# Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review
## 3 Results
### Study Outcomes
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Study** & **Aim** & **Dataset (Availability)** & **Ultrasound Specifications** & **Deep Learning Algorithm** & **Loss Function** \\ \hline Avasthi & Reconstruction of & **Phantom:** Five separate datasets, & Versonics: L11-5v transducer with & Residual Encoder-Decoder & Scaled MSE \\ \(\pi\), & high-quality & tissue-mimicking, commercial, and in vitro porcine & PWs at range \(-25\lx@math@degree\) to \(25\lx@math@degree\): LQ & Net & \\
2022 [32] & high-bandwidth images & carotid artery (private). & Limited bandwidth down to \(20\lx@math@degree\), & Net & \\  & from low-bandwidth images. & & HQ Full bandwidth. & & \\ \hline [PERSON] et al. [2017] & Reconstruct high-quality US & **In vivo**: Carotid, thyroid, and liver regions of & Versonics: ATL L7-4 probe & CNN & L2 loss \\
2017 [33] & images from a small number & healthy subjects. & (5.2 MHz, 125 members) with range & & \\  & of PW acquisitions. & **Phantom:** Gammers (private). & a\(\pm 15\lx@math@degree\): LQ 3 PWs. HQ-31 PWs. & & \\ \hline [PERSON] et al. & Achieve the quality of & **Phantom:** CIRS phantom and ex vivo lamb liver. & E-CUBE 12 Alpinion machine. & Boundary-Seeking GAN & Binary cross-entropy \\ \(\pi\), & multivo US images by & **Simulation:** Field II software [34] (private). & L3-12 Hard transducer (8.5 MHz). LQ & & (discriminator, MSE \\
2020 [34] & using a mapping function on & & image with single focal point. HQ & & + boundary-seeking \\  & a single-focus US image. & & Multi-focus image with & loss (generator) \\ \hline [PERSON] et al. & Improve the quality of & **In vivo**: Dataset provided by [PERSON] et al. [42] & (Derived from dataset sources) & Local Global Unet (LC-Unet) & MSE + SSIM \\
2020 [35] & handheld US devices using a & (cardi artery and brachiodiasis images of & **In vivo**: Versonics: L10-5 probe & + Simplified Residual & (LC-Unet) and L1 \\  & small number of & healthy volunteers. & (5.7 MHz). LQ 3 PWs, HQ & Network (\(\&\)ResNet) & (S\({}_{\text{\_}}\)Resnet) \\  & plane waves. & **Phantom:** RCUs[7] dataset, CIRS phantom & Compound image of 31 PWs with range \(-15\lx@math@degree\) to \(15\lx@math@degree\): LQ & & \\  & Simulation: US images from natural images using & & range \(-15\lx@math@degree\) to \(15\lx@math@degree\): LQ & & \\  & Field I software (only for pre-training LC-Unet) & **Phantom:** Meta: Versonics: L11 & & & \\  & (private and public). & probe (5.2 MHz, 128 elements). & & \\ \hline [PERSON] et al. & Improve the quality of & **Simulation:** Field II software (private). & Simulation dataset at 5 MHz center & Context Encoder & Not reported \\
2018 [56] & ultrasonic B-mode images from 32 to that from & & frequency, 308 nm mph, \(71\lx@math@degree\), & Reconstruction GAN & \\  & 128 channels. & & HQ 128-channel image. & & \\ \hline [PERSON] et al. & Contrast and resolution & **In vivo**: Carotid and thyroid regions. & LQ- NPN056 portable US system & Cascade application of & Cycle consistency + \\
2021 [15] & enhancement of handheld & **Phantom:** ATS-539 phantom. & was used as low-quality input. HQ & unsupervised self-consistent & adversarial loss \\  & POCUS images. & **Simulation:** Intermediate domain images & E-CUBE 12R US, L3-12 transducer. & CycleGAN + supervised & (cycleGAN), \\  & generated by downgrading the in vivo and & & super-resolution network. & MAE + SSIM \\  & phantom images acquired from high-end system & (super-resolution network) \\ \hline \end{tabular}
\end{table} 
Table 2: Characteristics of selected studies. \begin{table}
\begin{tabular}{l l l l l l} \hline
**Study** & **Aim** & **Dataset (Availability)** & **Ultrasound Specifications** & **Deep Learning Algorithm** & **Loss Function** \\ \hline [PERSON] et al. & High-quality reconstruction & **In vivo:** Thigh muscle, finger phalanç, & Versanççççççççççççççççççççççççççççççççççççççççççççççççççççççççç