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通过使用深度学习生成的合成投影进行重建,改善从稀疏采集投影中获得的肺部单光子发射计算机断层扫描(SPECT)图像。

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

作者信息

Wikberg Emma, Essen Martijn van, Rydén Tobias, Svensson Johanna, Gjertsson Peter, Bernhardt Peter

机构信息

Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.

Medical Physics and Medical Bioengineering, Sahlgrenska University Hospital, Gothenburg, Sweden.

出版信息

EJNMMI Phys. 2024 Jun 28;11(1):53. doi: 10.1186/s40658-024-00655-x.

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 Lu-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 (R ≥ 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.

摘要

背景

在剂量测定中,对全身单光子发射计算机断层扫描/计算机断层扫描(SPECT/CT)成像的需求不断增加,而使用双头安格尔相机进行此类成像需要较长的采集时间。在此,我们评估了稀疏采集的投影,并评估了添加深度学习生成的合成中间投影(SIP)是否可以在保持剂量测定准确性的同时提高图像质量。

方法

本研究纳入了16例接受镥-奥曲肽治疗并在四个时间点进行SPECT/CT成像(120个投影,120P)的患者。设计并训练了深度神经网络(CUSIP),以从30个采集的投影(30P)编译90个SIP。使用基于蒙特卡洛的有序子集最大期望值(OSEM)重建方法对120P、30P和三个不同的CUSIP集(30P + 90个SIP)进行重建(分别得到120P_rec、30P_rec和CUSIP_recs)。通过视觉比较噪声水平。评估了归一化均方根误差、归一化平均绝对误差、峰值信噪比和结构相似性的定量指标,并估计了每个重建集的肾脏和骨髓吸收剂量。

结果

使用SIP在视觉上改善了噪声水平。所有定量指标均显示CUSIP集与120P之间具有高度相似性。线性回归表明,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(R≥0.97)。与120P_rec相比,所有重建集的肾脏吸收剂量的平均相对差异在3%以内。对于骨髓吸收剂量,相对差异的离散度更高,并且CUSIP_recs在平均相对差异方面优于30P_rec(与9%相比在4%以内)。30P_rec的肾脏和骨髓吸收剂量与120_rec的剂量在统计学上有显著差异,而表现最佳的CUSIP_rec的吸收剂量则未发现统计学上的显著差异。

结论

在进行SPECT/CT重建时,使用SIP可以大幅缩短SPECT/CT成像的采集时间,从而能够采集多个高质量视野的图像,同时保持令人满意的剂量测定准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c659/11213840/9a937126b37f/40658_2024_655_Fig1_HTML.jpg

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