Sequential deep learning image enhancement models improve diagnostic confidence, lesion detectability, and image reconstruction time in PET.

IF 3 2区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING EJNMMI Physics Pub Date : 2024-03-15 DOI:10.1186/s40658-024-00632-4

Meghi Dedja, Abolfazl Mehranian, Kevin M Bradley, Matthew D Walker, Patrick A Fielding, Scott D Wollenweber, Robert Johnsen, Daniel R McGowan

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Abstract

Background: 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.

Results: 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_max 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.

Conclusions: 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.

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序列深度学习图像增强模型提高了 PET 的诊断可信度、病灶可探测性和图像重建时间。

背景：研究两种深度学习（DL）算法，即深度学习增强（DLE）和基于深度学习的飞行时间（ToF）（DLT）的顺序部署的潜在好处。DLE 旨在增强快速重建的有序子集期望最大化算法（OSEM）图像，使其趋向于块序列正则化期望最大化算法（BSREM）图像，而 DLT 则旨在提高无 ToF 重建的 BSREM 图像的质量。由于这两种算法的目的不同，顺序应用可将各自的优势结合起来。在 Discovery 710（D710）和 Discovery MI（DMI；均为 GE HealthCare）上分别进行了 20 次 FDG PET-CT 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。为了评估图像噪声，在肺部和肝脏绘制了直径为30毫米的球形VOI，以测量体积内体素的标准偏差。在临床盲读中，两位经验丰富的读者根据病变可探测性、诊断可信度和图像质量，用李克特五点量表对图像进行评分：结果：应用 DLE + DLT 降低了噪声，同时提高了病变可探测性、诊断信心和图像重建时间。ToF-OSEM + DLE + DLT重建显示，在D710和DMI上获得的数据，病变SUVmax分别增加了28±14%（平均值±标准偏差）和11±5%。同样的重建在病灶可探测性和诊断可信度方面的临床读数中，D710得分最高：结论：与 ToF-BSREM 图像相比，DLE 和 DLT 的组合提高了诊断可信度和病变可探测性。由于 DLE + DLT 使用的是输入的 OSEM 图像，而且 DL 推断速度很快，因此整体重建时间显著缩短。这可应用于全身 PET。

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来源期刊

EJNMMI Physics Physics and Astronomy-Radiation

CiteScore

6.70

自引率

10.00%

发文量

审稿时长

13 weeks

期刊介绍： EJNMMI Physics is an international platform for scientists, users and adopters of nuclear medicine with a particular interest in physics matters. As a companion journal to the European Journal of Nuclear Medicine and Molecular Imaging, this journal has a multi-disciplinary approach and welcomes original materials and studies with a focus on applied physics and mathematics as well as imaging systems engineering and prototyping in nuclear medicine. This includes physics-driven approaches or algorithms supported by physics that foster early clinical adoption of nuclear medicine imaging and therapy.