# Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
## 3 Results
### Optimizing MRI Brain Imaging and Analysis  
Assessing imaging quality and optimizing image acquisition are significant for medical imaging analysis. Reconstruction techniques adjust the scanning parameters to maximize the image quality and control the scanning time, which is of great benefit for pediatric imaging in which many subjects cannot stay still for a long time [148]. Furthermore, some scans may be missing or with low quality due to inadequate scanning time or fail completion by the participants. Image generation algorithms synthesize pseudo-images from low-resolution image or latent space, which provide a solution to recapture missing data or rectify scans with low quality [149]. Here, we review the deep learning algorithms for image quality assessment, reconstruction, and synthesis (Table 6).  
Image quality assessment tools were constructed with 2D CNN for structural MRI and DTI [150; 151; 152]. Study [153] utilized a two-stage transfer learning strategy which showed near-perfect accuracy in evaluating image quality and is capable of real-time large-scale assessment. GANs are widely applied in image generation tasks [149; 154; 155; 156; 157]. GANs showed great capability in generating synthetic images to implement missing data or improve the signal-to-noise ratio of poor quality images [24; 149]. Study [148] proposed CNN models for reconstruction which reduced the scan time by 42% while maintaining image quality and lesion detectability. CNN combined with RNN also showed superior performance in improving the signal-to-noise ratio [24].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Year** & **Task** & **Population** & **Technique** & **Preprocessing Method** & **Results** \\ \hline
[158] & 2020 & Image & \begin{tabular}{c} 131 neuro-oncology \\ patients \\ (age 0.4–17.1 years) \\ \end{tabular} & \
\begin{tabular}{c} \multirow{2}{*}{ASL} \\ - \\ \end{tabular} & \multirow{2}{*}{-} & Auto- &
\begin{tabular}{c} SNR Gain \\ 62\% \\ \end{tabular} \\ \hline
[159] & 2018 & \begin{tabular}{c} 28 infants (scan at birth, \\ \end{tabular} & \
\begin{tabular}{c} \\ 3 months, and 6 \\ months) \\ \end{tabular} & DTI & FSL & CNN &
\begin{tabular}{c} MAE \\ from \\ neonates) \\ \end{tabular} \\ \hline
[154] & 2019 & \begin{tabular}{c} Image \\ Generation \\ \end{tabular} & \
\begin{tabular}{c} 16 subjects \\ (age 1.1–21.3 years) \\ \end{tabular} & sMRI & - & GAN &
\begin{tabular}{c} MAE \\ 52.4 \(\pm\) 17.6 \\ \end{tabular} \\ \hline
[155] & 2020 & \begin{tabular}{c} Image \\ Generation \\ \end{tabular} & \begin{tabular}{c} 60 subjects \\ (age 2.6–19 years) \\ \end{tabular} & sMRI & \
\begin{tabular}{c} In-house \\ pipeline \\ \end{tabular} & GAN &
\begin{tabular}{c} MAE \\ 61.0 \(\pm\) 14.1 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimizing MRI brain imaging and analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Study** & **Year** & **Score** & **Population** & **Technique** & **Preprocessing** & **Method** & **Results** \\ \hline
[144] & 2020 & Motor &
\begin{tabular}{c} 77 very pre-term infants \\ (gestation age \textless{}31 weeks ) \\ \end{tabular} & DTI & ANTS & CNN & Accuracy 73\% \\ \hline
[139] & 2021 & Oral Reading &
\begin{tabular}{c} ABCD Study 5252 subjects \\ (age 9–10 years) \\ \end{tabular} & sMRI, DTI & - & Auto-encoder & MSE 206.5 \\ \hline  & & & Abbreviations: sMRI—structural MRI, rs-fMRI—resting-state functional MRI, DTI—Diffusion Tensor Imaging, CNN—Convolutional neural network, MAE—mean absolute error, MSE—mean squared error. & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._