Department of Electrical Engineering and Radiology, Stanford University, Stanford, CA 94305, USA.
Med Phys. 2011 Oct;38(10):5551-62. doi: 10.1118/1.3633895.
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.
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.
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.
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.
CT 协议的选择会极大地影响患者的剂量和图像质量。由于在给定的患者身上获取不同技术的多次扫描是不可取的，因此能够从高质量的检查中预测图像质量的变化是非常有用的。虽然现有的方法允许从获取的 CT 检查中生成较低曝光（mAs）的模拟图像，但作者提出并验证了一种新的方法，称为合成 CT，它可以为单能和双能扫描生成任意低剂量协议（kVp、mAs 和过滤）的患者的逼真图像。
合成 CT 算法是通过仔细确保模拟协议的预期信号和噪声是准确的来推导的。该方法依赖于这样的观察，即从双能 CT 扫描的物质分解可以预测任意谱的透射。它需要对患者进行初始的双能扫描，以合成单能扫描的原始投影或合成双能扫描的物质分解。初始的双能扫描为合成投影贡献固有噪声，在添加更多噪声以模拟低剂量协议之前，必须对其进行核算。因此，合成 CT 受到以下约束：合成数据的噪声必须大于固有噪声。作者通过使用不同碘浓度溶液的亚克力体模的双能扫描，在一系列协议下对合成 CT 算法进行了实验验证。体模的初始 80/140 kVp 双能扫描提供了必要的物质分解，以在 100 kVp 和 120 kVp 范围内合成各种 mAs 值的图像。他们将这些模拟的单能扫描与相同协议下的实际扫描进行了比较。此外，通过向初始物质分解添加相关噪声，合成了 100/120 kVp 双能扫描的物质分解。上述噪声约束还允许我们计算出每个 kVp 可合成的可行 mAs 值。
在 100 kVp 和 120 kVp 以及各种 mAs 值下，合成的单能图像和实际重建图像具有相同的信号和噪声特性。例如，在 100 kVp 时，当 mAs 减半时，两者的噪声都增加了 2。当将分解图像线性加权为 40 到 100 keV 之间的单能图像时，双能协议的合成和实际物质分解具有极好的一致性。对于模拟的单能协议，kVp 在 80 到 140 之间，最高可行的 mAs 值超过初始扫描的任何一个。
本工作通过将其结果与实际扫描进行比较，描述和验证了合成 CT 理论和算法。合成 CT 是一种强大的新工具，它使用户能够真实地看到协议选择如何影响 CT 图像，并使放射科医生能够在回顾性地识别出在真实患者上提供诊断质量图像的最低剂量协议。
