Artificial Intelligence in Prenatal Ultrasound Diagnosis

The application of artificial intelligence (AI) technology to medical imaging has resulted in great breakthroughs. Given the unique position of ultrasound (US) in prenatal screening, the research on AI in prenatal US has practical significance with its application to prenatal US diagnosis improving work efficiency, providing quantitative assessments, standardizing measurements, improving diagnostic accuracy, and automating image quality control. This review provides an overview of recent studies that have applied AI technology to prenatal US diagnosis and explains the challenges encountered in these applications.

Convolutional Neural Networks for Automatic Image Quality Control and EARL Compliance of PET Images

Background: Machine learning studies require a large number of images often obtained on different PET scanners. When merging these images, the use of harmonized images following EARL-standards is essential. However, when including retrospective images, EARL accreditation might not have been in place. The aim of this study was to develop a convolutional neural network (CNN) that can identify retrospectively if an image is EARL compliant and if it is meeting older or newer EARL-standards. Materials and Methods: 96 PET images acquired on three PET/CT systems were included in the study. All images were reconstructed with the locally clinically preferred, EARL1, and EARL2 compliant reconstruction protocols. After image pre-processing, one CNN was trained to separate clinical and EARL compliant reconstructions. A second CNN was optimized to identify EARL1 and EARL2 compliant images. The accuracy of both CNNs was assessed using 5-fold cross validation. The CNNs were validated on 24 images acquired on a PET scanner not included in the training data. To assess the impact of image noise on the CNN decision, the 24 images were reconstructed with different scan durations.Results: In the cross-validation, the first CNN classified all images correctly. When identifying EARL1 and EARL2 compliant images, the second CNN identified 100% EARL1 compliant and 85% EARL2 compliant images correctly. The accuracy in the independent dataset was comparable to the cross-validation accuracy. The scan duration had almost no impact on the results. Conclusion: The two CNNs trained in this study can be used to retrospectively include images in a multi-center setting by e.g. adding additional smoothing. This method is especially important for machine learning studies where the harmonization of images from different PET systems is essential.

Convolutional Neural Networks for automatic image quality control and EARL compliance of PET images

Background Machine learning studies require a large number of images often obtained on different PET scanners. When merging these images, the use of harmonized images following EARL-standards is essential. However, when including retrospective images, EARL accreditation might not have been in place. The aim of this study was to develop a convolutional neural network (CNN) that can identify retrospectively if an image is EARL compliant and if it is meeting older or newer EARL-standards. Materials and Methods 96 PET images acquired on three PET/CT systems were included in the study. All images were reconstructed with the locally clinically preferred, EARL1, and EARL2 compliant reconstruction protocols. After image pre-processing, one CNN was trained to separate clinical and EARL compliant reconstructions. A second CNN was optimized to identify EARL1 and EARL2 compliant images. The accuracy of both CNNs was assessed using 5-fold cross validation. The CNNs were validated on 24 images acquired on a PET scanner not included in the training data. To assess the impact of image noise on the CNN decision, the 24 images were reconstructed with different scan durations. Results In the cross-validation, the first CNN classified all images correctly. When identifying EARL1 and EARL2 compliant images, the second CNN identified 100% EARL1 compliant and 85% EARL2 compliant images correctly. The accuracy in the independent dataset was comparable to the cross-validation accuracy. The scan duration had almost no impact on the results. Conclusion The two CNNs trained in this study can be used to retrospectively include images in a multi-center setting by e.g. adding additional smoothing. This method is especially important for machine learning studies where the harmonization of images from different PET systems is essential.

Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging

Artificial intelligence (AI) is being increasingly adopted in medical research and applications. Medical AI devices have continuously been approved by the Food and Drug Administration in the United States and the responsible institutions of other countries. Ultrasound (US) imaging is commonly used in an extensive range of medical fields. However, AI-based US imaging analysis and its clinical implementation have not progressed steadily compared to other medical imaging modalities. The characteristic issues of US imaging owing to its manual operation and acoustic shadows cause difficulties in image quality control. In this review, we would like to introduce the global trends of medical AI research in US imaging from both clinical and basic perspectives. We also discuss US image preprocessing, ingenious algorithms that are suitable for US imaging analysis, AI explainability for obtaining informed consent, the approval process of medical AI devices, and future perspectives towards the clinical application of AI-based US diagnostic support technologies.

ASSESSMENT OF NEW HIGH-DEFINITION BOREHOLE IMAGING-WHILE-DRILLING TECHNOLOGY: LEARNINGS FROM PRE-SALT CARBONATES OF BRAZIL

The pre-salt carbonates of Brazil pose drilling and characterization challenges associated with inherent reservoir heterogeneity; and borehole imaging while drilling often provides insights helpful for both, operational and subsequent decisions. The findings and learnings from a 3-well campaign, offshore Brazil are presented to assess and validate a recently deployed high-definition borehole imaging technology that provides industry’s first real-time ultrasonic amplitude images and time-to-depth corrections for best possible images maintaining the geological features integrity. High-definition ultrasonic measurements were acquired at two central frequencies with 0.2-in resolution and provided amplitude and transit time images for geological characterization and petrophysical evaluation in addition to azimuthal ultrasonic calipers. The lossy nature of amplitude data makes it difficult to transmit in real-time; therefore, a unique data compression technology was used to achieve industry’s first high quality amplitude images streaming while drilling. In deepwater operations acquisition of high-definition logging while drilling (LWD) images can be severely degraded if time-to-depth offset due to heave is not compensated. Recently developed heave-filtering workflows ensured the integrity of subsurface features. The time-indexed data was processed with this application in real-time, providing good results and confidence in the capability of this technology. Image-logs of the first well were helpful in interpretation and added value to the reservoir understanding; however, many intervals suffered from lack of confidence in image features. Simulations were performed to improve the images acquisition parameters based on learnings from this experience. New optimized operational parameters were applied in next two wells, resulting in image logs of excellent quality. Data from second well suffered from high heave while drilling, which required implementation of the heave-filtering memory data workflow. For the third well, an additional requirement for real-time image quality-control was defined, requiring data to be processed after every drill-stand. Real-time data quality provided confidence in optimal quality of memory data, thereby eliminating the need of post-drilling wireline operations in open-hole. The images acquired in memory helped characterize intervals of stromatolites with various morphology, and zones of vugs distribution, providing excellent alternative for wireline logging, de-risking the operations in pre-salt carbonate logging in Brazil offshore operations.

Proposals for the Russian quality assurance program in computed tomography

To ensure the quality assurance of CT-examinations, it is necessary to obtain the high-quality diagnostic information and maintain the optimal exposure levels of patients and medical staff. This paper is focused on the requirements and main aspects of quality assurance of CT-examinations, which include quality control of the equipment, methods of CT-image quality control, optimization of radiation protection, as well as management of the unintended and accidental medical exposure. The paper contains recommendations on quality control of diagnostic equipment, methods for monitoring the quality control of CT-images, values of diagnostic reference levels for the detection of abnormally high patient doses and optimization of the radiation protection of patients, as well as the recommendations for management of radiation and non-radiation accidents. All main sections of the paper represent an unified quality assurance system in computed tomography.

Evaluation of Self-Produced Phantom Usafulness for image Quality Control of Radiation Generator for Diagnosis

Background/Objectives: Quality control can improve the quality of medical care along with the stability of diagnostic X-ray generator. Regular quality control provides reliable quality control of the machine and maintains consistency of general imaging using radiation for efficient diagnosis. Methods/Statistical analysis: A phantom for quality control of diagnostic X-ray generator was produced using a 3D printer. Quantitative and qualitative evaluation of the phantom utility was conducted by modifying images acquired using S and D companies’ tools with Source to Image-Receptor Distance (SID) levels ranging between 130 cm and 180 cm. The evaluation indices were determined based on the analysis of field compliance, uniformity, low and high-contrast resolution, and linearity. Findings: The evaluation was conducted by acquiring and changing the radiographic image to SIDs between 130 cm and 180 cm using the indigenous phantom. The field compliance of S and D companies in terms of quantitative evaluation indices was both appropriate within ± 1% according to the SID change. To ensure a uniform SID 130 cm, the internal and external means of S company were 893 and 943, respectively, while those of the D company were 228.1 and 261.4, respectively. At an SID of 180 cm, the internal and external means of the S company were 928.1 and 958.4, respectively, while those of the D company were 257.2 and 299, respectively. A characteristic of the DR system was identified to ensure linearity, altered exposure dosage according to the step wedge height, and the difference in SI values according to the characteristics of the equipment and linearity. The qualitative evaluation indices were determined by identifying the size of the hole under high-contrast resolution up to 0.8㎜ and the bar size up to 1.6 lp/㎜. The low contrast resolution was evaluated with a C-D pattern, and at SID 130 cm, the S company scored 124.6 points and the D company 116 points, and at 180 cm, the S company scored 111.4 and the D company 104.6 points. Improvements/Applications: The utility of the homegrown phantom in quality control was confirmed for each index. The medical institutions are required to introduce quality control regulations for general image examination using radiation. It is helpful to efficiently manage old equipment and improve public health and medical care by linking with the health insurance fee.