The use of artificial intelligence in computed tomography image reconstruction - A literature review

In vitro development of a muscle-tendon junction construct using decellularised extracellular matrix: Effect of cyclic tensile loading

The muscle tendon junction (MTJ) plays a crucial role in transmitting the force generated by muscles to the tendon and then to the bone. Injuries such as tears and strains frequently happen at the MTJ, where the regenerative process is limited due to poor vascularization and the complex structure of the tissue. Current solutions for a complete tear at the MTJ have not been successful and therefore, the development of a tissue-engineered MTJ may provide a more effective treatment. In this study, decellularised extracellular matrix (DECM) derived from sheep MTJ was used to provide a scaffold for the MTJ with the relevant mechanical properties and differentiation cues such as the relase of growth factors. Human mesenchymal stem cells (MSCs) were seeded on DECM and 10 % cyclic strain was applied using a bioreactor. MSCs cultured on DECM showed significantly higher gene and protein expression of MTJ markers such as collagen 22, paxillin and talin, than MSCs in 2D culture. Although collagen 22 protein expression was higher in the cells with strain than without strain, reduced gene expression of other MTJ markers was observed when the strain was applied. DECM combined with 10 % strain enhanced myogenic differentiation, while tenogenic differentiation was reduced when compared to static cultures of MSCs on DECM. For the first time, these results showed that DECM derived from the MTJ can induce MTJ marker gene and protein expression by MSCs, however, the effect of strain on the MTJ development in DECM culture needs further investigation.

Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review

Background

The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations.

Methods

We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines.

Results

Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations.

Conclusions

DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.

The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review

Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that a workflow that combines DP and artificial intelligence (AI) applied to histopathology has potential value in supporting the diagnosis, treatment evaluation, and prognosis prediction of liver diseases. Here, we provide a systematic review of the use of this workflow in the field of hepatology. Based on the PRISMA 2020 criteria, a search of the PubMed, SCOPUS, and Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted the specifications and objectives of each study, the AI tools used, and the results obtained. From the 266 initial records identified, 25 eligible studies were selected, mainly conducted on human liver tissues. Most of the studies were performed using whole-slide imaging systems for imaging acquisition and applying different machine learning and deep learning methods for image pre-processing, segmentation, feature extractions, and classification. Of note, most of the studies selected demonstrated good performance as classifiers of liver histological images compared to pathologist annotations. Promising results to date bode well for the not-too-distant inclusion of these techniques in clinical practice.

Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics

Artificial intelligence (AI) in medicine has gained a lot of momentum in the last decades and has been applied to various fields of medicine. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have facilitated the role of AI in modern healthcare. Similarly, as in other fields, AI applications, such as machine learning, artificial neural networks, and deep learning, have shown great potential in andrology and reproductive medicine. AI-based tools are poised to become valuable assets with abilities to support and aid in diagnosing and treating male infertility, and in improving the accuracy of patient care. These automated, AI-based predictions may offer consistency and efficiency in terms of time and cost in infertility research and clinical management. In andrology and reproductive medicine, AI has been used for objective sperm, oocyte, and embryo selection, prediction of surgical outcomes, cost-effective assessment, development of robotic surgery, and clinical decision-making systems. In the future, better integration and implementation of AI into medicine will undoubtedly lead to pioneering evidence-based breakthroughs and the reshaping of andrology and reproductive medicine.

Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT

This study is aimed to evaluate effects of deep learning image reconstruction (DLIR) on image quality in single-energy CT (SECT) and dual-energy CT (DECT), in reference to adaptive statistical iterative reconstruction-V (ASIR-V). The Gammex 464 phantom was scanned in SECT and DECT modes at three dose levels (5, 10, and 20 mGy). Raw data were reconstructed using six algorithms: filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) strength, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H), to generate SECT 120kVp images and DECT 120kVp-like images. Objective image quality metrics were computed, including noise power spectrum (NPS), task transfer function (TTF), and detectability index (d'). Subjective image quality evaluation, including image noise, texture, sharpness, overall quality, and low- and high-contrast detectability, was performed by six readers. DLIR-H reduced overall noise magnitudes from FBP by 55.2% in a more balanced way of low and high frequency ranges comparing to AV-40, and improved the TTF values at 50% for acrylic inserts by average percentages of 18.32%. Comparing to SECT 20 mGy AV-40 images, the DECT 10 mGy DLIR-H images showed 20.90% and 7.75% improvement in d' for the small-object high-contrast and large-object low-contrast tasks, respectively. Subjective evaluation showed higher image quality and better detectability. At 50% of the radiation dose level, DECT with DLIR-H yields a gain in objective detectability index compared to full-dose AV-40 SECT images used in daily practice.

Artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease

Medical imaging has been intensively employed in screening, diagnosis and monitoring during the COVID-19 pandemic. With the improvement of RT-PCR and rapid inspection technologies, the diagnostic references have shifted. Current recommendations tend to limit the application of medical imaging in the acute setting. Nevertheless, efficient and complementary values of medical imaging have been recognized at the beginning of the pandemic when facing unknown infectious diseases and a lack of sufficient diagnostic tools. Optimizing medical imaging for pandemics may still have encouraging implications for future public health, especially for long-lasting post-COVID-19 syndrome theranostics. A critical concern for the application of medical imaging is the increased radiation burden, particularly when medical imaging is used for screening and rapid containment purposes. Emerging artificial intelligence (AI) technology provides the opportunity to reduce the radiation burden while maintaining diagnostic quality. This review summarizes the current AI research on dose reduction for medical imaging, and the retrospective identification of their potential in COVID-19 may still have positive implications for future public health.

Nanomaterial-based contrast agents

Medical imaging, which empowers the detection of physiological and pathological processes within living subjects, has a vital role in both preclinical and clinical diagnostics. Contrast agents are often needed to accompany anatomical data with functional information or to provide phenotyping of the disease in question. Many newly emerging contrast agents are based on nanomaterials as their high payloads, unique physicochemical properties, improved sensitivity and multimodality capacity are highly desired for many advanced forms of bioimaging techniques and applications. Here, we review the developments in the field of nanomaterial-based contrast agents. We outline important nanomaterial design considerations and discuss the effect on their physicochemical attributes, contrast properties and biological behaviour. We also describe commonly used approaches for formulating, functionalizing and characterizing these nanomaterials. Key applications are highlighted by categorizing nanomaterials on the basis of their X-ray, magnetic, nuclear, optical and/or photoacoustic contrast properties. Finally, we offer our perspectives on current challenges and emerging research topics as well as expectations for future advancements in the field.

Knowledge, perceptions, and expectations of Artificial intelligence in radiography practice: A global radiography workforce survey

BACKGROUND

Artificial Intelligence (AI) technologies have already started impacting clinical practice across various settings worldwide, including the radiography profession. This study is aimed at exploring a world-wide view on AI technologies in relation to knowledge, perceptions, and expectations of radiography professionals.

METHODS

An online survey (hosted on Qualtrics) on key AI concepts was open to radiography professionals worldwide (August 1st to December 31st 2020). The survey sought both quantitative and qualitative data on topical issues relating to knowledge, perceptions, and expectations in relation to AI implementation in radiography practice. Data obtained was analysed using the Statistical Package for Social Sciences (SPSS) (v.26) and the six-phase thematic analysis approach.

RESULTS

A total of 314 valid responses were obtained with a fair geographical distribution. Of the respondents, 54.1% (157/290) were from North America and were predominantly clinical practicing radiographers (60.5%, 190/314). Our findings broadly relate to different perceived benefits and misgivings/shortcomings of AI implementation in radiography practice. The benefits relate to enhanced workflows and optimised workstreams while the misgivings/shortcomings revolve around de-skilling and impact on patient-centred care due to over-reliance on advanced technology following AI implementation.

DISCUSSION

Artificial intelligence is a tool but to operate optimally it requires human input and validation. Radiographers working at the interface between technology and the patient are key stakeholders in AI implementation. Lack of training and of transparency of AI tools create a mixed response of radiographers when they discuss their perceived benefits and challenges. It is also possible that their responses are nuanced by different regional and geographical contexts when it comes to AI deployment. Irrespective of geography, there is still a lot to be done about formalised AI training for radiographers worldwide. This is a vital step to ensure safe and effective AI implementation, adoption, and faster integration into clinical practice by healthcare workers including radiographers.

CONCLUSION

Advancement of AI technologies and implementation should be accompanied by proportional training of end-users in radiography and beyond. There are many benefits of AI-enabled radiography workflows and improvement on efficiencies but equally there will be widespread disruption of traditional roles and patient-centred care, which can be managed by a well-educated and well-informed workforce.

Complex Relationship Between Artificial Intelligence and CT Radiation Dose

Concerns over need for CT radiation dose optimization and reduction led to improved scanner efficiency and introduction of several reconstruction techniques and image processing-based software. The latest technologies use artificial intelligence (AI) for CT dose optimization and image quality improvement. While CT dose optimization has and can benefit from AI, variations in scanner technologies, reconstruction methods, and scan protocols can lead to substantial variations in radiation doses and image quality across and within different scanners. These variations in turn can influence performance of AI algorithms being deployed for tasks such as detection, segmentation, characterization, and quantification. We review the complex relationship between AI and CT radiation dose.

Is There Any Improvement in Image Quality in Obese Patients When Using a New X-ray Tube and Deep Learning Image Reconstruction in Coronary Computed Tomography Angiography?

Deep learning image reconstruction (DLIR) is a technique that should reduce noise and improve image quality. This study assessed the impact of using both higher tube currents as well as DLIR on the image quality and diagnostic accuracy. The study consisted of 51 symptomatic obese (BMI > 30 kg/m2) patients with low to moderate risk of coronary artery disease (CAD). All patients underwent coronary computed tomography angiography (CCTA) twice, first with the Revolution CT scanner and then with the upgraded Revolution Apex scanner with the ability to increase tube current. Images were reconstructed using ASiR-V 50% and DLIR. The image quality was evaluated by an observer using a Likert score and by ROI measurements in aorta and the myocardium. Image quality was significantly improved with the Revolution Apex scanner and reconstruction with DLIR resulting in an odds ratio of 1.23 (p = 0.017), and noise was reduced by 41%. A total of 88% of the image sets performed with Revolution Apex + DLIR were assessed as good enough for diagnosis compared to 69% of the image sets performed with Revolution Apex/CT + ASiR-V. In obese patients, the combination of higher tube current and DLIR significantly improves the subjective image quality and diagnostic utility and reduces noise.

The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review

Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative reconstruction (IR), which have been utilised widely in the image reconstruction process of computed tomography (CT) are not suitable in the case of low-dose CT applications, because of the unsatisfying quality of the reconstructed image and inefficient reconstruction time. Therefore, as the demand for CT radiation dose reduction continues to increase, the use of artificial intelligence (AI) in image reconstruction has become a trend that attracts more and more attention. This systematic review examined various deep learning methods to determine their characteristics, availability, intended use and expected outputs concerning low-dose CT image reconstruction. Utilising the methodology of Kitchenham and Charter, we performed a systematic search of the literature from 2016 to 2021 in Springer, Science Direct, arXiv, PubMed, ACM, IEEE, and Scopus. This review showed that algorithms using deep learning technology are superior to traditional IR methods in noise suppression, artifact reduction and structure preservation, in terms of improving the image quality of low-dose reconstructed images. In conclusion, we provided an overview of the use of deep learning approaches in low-dose CT image reconstruction together with their benefits, limitations, and opportunities for improvement.

Clinical acceptance of deep learning reconstruction for abdominal CT imaging: objective and subjective image quality and low-contrast detectability assessment

OBJECTIVE

To evaluate the image quality and clinical acceptance of a deep learning reconstruction (DLR) algorithm compared to traditional iterative reconstruction (IR) algorithms.

METHODS

CT acquisitions were performed with two phantoms and a total of nine dose levels. Images were reconstructed with two types of IR algorithms, DLR and filtered-back projection. Spatial resolution, image texture, mean noise value, and objective and subjective low-contrast detectability were compared. Ten senior radiologists evaluated the clinical acceptance of these algorithms by scoring ten CT exams reconstructed with the DLR and IR algorithms evaluated.

RESULTS

Compared to MBIR, DLR yielded a lower noise and a higher low-contrast detectability index at low doses (CTDI_vol ≤ 2.2 and ≤ 4.5 mGy, respectively). Spatial resolution and detectability at higher doses were better with MBIR. Compared to HIR, DLR yielded a higher spatial resolution, a lower noise, and a higher detectability index. Despite these differences in algorithm performance, significant differences in subjective low-contrast performance were not found (p ≥ 0.005). DLR texture was finer than that of MBIR and closer to that of HIR. Radiologists preferred DLR images for all criteria assessed (p < 0.0001), whereas MBIR was rated worse than HIR (p < 0.0001) in all criteria evaluated, except for noise (p = 0.044). DLR reconstruction time was 12 times faster than that of MBIR.

CONCLUSION

DLR yielded a gain in objective detection and noise at lower dose levels with the best clinical acceptance among the evaluated reconstruction algorithms.

KEY POINTS

• DLR yielded improved objective low-contrast detection and noise at lower dose levels. • Despite the differences in objective detectability among the algorithms evaluated, there were no differences in subjective detectability. • DLR presented significantly higher clinical acceptability scores compared to MBIR and HIR.

Deep learning versus iterative image reconstruction algorithm for head CT in trauma

PURPOSE

To compare the image quality between a deep learning-based image reconstruction algorithm (DLIR) and an adaptive statistical iterative reconstruction algorithm (ASiR-V) in noncontrast trauma head CT.

METHODS

Head CT scans from 94 consecutive trauma patients were included. Images were reconstructed with ASiR-V 50% and the DLIR strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The image quality was assessed quantitatively and qualitatively and compared between the different reconstruction algorithms. Inter-reader agreement was assessed by weighted kappa.

RESULTS

DLIR-M and DLIR-H demonstrated lower image noise (p < 0.001 for all pairwise comparisons), higher SNR of up to 82.9% (p < 0.001), and higher CNR of up to 53.3% (p < 0.001) compared to ASiR-V. DLIR-H outperformed other DLIR strengths (p ranging from < 0.001 to 0.016). DLIR-M outperformed DLIR-L (p < 0.001) and ASiR-V (p < 0.001). The distribution of reader scores for DLIR-M and DLIR-H shifted towards higher scores compared to DLIR-L and ASiR-V. There was a tendency towards higher scores with increasing DLIR strengths. There were fewer non-diagnostic CT series for DLIR-M and DLIR-H compared to ASiR-V and DLIR-L. No images were graded as non-diagnostic for DLIR-H regarding intracranial hemorrhage. The inter-reader agreement was fair-good between the second most and the less experienced reader, poor-moderate between the most and the less experienced reader, and poor-fair between the most and the second most experienced reader.

CONCLUSION

The image quality of trauma head CT series reconstructed with DLIR outperformed those reconstructed with ASiR-V. In particular, DLIR-M and DLIR-H demonstrated significantly improved image quality and fewer non-diagnostic images. The improvement in qualitative image quality was greater for the second most and the less experienced readers compared to the most experienced reader.

The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis

OBJECTIVE

To determine the difference in CT values and image quality of abdominal CT images reconstructed by filtered back-projection (FBP), hybrid iterative reconstruction (IR), and deep learning reconstruction (DLR).

METHODS

PubMed and Embase were systematically searched for articles regarding CT densitometry in the abdomen and the image reconstruction techniques FBP, hybrid IR, and DLR. Mean differences in CT values between reconstruction techniques were analyzed. A comparison between signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of FBP, hybrid IR, and DLR was made. A comparison of diagnostic confidence between hybrid IR and DLR was made.

RESULTS

Sixteen articles were included, six being suitable for meta-analysis. In the liver, the mean difference between hybrid IR and DLR was - 0.633 HU (p = 0.483, SD ± 0.902 HU). In the spleen, the mean difference between hybrid IR and DLR was - 0.099 HU (p = 0.925, SD ± 1.061 HU). In the pancreas, the mean difference between hybrid IR and DLR was - 1.372 HU (p = 0.353, SD ± 1.476 HU). In 14 articles, CNR was described. In all cases, DLR showed a significantly higher CNR. In 9 articles, SNR was described. In all cases but one, DLR showed a significantly higher SNR. In all cases, DLR showed a significantly higher diagnostic confidence.

CONCLUSIONS

There were no significant differences in CT values reconstructed by FBP, hybrid IR, and DLR in abdominal organs. This shows that these reconstruction techniques are consistent in reconstructing CT values. DLR images showed a significantly higher SNR and CNR, compared to FBP and hybrid IR.

KEY POINTS

CT values of abdominal CT images are similar between deep learning reconstruction (DLR), filtered back-projection (FBP), and hybrid iterative reconstruction (IR). DLR results in improved image quality in terms of SNR and CNR compared to FBP and hybrid IR images. DLR can thus be safely implemented in the clinical setting resulting in improved image quality without affecting CT values.

Radiation Dose Modulation of Computed Tomography Component in Positron Emission Tomography/Computed Tomography

In oncology practice, the CT component of PET/CT may be used for attenuation correction, lesion localization, and CT diagnosis, and significantly enhances the clinical benefit of PET. However, acquisition of CT covering the whole body increases radiation dose and consequently the risk of cancer induction, and optimization should be pursued. In CT, radiation dose is a major determinant of image quality, and is mainly adjusted by modulation of tube current. Automatic exposure control (AEC) is widely used for tube current modulation, and increases tube current in a large patient and in strongly attenuating regions of a given patient to preserve image quality despite strong X-ray attenuation. Radiation dose determined by AEC depends on various factors, such as the type of AEC software, scout imaging direction, arm positioning, and patient centering. Because radiation dose reduction increases image noise and may degrade clinical utility, image quality should be assessed together with radiation dose in the process of optimization. Clinical demands for image quality vary largely depending on the aim of the CT component, with lower quality being sufficient for lesion localization than for CT diagnosis. Therefore, optimal radiation dose differs according to the aim. Determining optimal dose is a somewhat subjective and difficult task, and use of the diagnostic reference level, determined based on national or regional survey, is recommended to recognize need for optimization. The volume CT dose index and dose-length product are used as indices of CT radiation dose, and effective dose may also be calculated for comparison of stochastic effects among different radiation sources and among different imaging procedures. Wide coverage from the head to the lower extremities causes problems in estimating these indices in whole-body PET/CT. CT definitely enhances clinical benefits of PET but simultaneously increases potential detriments due to radiation exposure. In the era of hybrid imaging, nuclear medicine practitioners should be aware of the technology and radiation dose management of CT.

Synchrotron X-Ray Tomography for Rechargeable Battery Research: Fundamentals, Setups and Applications

Understanding the complicated interplay of the continuously evolving electrode materials in their inherent 3D states during the battery operating condition is of great importance for advancing rechargeable battery research. In this regard, the synchrotron X-ray tomography technique, which enables non-destructive, multi-scale, and 3D imaging of a variety of electrode components before/during/after battery operation, becomes an essential tool to deepen this understanding. The past few years have witnessed an increasingly growing interest in applying this technique in battery research. Hence, it is time to not only summarize the already obtained battery-related knowledge by using this technique, but also to present a fundamental elucidation of this technique to boost future studies in battery research. To this end, this review firstly introduces the fundamental principles and experimental setups of the synchrotron X-ray tomography technique. After that, a user guide to its application in battery research and examples of its applications in research of various types of batteries are presented. The current review ends with a discussion of the future opportunities of this technique for next-generation rechargeable batteries research. It is expected that this review can enhance the reader's understanding of the synchrotron X-ray tomography technique and stimulate new ideas and opportunities in battery research.