Abadi Ehsan, Segars W Paul, Felice Nicholas, Sotoudeh-Paima Saman, Hoffman Eric A, Wang Xiao, Wang Wei, Clark Darin, Ye Siqi, Jadick Giavanna, Fryling Milo, Frush Donald P, Samei Ehsan
Center for Virtual Imaging Trial, Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA.
Medical Physics Graduate Program, Duke University, Durham, North Carolina, USA.
Med Phys. 2025 Apr;52(4):1978-1990. doi: 10.1002/mp.17619. Epub 2025 Jan 14.
This Special Report summarizes the 2022, AAPM grand challenge on Truth-based CT image reconstruction.
To provide an objective framework for evaluating CT reconstruction methods using virtual imaging resources consisting of a library of simulated CT projection images of a population of human models with various diseases.
Two hundred unique anthropomorphic, computational models were created with varied diseases consisting of 67 emphysema, 67 lung lesions, and 66 liver lesions. The organs were modeled based on clinical CT images of real patients. The emphysematous regions were modeled using segmentations from patient CT cases in the COPDGene Phase I dataset. For the lung and liver lesion cases, 1-6 malignant lesions were created and inserted into the human models, with lesion diameters ranging from 5.6 to 21.9 mm for lung lesions and 3.9 to 14.9 mm for liver lesions. The contrast defined between the liver lesions and liver parenchyma was 82 ± 12 HU, ranging from 50 to 110 HU. Similarly, the contrast between the lung lesions and the lung parenchyma was defined as 781 ± 11 HU, ranging from 725 to 805 HU. For the emphysematous regions, the defined HU values were -950 ± 17 HU ranging from -918 to -979 HU. The developed human models were imaged with a validated CT simulator. The resulting CT sinograms were shared with the participants. The participants reconstructed CT images from the sinograms and sent back their reconstructed images. The reconstructed images were then scored by comparing the results against the corresponding ground truth values. The scores included both task-generic (root mean square error [RMSE] and structural similarity matrix [SSIM]), and task-specific (detectability index [d'] and lesion volume accuracy) metrics. For the cases with multiple lesions, the measured metric was averaged across all the lesions. To combine the metrics with each other, each metric was normalized to a range of 0 to 1 per disease type, with "0" and "1" being the worst and best measured values across all cases of the disease type for all received reconstructions.
The True-CT challenge attracted 52 participants, out of which 5 successfully completed the challenge and submitted the requested 200 reconstructions. Across all participants and disease types, SSIM absolute values ranged from 0.22 to 0.90, RMSE from 77.6 to 490.5 HU, d' from 0.1 to 64.6, and volume accuracy ranged from 1.2 to 753.1 mm. The overall scores demonstrated that participant "A" had the best performance in all categories, except for the metrics of d' for lung lesions and RMSE for liver lesions. Participant "A" had an average normalized score of 0.41 ± 0.22, 0.48 ± 0.32, and 0.42 ± 0.33 for the emphysema, lung lesion, and liver lesion cases, respectively.
The True-CT challenge successfully enabled objective assessment of CT reconstructions with the unique advantage of access to a diverse population of diseased human models with known ground truth. This study highlights the significant potential of virtual imaging trials in objective assessment of medical imaging technologies.
本特别报告总结了2022年美国医学物理学会（AAPM）基于真实情况的CT图像重建重大挑战。
提供一个客观框架，以使用虚拟成像资源评估CT重建方法，该虚拟成像资源由包含患有各种疾病的人类模型群体的模拟CT投影图像库组成。
创建了200个独特的具有不同疾病的人体计算模型，其中包括67个肺气肿模型、67个肺部病变模型和66个肝脏病变模型。这些器官基于真实患者的临床CT图像进行建模。肺气肿区域使用慢性阻塞性肺疾病基因（COPDGene）I期数据集中患者CT病例的分割结果进行建模。对于肺部和肝脏病变病例，创建并将1 - 6个恶性病变插入人体模型中，肺部病变的直径范围为5.6至21.9毫米，肝脏病变的直径范围为3.9至14.9毫米。肝脏病变与肝实质之间定义的对比度为82±12亨氏单位（HU），范围为50至110 HU。同样，肺部病变与肺实质之间的对比度定义为781±11 HU，范围为725至805 HU。对于肺气肿区域，定义的HU值为 - 950±17 HU，范围为 - 918至 - 979 HU。使用经过验证的CT模拟器对开发的人体模型进行成像。将得到的CT正弦图与参与者共享。参与者从正弦图重建CT图像并返回其重建图像。然后通过将结果与相应的真实值进行比较对重建图像进行评分。评分包括通用任务指标（均方根误差[RMSE]和结构相似性矩阵[SSIM]）以及特定任务指标（可检测性指数[d']和病变体积准确性）。对于有多个病变的病例，测量指标在所有病变上进行平均。为了将这些指标相互结合，每个指标针对每种疾病类型归一化到0至1的范围，其中“0”和“1”分别是该疾病类型所有接收重建的所有病例中的最差和最佳测量值。
真实CT挑战吸引了52名参与者，其中5人成功完成挑战并提交了所需的200次重建结果。在所有参与者和疾病类型中，SSIM绝对值范围为0.22至0.90，RMSE为77.6至490.5 HU，d'为0.1至64.6，体积准确性范围为1.2至753.1毫米。总体评分表明，参与者“A”在所有类别中表现最佳，但肺部病变的d'指标和肝脏病变的RMSE指标除外。参与者“A”在肺气肿、肺部病变和肝脏病变病例中的平均归一化评分为0.41±0.22、0.48±0.32和0.42±0.33。
真实CT挑战成功实现了对CT重建的客观评估，其独特优势在于能够获取具有已知真实情况的各种患病人体模型群体。本研究突出了虚拟成像试验在医学成像技术客观评估中的巨大潜力。
