy scan. The initial dual energy scan contributes inherent noise to the synthesized projections that must be accounted for before adding more noise to simulate low dose protocols. Therefore, synthetic CT is subject to the constraint that the synthesized data have noise greater than the inherent noise. The authors experimentally validated the synthetic CT algorithm across a range of protocols using a dual energy scan of an acrylic phantom with solutions of different iodine concentrations. An initial 80/140 kVp dual energy scan of the phantom provided the material decomposition necessary to synthesize images at 100 kVp and at 120 kVp, across a range of mAs values. They compared these synthesized single energy scans of the phantom to actual scans at the same protocols. Furthermore, material decompositions of a 100/120 kVp dual energy scan are synthesized by adding correlated noise to the initial material decompositions. The aforementioned noise constraint also allows us to compute feasible mAs values that can be synthesized for each kVp. RESULTS: The single energy synthesized and actual reconstructed images exhibit identical signal and noise properties at 100 kVp and at 120 kVp, and across a range of mAs values. For example, the noise in both the synthesized and actual images at 100 kVp increases by 2 when the mAs is halved. The synthesized and actual material decompositions of a dual energy protocol show excellent agreement when the decomposition images are linearly weighted to form monoenergetic images at energies from 40 to 100 keV. For simulated single energy protocols with kVp between 80 and 140, the highest feasible mAs exceeds that of either initial scan. CONCLUSIONS: This work describes and validates the synthetic CT theory and algorithm by comparing its results to actual scans. Synthetic CT is a powerful new tool that allows users to realistically see how protocol selection affects CT images and enables radiologists to retrospectively identify the lowest dose protocol achievable that provides diagnostic quality images on real patients.
机译:目的:CT方案的选择会极大地影响患者的剂量和图像质量。由于不希望以给定患者使用不同技术进行多次扫描,因此从高质量检查开始预测图像质量变化的能力可能会非常有用。虽然现有方法允许人们从获得的CT检查中生成较低暴露(mAs)的模拟图像,但作者提出并验证了一种称为合成CT的新方法,该方法可以在任意低剂量方案(kVp,mAs,和过滤)进行单能量和双能量扫描。方法:通过仔细确保预期的信号和噪声对于模拟协议是准确的,得出合成CT算法。该方法依赖于以下观察结果:双能CT扫描产生的材料分解可以预测任意光谱的透射。它需要对患者进行初始双能量扫描,以合成单能量扫描的原始投影或合成双能量扫描的材料分解。初始双能量扫描将固有噪声贡献给合成的投影,在添加更多的噪声以模拟低剂量方案之前必须考虑这些噪声。因此,合成CT受到以下限制:合成数据具有比固有噪声更大的噪声。作者使用丙烯酸模型的双能扫描和不同碘浓度的溶液,通过一系列协议对合成CT算法进行了实验验证。幻像的初始80/140 kVp双能扫描提供了在100 mAp和120 kVp范围内的mAs值范围内合成图像所需的材料分解。他们将人体模型的这些合成的单能扫描与相同协议下的实际扫描进行了比较。此外,通过将相关噪声添加到初始材料分解中来合成100/120 kVp双能扫描的材料分解。前面提到的噪声约束也使我们能够计算可行的mAs值,可以针对每个kVp进行合成。结果:单能量合成图像和实际重建图像在100 kVp和120 kVp以及整个mAs值范围内显示相同的信号和噪声特性。例如,当mAs减半时,在100 kVp的合成图像和实际图像中的噪声都会增加2。当分解图像被线性加权以形成能量为40到100 keV的单能图像时,双重能量协议的合成和实际材料分解显示出极好的一致性。对于kVp在80到140之间的模拟单能协议,最高可行mAs超过了任一初始扫描的mAs。结论:这项工作通过将其结果与实际扫描结果进行比较来描述和验证合成CT理论和算法。合成CT是功能强大的新工具,它使用户可以现实地查看协议选择如何影响CT图像,并使放射科医生能够回顾性地确定可实现的最低剂量协议,从而为真实患者提供诊断质量的图像。

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