PDF(Pubmed)
Abstract：
To develop a partially interpretable neural network for joint suppression of banding and flow artifacts in non-phase-cycled bSSFP cine imaging.
A dual-stage neural network consisting of a voxel-identification (VI) sub-network and artifact-suppression (AS) sub-network is proposed. The VI sub-network provides identification of artifacts, which guides artifact suppression and improves interpretability. The AS sub-network reduces banding and flow artifacts. Short-axis cine images of 12 frequency offsets from 28 healthy subjects were used to train and test the dual-stage network. An additional 77 patients were retrospectively enrolled to evaluate its clinical generalizability. For healthy subjects, artifact suppression performance was analyzed by comparison with traditional phase cycling. The partial interpretability provided by the VI sub-network was analyzed via correlation analysis. Generalizability was evaluated for cine obtained with different sequence parameters and scanners. For patients, artifact suppression performance and partial interpretability of the network were qualitatively evaluated by 3 clinicians. Cardiac function before and after artifact suppression was assessed via left ventricular ejection fraction (LVEF).
For the healthy subjects, visual inspection and quantitative analysis found a considerable reduction of banding and flow artifacts by the proposed network. Compared with traditional phase cycling, the proposed network improved flow artifact scores (4.57 ± 0.23 vs 3.40 ± 0.38, P = 0.002) and overall image quality (4.33 ± 0.22 vs 3.60 ± 0.38, P = 0.002). The VI sub-network well identified the location of banding and flow artifacts in the original movie and significantly correlated with the change of signal intensities in these regions. Changes of imaging parameters or the scanner did not cause a significant change of overall image quality relative to the baseline dataset, suggesting a good generalizability. For the patients, qualitative analysis showed a significant improvement of banding artifacts (4.01 ± 0.50 vs 2.77 ± 0.40, P < 0.001), flow artifacts (4.22 ± 0.38 vs 2.97 ± 0.57, P < 0.001), and image quality (3.91 ± 0.45 vs 2.60 ± 0.43, P < 0.001) relative to the original cine. The artifact suppression slightly reduced the LVEF (mean bias = -1.25%, P = 0.01).
The dual-stage network simultaneously reduces banding and flow artifacts in bSSFP cine imaging with a partial interpretability, sparing the need for sequence modification. The method can be easily deployed in a clinical setting to identify artifacts and improve cine image quality.
A dual-stage neural network consisting of a voxel-identification (VI) sub-network and artifact-suppression (AS) sub-network is proposed. The VI sub-network provides identification of artifacts, which guides artifact suppression and improves interpretability. The AS sub-network reduces banding and flow artifacts. Short-axis cine images of 12 frequency offsets from 28 healthy subjects were used to train and test the dual-stage network. An additional 77 patients were retrospectively enrolled to evaluate its clinical generalizability. For healthy subjects, artifact suppression performance was analyzed by comparison with traditional phase cycling. The partial interpretability provided by the VI sub-network was analyzed via correlation analysis. Generalizability was evaluated for cine obtained with different sequence parameters and scanners. For patients, artifact suppression performance and partial interpretability of the network were qualitatively evaluated by 3 clinicians. Cardiac function before and after artifact suppression was assessed via left ventricular ejection fraction (LVEF).
For the healthy subjects, visual inspection and quantitative analysis found a considerable reduction of banding and flow artifacts by the proposed network. Compared with traditional phase cycling, the proposed network improved flow artifact scores (4.57 ± 0.23 vs 3.40 ± 0.38, P = 0.002) and overall image quality (4.33 ± 0.22 vs 3.60 ± 0.38, P = 0.002). The VI sub-network well identified the location of banding and flow artifacts in the original movie and significantly correlated with the change of signal intensities in these regions. Changes of imaging parameters or the scanner did not cause a significant change of overall image quality relative to the baseline dataset, suggesting a good generalizability. For the patients, qualitative analysis showed a significant improvement of banding artifacts (4.01 ± 0.50 vs 2.77 ± 0.40, P < 0.001), flow artifacts (4.22 ± 0.38 vs 2.97 ± 0.57, P < 0.001), and image quality (3.91 ± 0.45 vs 2.60 ± 0.43, P < 0.001) relative to the original cine. The artifact suppression slightly reduced the LVEF (mean bias = -1.25%, P = 0.01).
The dual-stage network simultaneously reduces banding and flow artifacts in bSSFP cine imaging with a partial interpretability, sparing the need for sequence modification. The method can be easily deployed in a clinical setting to identify artifacts and improve cine image quality.
摘要：
目的：开发一种部分可解释的神经网络，用于联合抑制非相位循环bSSFP电影成像中的条带和流动伪影。
方法：提出了由体素识别（VI）子网络和伪影抑制（AS）子网络组成的双级神经网络。VI子网络提供工件的识别，它引导伪影抑制并提高可解释性。AS子网络减少了条带和流伪影。来自28名健康受试者的12个频率偏移的短轴电影图像用于训练和测试双级网络。另外77例患者被回顾性纳入以评估其临床普遍性。对于健康的受试者，通过与传统相位循环的比较，分析了伪影抑制性能。通过相关性分析对VI子网络提供的部分可解释性进行了分析。对使用不同序列参数和扫描仪获得的电影进行了通用性评估。对于患者来说,3名临床医师对网络的伪影抑制性能和部分可解释性进行了定性评估.通过左心室射血分数（LVEF）评估伪影抑制前后的心功能。
结果：对于健康受试者，视觉检查和定量分析发现，所提出的网络大大减少了条带和流动伪影。与传统相位循环相比，所提出的网络改善了血流伪影评分(4.57±0.23vs3.40±0.38,P=0.002)和总体图像质量(4.33±0.22vs3.60±0.38,P=0.002).VI子网络很好地识别了原始电影中条带和流动伪影的位置，并与这些区域中信号强度的变化显着相关。成像参数或扫描仪的变化没有引起相对于基线数据集的整体图像质量的显著变化，表明了良好的概括性。对病人来说,定性分析显示条带伪影显著改善(4.01±0.50vs2.77±0.40,P<0.001),流动伪影（4.22±0.38vs2.97±0.57，P<0.001），相对于原始电影，图像质量（3.91±0.45vs2.60±0.43，P<0.001）。伪影抑制略微降低了LVEF(平均偏差=-1.25%，P=0.01）。
结论：双阶段网络可同时减少bSSFP电影成像中的条带和流动伪影，具有部分可解释性，保留序列修改的需要。该方法可以容易地部署在临床环境中以识别伪影并提高电影图像质量。
方法：提出了由体素识别（VI）子网络和伪影抑制（AS）子网络组成的双级神经网络。VI子网络提供工件的识别，它引导伪影抑制并提高可解释性。AS子网络减少了条带和流伪影。来自28名健康受试者的12个频率偏移的短轴电影图像用于训练和测试双级网络。另外77例患者被回顾性纳入以评估其临床普遍性。对于健康的受试者，通过与传统相位循环的比较，分析了伪影抑制性能。通过相关性分析对VI子网络提供的部分可解释性进行了分析。对使用不同序列参数和扫描仪获得的电影进行了通用性评估。对于患者来说,3名临床医师对网络的伪影抑制性能和部分可解释性进行了定性评估.通过左心室射血分数（LVEF）评估伪影抑制前后的心功能。
结果：对于健康受试者，视觉检查和定量分析发现，所提出的网络大大减少了条带和流动伪影。与传统相位循环相比，所提出的网络改善了血流伪影评分(4.57±0.23vs3.40±0.38,P=0.002)和总体图像质量(4.33±0.22vs3.60±0.38,P=0.002).VI子网络很好地识别了原始电影中条带和流动伪影的位置，并与这些区域中信号强度的变化显着相关。成像参数或扫描仪的变化没有引起相对于基线数据集的整体图像质量的显著变化，表明了良好的概括性。对病人来说,定性分析显示条带伪影显著改善(4.01±0.50vs2.77±0.40,P<0.001),流动伪影（4.22±0.38vs2.97±0.57，P<0.001），相对于原始电影，图像质量（3.91±0.45vs2.60±0.43，P<0.001）。伪影抑制略微降低了LVEF(平均偏差=-1.25%，P=0.01）。
结论：双阶段网络可同时减少bSSFP电影成像中的条带和流动伪影，具有部分可解释性，保留序列修改的需要。该方法可以容易地部署在临床环境中以识别伪影并提高电影图像质量。
