Temporal footprint reduction via neural network denoising in 177Lu radioligand therapy

IF 2.7 3区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Max Celio Nzatsi , Nicolas Varmenot , David Sarrut , Grégory Delpon , Michel Cherel , Caroline Rousseau , Ludovic Ferrer
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引用次数: 0

Abstract

Background

Internal vectorised therapies, particularly with [177Lu]-labelled agents, are increasingly used for metastatic prostate cancer and neuroendocrine tumours. However, routine dosimetry for organs-at-risk and tumours remains limited due to the complexity and time requirements of current protocols.

Method

We developed a Generative Adversarial Network (GAN) to transform rapid 6 s SPECT projections into synthetic 30 s-equivalent projections. SPECT data from twenty patients and phantom acquisitions were collected at multiple time-points.

Results

The GAN accurately predicted 30 s projections, enabling estimation of time-integrated activities in kidneys and liver with maximum errors below 6 % and 1 %, respectively, compared to standard acquisitions. For tumours and phantom spheres, results were more variable. On phantom data, GAN-inferred reconstructions showed lower biases for spheres of 20, 8, and 1 mL (8.2 %, 6.9 %, and 21.7 %) compared to direct 6 s acquisitions (12.4 %, 20.4 %, and 24.0 %). However, in patient lesions, 37 segmented tumours showed higher median discrepancies in cumulated activity for the GAN (15.4 %) than for the 6 s approach (4.1 %).

Conclusion

Our preliminary results indicate that the GAN can provide reliable dosimetry for organs-at-risk, but further optimisation is needed for small lesion quantification. This approach could reduce SPECT acquisition time from 45 to 9 min for standard three-bed studies, potentially facilitating wider adoption of dosimetry in nuclear medicine and addressing challenges related to toxicity and cumulative absorbed doses in personalised radiopharmaceutical therapy.
基于神经网络去噪的177Lu放射治疗中时间足迹的减少
背景:内矢量化治疗,特别是[177Lu]标记药物,越来越多地用于转移性前列腺癌和神经内分泌肿瘤。然而,由于目前方案的复杂性和时间要求,对高危器官和肿瘤的常规剂量测定仍然有限。方法利用生成式对抗网络(GAN)将快速的6秒SPECT投影转化为合成的30秒等效投影。在多个时间点收集20例患者的SPECT数据和幻影采集。结果:与标准获取相比,GAN准确地预测了30秒的预测,能够估计肾脏和肝脏的时间集成活动,最大误差分别低于6%和1%。对于肿瘤和幻球,结果则更加多变。在幻影数据上,gan推断重建显示,与直接获得的6s数据(12.4%,20.4%和24.0%)相比,20ml, 8 mL和1ml球体的偏差较低(8.2%,6.9%和21.7%)。然而,在患者病变中,37个节段性肿瘤显示GAN累积活性的中位数差异(15.4%)高于6s入路(4.1%)。结论初步结果表明,氮化镓可以为高危器官提供可靠的剂量测定,但对小病变的定量还需进一步优化。这种方法可以将标准三床研究的SPECT采集时间从45分钟减少到9分钟,潜在地促进了剂量学在核医学中的广泛应用,并解决了个性化放射性药物治疗中与毒性和累积吸收剂量相关的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.80
自引率
14.70%
发文量
493
审稿时长
78 days
期刊介绍: Physica Medica, European Journal of Medical Physics, publishing with Elsevier from 2007, provides an international forum for research and reviews on the following main topics: Medical Imaging Radiation Therapy Radiation Protection Measuring Systems and Signal Processing Education and training in Medical Physics Professional issues in Medical Physics.
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