使用扩张卷积神经网络从低计数图像中进行全计数PET恢复。

Full-count PET recovery from low-count image using a dilated convolutional neural network.

作者信息

Spuhler Karl, Serrano-Sosa Mario, Cattell Renee, DeLorenzo Christine, Huang Chuan

机构信息

Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA.

Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA.

出版信息

Med Phys. 2020 Oct;47(10):4928-4938. doi: 10.1002/mp.14402. Epub 2020 Aug 6.

DOI:10.1002/mp.14402

PMID:32687608

Abstract

PURPOSE

Positron emission tomography (PET) is an essential technique in many clinical applications that allows for quantitative imaging at the molecular level. This study aims to develop a denoising method using a novel dilated convolutional neural network (CNN) to recover full-count images from low-count images.

METHODS

We adopted similar hierarchical structures as the conventional U-Net and incorporated dilated kernels in each convolution to allow the network to observe larger, more robust features within the image without the requirement of downsampling and upsampling internal representations. Our dNet was trained alongside a U-Net for comparison. Both models were evaluated using a leave-one-out cross-validation procedure on a dataset of 35 subjects (~3500 slabs), which were obtained from an ongoing F-Fluorodeoxyglucose (FDG) study. Low-count PET data (10% count) were generated by randomly selecting one-tenth of all events in the associated listmode file. Analysis was done on the static image from the last 10 minutes of emission data. Both low-count PET and full-count PET were reconstructed using ordered subset expectation maximization (OSEM). Objective image quality metrics, including mean absolute percent error (MAPE), peak signal-to-noise ratio (PSNR), and structural similarity index metric (SSIM), were used to analyze the deep learning methods. Both deep learning methods were further compared to a traditional Gaussian filtering method. Further, region of interest (ROI) quantitative analysis was also used to compare U-Net and dNet architectures.

RESULTS

Both the U-Net and our proposed network were successfully trained to synthesize full-count PET images from the generated low-count PET images. Compared to low-count PET and Gaussian filtering, both deep learning methods improved MAPE, PSNR, and SSIM. Our dNet also systematically outperformed U-Net on all three metrics (MAPE: 4.99 ± 0.68 vs 5.31 ± 0.76, P < 0.01; PSNR: 31.55 ± 1.31 dB vs 31.05 ± 1.39, P < 0.01; SSIM: 0.9513 ± 0.0154 vs 0.9447 ± 0.0178, P < 0.01). ROI quantification showed greater quantitative improvements using dNet over U-Net.

CONCLUSION

This study proposed a novel approach of using dilated convolutions for recovering full-count PET images from low-count PET images.

摘要

目的

正电子发射断层扫描（PET）是许多临床应用中的一项重要技术，可在分子水平进行定量成像。本研究旨在开发一种去噪方法，使用新型扩张卷积神经网络（CNN）从低计数图像中恢复全计数图像。

方法

我们采用了与传统U-Net类似的层次结构，并在每个卷积中引入扩张卷积核，使网络能够在无需对内部表示进行下采样和上采样的情况下，观察图像中更大、更稳健的特征。我们的dNet与U-Net一起训练以进行比较。两种模型均使用留一法交叉验证程序在一个包含35名受试者（约3500个切片）的数据集上进行评估，这些数据来自正在进行的氟代脱氧葡萄糖（FDG）研究。通过随机选择关联列表模式文件中所有事件的十分之一来生成低计数PET数据（10%计数）。对发射数据最后10分钟的静态图像进行分析。低计数PET和全计数PET均使用有序子集期望最大化（OSEM）进行重建。使用包括平均绝对百分比误差（MAPE）、峰值信噪比（PSNR）和结构相似性指数（SSIM）在内的客观图像质量指标来分析深度学习方法。将这两种深度学习方法与传统高斯滤波方法进行进一步比较。此外，还使用感兴趣区域（ROI）定量分析来比较U-Net和dNet架构。

结果

U-Net和我们提出的网络均成功训练，可从生成的低计数PET图像中合成全计数PET图像。与低计数PET和高斯滤波相比，两种深度学习方法均改善了MAPE、PSNR和SSIM。我们的dNet在所有三个指标上也系统地优于U-Net（MAPE：4.99±0.68对5.31±0.76，P<0.01；PSNR：31.55±1.31dB对31.05±1.39，P<0.01；SSIM：0.9513±0.0154对0.9447±0.0178，P<0.01）。ROI定量分析表明，使用dNet比U-Net有更大的定量改善。