Improvements of 177Lu SPECT images from sparsely acquired projections by reconstruction with deep-learning-generated synthetic projections.

IF 3 2区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING EJNMMI Physics Pub Date : 2024-06-28 DOI:10.1186/s40658-024-00655-x
Emma Wikberg, Martijn van Essen, Tobias Rydén, Johanna Svensson, Peter Gjertsson, Peter Bernhardt
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Abstract

Background: For dosimetry, the demand for whole-body SPECT/CT imaging, which require long acquisition durations with dual-head Anger cameras, is increasing. Here we evaluated sparsely acquired projections and assessed whether the addition of deep-learning-generated synthetic intermediate projections (SIPs) could improve the image quality while preserving dosimetric accuracy.

Methods: This study included 16 patients treated with 177Lu-DOTATATE with SPECT/CT imaging (120 projections, 120P) at four time points. Deep neural networks (CUSIPs) were designed and trained to compile 90 SIPs from 30 acquired projections (30P). The 120P, 30P, and three different CUSIP sets (30P + 90 SIPs) were reconstructed using Monte Carlo-based OSEM reconstruction (yielding 120P_rec, 30P_rec, and CUSIP_recs). The noise levels were visually compared. Quantitative measures of normalised root mean square error, normalised mean absolute error, peak signal-to-noise ratio, and structural similarity were evaluated, and kidney and bone marrow absorbed doses were estimated for each reconstruction set.

Results: The use of SIPs visually improved noise levels. All quantitative measures demonstrated high similarity between CUSIP sets and 120P. Linear regression showed nearly perfect concordance of the kidney and bone marrow absorbed doses for all reconstruction sets, compared to the doses of 120P_rec (R2 ≥ 0.97). Compared to 120P_rec, the mean relative difference in kidney absorbed dose, for all reconstruction sets, was within 3%. For bone marrow absorbed doses, there was a higher dissipation in relative differences, and CUSIP_recs outperformed 30P_rec in mean relative difference (within 4% compared to 9%). Kidney and bone marrow absorbed doses for 30P_rec were statistically significantly different from those of 120_rec, as opposed to the absorbed doses of the best performing CUSIP_rec, where no statistically significant difference was found.

Conclusion: When performing SPECT/CT reconstruction, the use of SIPs can substantially reduce acquisition durations in SPECT/CT imaging, enabling acquisition of multiple fields of view of high image quality with satisfactory dosimetric accuracy.

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利用深度学习生成的合成投影进行重建,从稀疏获取的投影中改进 177Lu SPECT 图像。
背景:在剂量测定方面,对全身 SPECT/CT 成像的需求日益增加,而这种成像需要使用双头 Anger 相机进行长时间的采集。在此,我们评估了稀疏采集投影,并评估了添加深度学习生成的合成中间投影(SIP)是否能在保持剂量测定准确性的同时提高图像质量:这项研究包括16名接受177Lu-DOTATATE治疗的患者,他们在四个时间点接受了SPECT/CT成像(120个投影,120P)。设计并训练了深度神经网络(CUSIP),以便从 30 个获得的投影(30P)中编辑 90 个 SIP。使用基于蒙特卡洛的 OSEM 重建技术对 120P、30P 和三种不同的 CUSIP 集(30P + 90 SIPs)进行重建(得出 120P_rec、30P_rec 和 CUSIP_recs)。对噪声水平进行了目测比较。对归一化均方根误差、归一化平均绝对误差、峰值信噪比和结构相似性进行了定量评估,并估算了每组重建的肾脏和骨髓吸收剂量:结果:SIP 的使用明显改善了噪声水平。所有定量指标均显示 CUSIP 集与 120P 之间具有高度相似性。线性回归显示,与 120P_rec 的剂量相比,所有重建集的肾脏和骨髓吸收剂量几乎完全一致(R2 ≥ 0.97)。与 120P_rec 相比,所有重建组的肾脏吸收剂量的平均相对差异在 3% 以内。就骨髓吸收剂量而言,相对差异的消散程度较高,CUSIP_recs 在平均相对差异方面优于 30P_rec(在 4% 以内,而 30P_rec 为 9%)。30P_rec与120_rec的肾脏和骨髓吸收剂量在统计学上有显著差异,而表现最好的CUSIP_rec的吸收剂量在统计学上没有显著差异:结论:在进行SPECT/CT重建时,使用SIP可大大缩短SPECT/CT成像的采集时间,从而采集到多视场的高图像质量和令人满意的剂量测定精度。
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来源期刊
EJNMMI Physics
EJNMMI Physics Physics and Astronomy-Radiation
CiteScore
6.70
自引率
10.00%
发文量
78
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.
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