PDF(Pubmed)
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.
CONCLUSIONS: 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.
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.
CONCLUSIONS: 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：对于剂量学，对全身SPECT/CT成像的需求，双头愤怒相机需要较长的采集时间，正在增加。在这里，我们评估了稀疏获取的投影，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：本研究包括16例患者，用177Lu-DOTATATE进行SPECT/CT成像（120个投影，120P)在四个时间点。设计并训练深度神经网络（CUSIP），以从30个获得的投影（30P）中编译90个SIP。120P,30P,并使用基于蒙特卡洛的OSEM重建重建了三个不同的CUSIP集（30P90SIP）（产生120P_rec，30P_rec,和CUSIP_recs）。视觉比较噪声水平。归一化均方根误差的定量测量，归一化平均绝对误差，峰值信噪比，和结构相似性进行了评估，对每个重建组的肾脏和骨髓吸收剂量进行估算。
结果：使用SIP在视觉上改善了噪声水平。所有定量测量都显示出CUSIP集和120P之间的高度相似性。线性回归显示，所有重建装置的肾脏和骨髓吸收剂量几乎完全一致，与120P_rec的剂量相比（R2≥0.97）。与120P_rec相比，肾脏吸收剂量的平均相对差异，对于所有重建集，在3%以内。对于骨髓吸收剂量，相对差异有更高的耗散，CUSIP_recs的平均相对差异优于30P_rec(4%以内，9%)。30P_rec的肾脏和骨髓吸收剂量与120_rec的有统计学意义。与最佳表现的CUSIP_rec的吸收剂量相反，没有发现统计学上的显著差异。
结论：进行SPECT/CT重建时，使用SIP可以大大减少SPECT/CT成像中的采集持续时间，能够以令人满意的剂量精度采集高图像质量的多个视场。
方法：本研究包括16例患者，用177Lu-DOTATATE进行SPECT/CT成像（120个投影，120P)在四个时间点。设计并训练深度神经网络（CUSIP），以从30个获得的投影（30P）中编译90个SIP。120P,30P,并使用基于蒙特卡洛的OSEM重建重建了三个不同的CUSIP集（30P90SIP）（产生120P_rec，30P_rec,和CUSIP_recs）。视觉比较噪声水平。归一化均方根误差的定量测量，归一化平均绝对误差，峰值信噪比，和结构相似性进行了评估，对每个重建组的肾脏和骨髓吸收剂量进行估算。
结果：使用SIP在视觉上改善了噪声水平。所有定量测量都显示出CUSIP集和120P之间的高度相似性。线性回归显示，所有重建装置的肾脏和骨髓吸收剂量几乎完全一致，与120P_rec的剂量相比（R2≥0.97）。与120P_rec相比，肾脏吸收剂量的平均相对差异，对于所有重建集，在3%以内。对于骨髓吸收剂量，相对差异有更高的耗散，CUSIP_recs的平均相对差异优于30P_rec(4%以内，9%)。30P_rec的肾脏和骨髓吸收剂量与120_rec的有统计学意义。与最佳表现的CUSIP_rec的吸收剂量相反，没有发现统计学上的显著差异。
结论：进行SPECT/CT重建时，使用SIP可以大大减少SPECT/CT成像中的采集持续时间，能够以令人满意的剂量精度采集高图像质量的多个视场。
