Dedja Meghi, Mehranian Abolfazl, Bradley Kevin M, Walker Matthew D, Fielding Patrick A, Wollenweber Scott D, Johnsen Robert, McGowan Daniel R
Oxford University Hospitals, Oxford, UK.
GE HealthCare, Oxford, UK.
EJNMMI Phys. 2024 Mar 15;11(1):28. doi: 10.1186/s40658-024-00632-4.
Investigate the potential benefits of sequential deployment of two deep learning (DL) algorithms namely DL-Enhancement (DLE) and DL-based time-of-flight (ToF) (DLT). DLE aims to enhance the rapidly reconstructed ordered-subset-expectation-maximisation algorithm (OSEM) images towards block-sequential-regularised-expectation-maximisation (BSREM) images, whereas DLT aims to improve the quality of BSREM images reconstructed without ToF. As the algorithms differ in their purpose, sequential application may allow benefits from each to be combined. 20 FDG PET-CT scans were performed on a Discovery 710 (D710) and 20 on Discovery MI (DMI; both GE HealthCare). PET data was reconstructed using five combinations of algorithms:1. ToF-BSREM, 2. ToF-OSEM + DLE, 3. OSEM + DLE + DLT, 4. ToF-OSEM + DLE + DLT, 5. ToF-BSREM + DLT. To assess image noise, 30 mm-diameter spherical VOIs were drawn in both lung and liver to measure standard deviation of voxels within the volume. In a blind clinical reading, two experienced readers rated the images on a five-point Likert scale based on lesion detectability, diagnostic confidence, and image quality.
Applying DLE + DLT reduced noise whilst improving lesion detectability, diagnostic confidence, and image reconstruction time. ToF-OSEM + DLE + DLT reconstructions demonstrated an increase in lesion SUV of 28 ± 14% (average ± standard deviation) and 11 ± 5% for data acquired on the D710 and DMI, respectively. The same reconstruction scored highest in clinical readings for both lesion detectability and diagnostic confidence for D710.
The combination of DLE and DLT increased diagnostic confidence and lesion detectability compared to ToF-BSREM images. As DLE + DLT used input OSEM images, and because DL inferencing was fast, there was a significant decrease in overall reconstruction time. This could have applications to total body PET.
研究依次部署两种深度学习（DL）算法，即DL增强（DLE）算法和基于DL的飞行时间（ToF）算法（DLT）的潜在益处。DLE旨在将快速重建的有序子集期望最大化算法（OSEM）图像增强为块序贯正则化期望最大化（BSREM）图像，而DLT旨在提高未使用ToF重建的BSREM图像的质量。由于这两种算法目的不同，依次应用可能会使二者的优势得以结合。在Discovery 710（D710）上进行了20次FDG PET-CT扫描，在Discovery MI（DMI；均为通用电气医疗集团产品）上进行了20次扫描。PET数据使用五种算法组合进行重建：1. ToF-BSREM，2. ToF-OSEM + DLE，3. OSEM + DLE + DLT，4. ToF-OSEM + DLE + DLT，5. ToF-BSREM + DLT。为评估图像噪声，在肺和肝脏中均绘制了直径30毫米的球形感兴趣区（VOI），以测量该体积内体素的标准差。在一次盲法临床读片中，两名经验丰富的阅片者基于病变可检测性、诊断置信度和图像质量，采用五点李克特量表对图像进行评分。
应用DLE + DLT可降低噪声，同时提高病变可检测性、诊断置信度和图像重建时间。对于在D710和DMI上采集的数据，ToF-OSEM + DLE + DLT重建显示病变SUV分别增加了28±14%（平均值±标准差）和11±5%。对于D710，相同的重建在临床读片中的病变可检测性和诊断置信度方面得分最高。
与ToF-BSREM图像相比，DLE和DLT的组合提高了诊断置信度和病变可检测性。由于DLE + DLT使用输入的OSEM图像，且由于DL推理速度快，总体重建时间显著减少。这可能适用于全身PET检查。
