Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Literature Search
2.1.1. Search Strategy
2.1.2. Eligibility Criteria
2.1.3. Selection Procedure
2.2. Categorization According to Quality Enhancement Aspects
Quality Enhancement Aspect | Definition |
---|---|
1. Spatial resolution | The ability to differentiate two adjacent structures as being distinct from one another: either parallel (axial resolution) or perpendicular (lateral resolution) to the direction of the ultrasound beam [29]. |
2. Contrast resolution | The ability to distinguish between different echo amplitudes of adjacent structures through image intensity variations [29]. |
3. Detail enhancement of structures | Enhancement of texture, edges, or boundaries between structures. |
4. Noise | Minimization of random variability that is not part of the desired signal. |
5. General quality improvement | Mapping low-quality images to high-quality reference images, where the quality disparities are inherent to differences in the capture process and not artificially induced. |
2.3. Data Extraction
2.4. Statistical Analysis
3. Results
3.1. Study Selection
3.2. Quality Enhancement Aspects
3.3. Study Characteristics
3.4. Study Outcomes
Study | Aim | Dataset (Availability) | Ultrasound Specifications | Deep Learning Algorithm | Loss Function |
---|---|---|---|---|---|
Awasthi et al., 2022 [32] | Reconstruction of high-quality high-bandwidth images from low-bandwidth images. | Phantom: Five separate datasets, tissue-mimicking, commercial, and in vitro porcin carotid artery (private). | Verasonics: L11-5v transducer with PWs at range −25° to 25°. LQ: Limited bandwidth down to 20%, HQ: Full bandwidth. | Residual Encoder–Decoder Net | Scaled MSE |
Gasse et al., 2017 [33] | Reconstruct high-quality US images from a small number of PW acquisitions. | In vivo: Carotid, thyroid, and liver regions of healthy subjects. Phantom: Gammex (private). | Verasonics: ATL L7-4 probe (5.2 MHz, 128 elements) with range ±15°. LQ: 3 PWs. HQ: 31 PWs. | CNN | L2 loss |
Goudarzi et al., 2020 [34] | Achieve the quality of multifocus US images by using a mapping function on a single-focus US image. | Phantom: CIRS phantom and ex vivo lamb liver. Simulation: Field II software [34] (private). | E-CUBE 12 Alpinion machine: L3-12H transducer (8.5 MHz). LQ: image with single focal point. HQ: Multi-focus image with 3 focal points. | Boundary-Seeking GAN | Binary cross-entropy (discriminator), MSE + boundary-seeking loss (generator) |
Guo et al., 2020 [35] | Improve the quality of handheld US devices using a small number of plane waves. | In vivo: Dataset provided by Zhang et al. [42] (carotid artery and brachioradialis images of healthy volunteers). Phantom: PICMUS [97] dataset, CIRS phantom. Simulation: US images from natural images using Field II software (only for pre-training LG-Unet) (private and public). | (Derived from dataset sources) In vivo: Verasonics: L10-5 probe (7.5 MHz). LQ: 3 PWs, HQ: Compounded image of 31 PWs with range −15° to 15°. Phantom data: Verasonics: L11 probe (5.2 MHz, 128 elements). | Local Global Unet (LG-Unet) + Simplified Residual Network (S_ResNet) | MSE + SSIM (LG-Unet) and L1 (S_Resnet) |
Huang et al., 2018 [36] | Improve the quality of ultrasonic B-mode images from 32 to that from 128 channels. | Simulation: Field II software (private). | Simulation dataset at 5 MHz center frequency, 0.308 mm pitch, 71% bandwidth. LQ: 32-channel image. HQ: 128-channel image. | Context Encoder Reconstruction GAN | Not reported |
Khan et al., 2021 [15] | Contrast and resolution enhancement of handheld POCUS images. | In vivo: Carotid and thyroid regions. Phantom: ATS-539 phantom. Simulation: Intermediate domain images generated by downgrading the in vivo and phantom images acquired from high-end system (private). | LQ: NPUS050 portable US system was used as low-quality input. HQ: E-CUBE 12R US, L3-12 transducer. | Cascade application of unsupervised self-consistent CycleGAN + supervised super-resolution network. | Cycle consistency + adversarial loss (cycleGAN), MAE + SSIM (super-resolution network) |
Lu et al., 2020 [37] | High-quality reconstruction for DW imaging using a small number (3) of DW transmissions competing with those obtained by compounding with 31 DWS. | In vivo: Thigh muscle, finger phalanx, and liver regions. Phantom: CIRS and Gammex (private). | Verasonics: ATL P4-2 transducer. LQ: 3 DWs. HQ: Compounded image of 31 DWs. | CNN with Inception Module | MSE |
Lyu et al., 2023 [38] | Reconstruct super-resolution high-quality images from single-beam plane wave images. | PICMUS 2016 dataset [97] modulated following the guidelines of CUBDL consisting of Simulation: Generated with Field II software. Phantom: CIRS. In vivo: Carotid artery of healthy volunteer (public). | (Derived from dataset source)Verasonics: L11 probe with range −16° to 16°. LQ: Single PW image. HQ: PW images synthesized from 75 different angles using CPWC. | U-Shaped GAN based on Attention and Residual Connection (ARU-GAN) | Combination of MS-SSIM, classical adversarial, and perceptual loss |
Moinuddin et al., 2022 [39] | Enhance US images using a network where the task of noise suppression and resolution enhancement are carried out simultaneously. | In vivo: Breast US (BUS) dataset [98], for which high resolution and low noise label images were generated using NLLR normal filtration. Simulation: Salient object detection (SOD) dataset [99] augmentated using image formaton physics information divided in two datasets (public). | (Derived from dataset source)Siemens ACUSON Sequoia C512, 17L5 HD transducer (8.5 MHz). | Deep CNN | MSE |
Monkam et al., 2023 [40] | Suppress speckle noise and enhance texture and fine details. | Simulation: Original low-quality US images of HC18 Challenge fetal dataset [100], from which high-quality target images and additional low-quality images were generated (for training and testing). In vivo: publicly available datasets: HC18 Challenge (fetal) [100], BUSI (breast), CCA (common carotid artery) (for testing) (public). | (Derived from HC18 dataset source)Voluson E8 or the Voluson 730 US device. | U-Net with added feature refinement attention block (US-Net) | L1 loss |
Tang et al., 2021 [41] | Reconstruct high-resolution, high-quality plane wave images from low-quality plane wave images from different angles. | PICMUS 2016 dataset [97] modulated following the guidelines of CUBDL consisting of Simulation: Generated with Field II software. Phantom: CIRS. In vivo: Carotid artery of healthy volunteer (public). | (Derived from dataset source)Verasonics: L11 probe with range −16° to 16°. LQ: PW image using 3 angles. HQ: PW images synthesized from 75 different angles using CPWC. | Attention Mechanism and U-Net-Based GAN | Cross-entropy + MSE + perceptual loss |
Zhang et al., 2018 [42] | Reconstruct high-quality US images from small number of PWs (3). | In vivo: Carotid artery and brachioradioalis of heathy volunteers. Phantom: CIRS phantom, ex vivo swine muscles (private). | Verasonics: L10-5 (7.5 MHz) with range −15° to 15°. LQ: 3 PWs. HQ: Coherent compounding using 31 PWs. | GAN with feedforward CNN as both generator and discriminator network | MSE + adversarial loss (generator), binary cross-entropy (discriminator) |
Zhou et al., 2018 [43] | Improve the image quality of a single-angle PW image to that of a PW image synthesized from 75 different angles. | PICMUS 2016 dataset [97] synthesized by three different beamforming methods: In vivo: (1) thyroid gland and (2) carotid arteries of human volunteers (public). Phantom: CIRS phantom. Simulation: (1) point image and (2) cyst images generated using Field II software. | (Derived from dataset sources)Verasonics: L11 probe with range −16° to 16°. LQ: Single PW image. HQ: PW images synthesized from 75 different angles. | Multi-scaled CNN | MSE |
Zhou et al., 2020 [6] | Improve quality of portable US by mapping low-quality images to corresponding high-quality images. | Single-/multi-angle PWI simulation, phantom and in vivo data (only used for transfer learning). For training and testing: In vivo: Carotid and thyroid images of healthy volunteers. Phantom: CIRS and self-made gelatin and raw pork. Simulation: Field II software (private). | LQ: mSonics MU1, L10-5v. transducer. HQ: Verasonics—L11-4v transducer (phantom data) and Toshiba Aplio 500, 7.5 MHz (clinical data). | Two-stage GAN with U-Net and Gradual Learning Strategy. | MSE + SSIM + Conv loss |
Zhou et al., 2021 [14] | Enhance video quality of handheld US devices. | In vivo: Single- and multi-angle PW videos (only for training). Handheld and high-end images and videos of different bodyparts of healthy volunteers (for training and testing) (private). | PW videos: Verasonics—L11-4v transducer (6.25 MHz, 128-element) with range −16° to 16°. High-end US (HQ): Toshiba Aplio 500 device. Handheld US (LQ): mSonics MU1, L10-5 transducer. | Low-rank Representation Multi-pathway GAN | Adversarial + MSE + ultrasound-specific perceptual loss |
Study | Computation Time(Source Code Availability) | Number of Images in Test Set | Performance (±SD) of Low-Quality Input Image | Performance (±SD) of Generated Image |
---|---|---|---|---|
Awasthi et al., 2022 [32] | “Light weight” (available) | Phantom: dataset 1: n = 134 dataset 2: n = 90 dataset 3: n = 31 dataset 4: n = 70 dataset 5: n = 239 | Phantom: dataset 1: PSNR = 17.049 ± 1.107, RMSE = 0.141 ± 0.016, PC = 0.788 dataset 2: PSNR = 15.768 ± 1.376, RMSE = 0.165 ± 0.026 dataset 3: PSNR = 13.885 ± 1.276, RMSE = 0.204 ± 0.032 dataset 4: PSNR = 16.297 ± 1.212, RMSE = 0.155 ± 0.021 dataset 5: PSNR = 15.487 ± 1.876, RMSE = 0.172 ± 0.040 | Phantom: dataset 1: PSNR = 20.903 ± 1.189, RMSE = 0.091 ± 0.012, PC = 0.86 dataset 2: PSNR = 20.523 ± 1.242, RMSE = 0.095 ± 0.013 dataset 3: PSNR = 13.985 ± 1.120, RMSE = 0.201 ± 0.025 dataset 4: PSNR = 21.457 ± 1.238, RMSE = 0.085 ± 0.012 dataset 5: PSNR = 17.654 ± 1.536, RMSE = 0.133 ± 0.022 |
Gasse et al., 2017 [33] | Not reported (not available) | Mixed test set of in vivo and phantom data: n = 1000 | Only graphs given showing CR and LR reached by the proposed model with 3 PWs compared to the standard compounding of an increasingly larger number of PWs. | - |
Goudarzi et al., 2020 [34] | Not reported (available) | Phantom (CIRS): n = - Simulation: n = 360 | Phantom: FWHM = 1.52, CNR = 9.6 Simulation: SSIM = 0.622 ± 0.02, PSNR = 23.27 ± 1, FWHM = 1.3, CNR = 7.2 | Phantom: FWHM = 1.44, CNR = 11.1 Simulation: SSIM = 0.769 ± 0.017, PSNR = 25.32 ± 0.919, FWHM = 1.09, CNR = 8.02 |
Guo et al., 2020 [35] | Not reported (not available) | 225 (out of 9225) patch images from the in vivo, phantom, and simulation dataset (distribution between datasets not reported). | In vivo: PSNR = 16.04 Phantom: FWHM = 1.8 mm, CR = 0.36, CNR = 24.93 | In vivo: PSNR = 18.94 Phantom: FWHM = 1.3 mm, CR = 0.79, CNR = 32.81 |
Huang et al., 2018 [36] | Not reported (not available) | Simulation: n = 1 | Simulation: CNR: 0.939, PICMUS CNR: 2.381, FWHM: 13.34 | Simulation: CNR: 1.508, PICMUS CNR: 6.502, FWHM: 11.15 |
Khan et al., 2021 [15] | 13.18 ms (not available) | In vivo: n = 43 Phantom: n = 32 | Not reported | Gain compared to simulated intermediate quality images of in vivo and phantom data (only measuring fitness of super-resolution network): PSNR = 13.58, SSIM = 0.63 Non-reference metrics for entire proposed method for in vivo and phantom data: CR = 14.96, CNR = 2.38, GCNR = 0.8604 (which is 21.77%, 30.06% and 44.42% higher than those of the low-quality input images.) |
Lu et al., 2020 [37] | 0.75 ± 0.03 ms (not available) | Mixed in vivo and phantom data: n = 1000 | Mixed in vivo and phantom data: PSNR = 29.24 ± 1.57, SSIM = 0.83 ± 0.15, MI = 0.51 ± 0.16 Non-reference metrics are only shown in graph form for low-quality images. | Mixed in vivo and phantom data: PSNR = 31.13 ± 1.47, SSIM = 0.93 ± 0.06, MI = 0.82 ± 0.20,CR (near field) = 19.54, CR (far field) = 14.95, CNR (near field) = 7.63, CNR (far field) = 5.21, LR (near field) = 0.90, LR (middle field) = 1.64, LR (far field) = 2.35 |
Lyu et al., 2023 [38] | Not reported (not available) | In vivo: n = 150 Phantom: n = 150 Simulation: n = 150 | No performance metrics available for low-quality images; only for other traditional deep learning methods for comparison. | In vivo: PSNR = 26.508, CW-SSIM = 0.876, NCC = 0.943 Phantom: FWHM = 0.424, CR = 26.900, CNR = 3.693 Simulation: FWHM = 0.277, CR = 39.472, CNR = 5.141 |
Moinuddin et al., 2022 [39] | Not reported | In vivo: n = 33 Simulation: SOD-1: n = 200 SOD-2: n = 200 Evaluated with 5-fold cross-validation approach. | In vivo: PSNR = 26.0071 ± 2.3083, SSIM = 0.7098 ± 0.0761 Simulation: SOD-1: PSNR = 12.1587 ± 0.7839, SSIM = 0.5570 ± 0.1205 SOD-2: PSNR = 12.5272 ± 0.8243, SSIM = 0.1556 ± 0.1451, GCNR = 0.9936 ± 0.0039 | In vivo: PSNR = 26.9112 ± 2.3025, SSIM = 0.7522 ± 0.0635 Simulation: SOD-1: PSNR = 25.5275 ± 2.9712, SSIM = 0.6946 ± 0.1267 SOD-2: PSNR = 32.4719 ± 2.6179, SSIM = 0.8785 ± 0.0766, GCNR = 0.9966 ± 0.0026 |
Monkam et al., 2023 [40] | 52.16 ms (not available) | In vivo: HC18: n = 30 BUSI: n = 30 CCA: n = 30 Simulation: HC18: n = 335 | No performance metrics available for low-quality images; only for other enhancement methods for comparison. | In vivo: HC18: SNR = 39.32, CNR = 1.10, AGM = 27.46, ENL = 15.71 BUSI: SNR = 34.54, CNR = 4.20, AGM = 39.88, ENL = 17.04 CCA: SNR = 40.87, CNR = 2.59, AGM = 35.92, ENL = 23.03 Simulation: HC18: SSIM = 0.9155, PSNR = 32.87, EPI = 0.6371 |
Tang et al., 2021 [41] | Not reported (not available) | n = 360 (total number of images in test set for the in vivo, phantom, and simulation datasets, distribution not reported) | Phantom: FWHM = 0.5635, CR = 8.718, CNR = 1.109, GCNR = 0.609 Simulation: FWHM = 0.2808, CR = 13.769, CNR = 1.576, GCNR = 0.735 | In vivo: PSNR = 28.278, SSIM = 0.659, MI = 0.9980, NCC = 0.963 Phantom: FWHM = 0.3556, CR = 24.571, CNR = 2.495, GCNR = 0.915 Simulation: FWHM = 0.2695, CR = 39.484, CNR = 5.617, GCNR = 0.998 |
Zhang et al., 2018 [42] | Not reported (not available) | In vivo: n = 500 phantom: n = 30 | Mixed in vivo and phantom test set:FWHM = 0.50, CR = 10.23, CNR = 1.30 | Mixed in vivo and phantom test set:FWHM = 0.53, CR = 19.46, CNR = 2.25 |
Zhou et al., 2018 [43] | Not reported (not available) | In vivo: Thyroid dataset: n = 30 Simulation: Point dataset: n = 30 Cyst dataset: n = 30 Evaluated with 5-fold cross-validation approach. | In vivo: Thyroid dataset: PSNR = 14.9235, SSIM = 0.0291, MI = 0.3474 Simulation: Point dataset: PSNR = 24.1708, SSIM = 0.1962, MI = 0.4124, FWHM = 0.49 Cyst dataset: PSNR = 15.8860, SSIM = 0.5537, MI = 1.1976, CR = 137.0473 | In vivo: Thyroid dataset: PSNR = 21.7248, SSIM = 0.3034, MI = 0.8856 Simulation: Point dataset: PSNR = 36.5884, SSIM = 0.9216, MI = 0.4483, FWHM = 0.196 Cyst dataset: PSNR = 24.0167, SSIM = 0.6135, MI = 1.5622, CR = 184.0432 |
Zhou et al., 2020 [6] | Not reported (not available) | In vivo: n = 94 Phantom: n = 40 Simulation: n = 56 Evaluated with 5-fold cross validation approach. | In vivo: PSNR = 8.65 ± 1.32, SSIM = 0.18 ± 0.04, MI = 0.22 ± 0.13, BRISQUE = 38.91 ± 4.99 Phantom: PSNR = 15.26 ± 2.91, SSIM = 0.12 ± 0.03, MI = 0.20 ± 0.11, BRISQUE = 24.61 ± 4.50 Simulation: PSNR = 16.38 ± 2.35, SSIM = 0.19 ± 0.06, MI = 0.22 ± 0.16, BRISQUE = 29.08 ± 3.45 | In vivo: PSNR = 18.08 ± 1.57, SSIM = 0.41 ± 0.05, MI = 0.68 ± 0.18, BRISQUE = 35.25 ± 4.13 Phantom: PSNR = 24.70 ± 1.11, SSIM = 0.64 ± 0.07, MI = 0.26 ± 0.09, BRISQUE = 21.68 ± 3.36 Simulation: PSNR = 28.50 ± 2.01, SSIM = 0.59 ± 0.02, MI = 0.42 ± 0.04, BRISQUE = 23.30 ± 3.09 |
Zhou et al., 2021 [14] | Not reported (not available) | In vivo: n = 40 videos For full-reference methods, a single frame in handheld video was used, and most-similar frame in high-end video was selected. | In vivo: PSNR = 12.68 ± 3.45, SSIM = 0.24 ± 0.06, MI = 0.71 ± 0.09, NIQE = 19.48 ± 4.66, ultrasound quality score = 0.06 ± 0.03 | In vivo: PSNR = 19.95 ± 3.24, SSIM = 0.45 ± 0.06, MI = 1.05 ± 0.07, NIQE = 6.95 ± 1.97, ultrasound quality score = 0.89 ± 0.16 |
3.5. Meta-Analysis Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Search Strings
References
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van der Pol, H.G.A.; van Karnenbeek, L.M.; Wijkhuizen, M.; Geldof, F.; Dashtbozorg, B. Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review. Appl. Sci. 2024, 14, 7132. https://doi.org/10.3390/app14167132
van der Pol HGA, van Karnenbeek LM, Wijkhuizen M, Geldof F, Dashtbozorg B. Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review. Applied Sciences. 2024; 14(16):7132. https://doi.org/10.3390/app14167132
Chicago/Turabian Stylevan der Pol, Hilde G. A., Lennard M. van Karnenbeek, Mark Wijkhuizen, Freija Geldof, and Behdad Dashtbozorg. 2024. "Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review" Applied Sciences 14, no. 16: 7132. https://doi.org/10.3390/app14167132