Hierarchical medical image report adversarial generation with hybrid discriminator

BACKGROUND AND OBJECTIVES

Generating coherent reports from medical images is an important task for reducing doctors' workload. Unlike traditional image captioning tasks, the task of medical image report generation faces more challenges. Current models for generating reports from medical images often fail to characterize some abnormal findings, and some models generate reports with low quality. In this study, we propose a model to generate high-quality reports from medical images.

METHODS

In this paper, we propose a model called Hybrid Discriminator Generative Adversarial Network (HDGAN), which combines Generative Adversarial Network (GAN) with Reinforcement Learning (RL). The HDGAN model consists of a generator, a one-sentence discriminator, and a one-word discriminator. Specifically, the RL reward signals are judged on the one-sentence discriminator and one-word discriminator separately. The one-sentence discriminator can better learn sentence-level structural information, while the one-word discriminator can learn word diversity information effectively.

RESULTS

Our approach performs better on the IU-X-ray and COV-CTR datasets than the baseline models. For the ROUGE metric, our method outperforms the state-of-the-art model by 0.36 on the IU-X-ray, 0.06 on the MIMIC-CXR and 0.156 on the COV-CTR.

CONCLUSIONS

The compositional framework we proposed can generate more accurate medical image reports at different levels.

Analysis of polarization features of human breast cancer tissue by Mueller matrix visualization

Significance

Breast cancer ranks second in the world in terms of the number of women diagnosed. Effective methods for its early-stage detection are critical for facilitating timely intervention and lowering the mortality rate.

Aim

Polarimetry provides much useful information on the structural properties of breast cancer tissue samples and is a valuable diagnostic tool. The present study classifies human breast tissue samples as healthy or cancerous utilizing a surface-illuminated backscatter polarization imaging technique.

Approach

The viability of the proposed approach is demonstrated using 95 breast tissue samples, including 35 healthy samples, 20 benign cancer samples, 20 grade-2 malignant samples, and 20 grade-3 malignant samples.

Results

The observation results reveal that element m 23 in the Mueller matrix of the healthy samples has a deeper color and greater intensity than that in the breast cancer samples. Conversely, element m 32 shows a lighter color and reduced intensity. Finally, element m 44 has a darker color in the healthy samples than in the cancer samples. The analysis of variance test results and frequency distribution histograms confirm that elements m 23 , m 32 , and m 44 provide an effective means of detecting and classifying human breast tissue samples.

Conclusions

Overall, the results indicate that surface-illuminated backscatter polarization imaging has significant potential as an assistive tool for breast cancer diagnosis and classification.

Lessons Learned in Building Expertly Annotated Multi-Institution Datasets and Hosting the RSNA AI Challenges

The Radiological Society of North America (RSNA) has held artificial intelligence competitions to tackle real-world medical imaging problems at least annually since 2017. This article examines the challenges and processes involved in organizing these competitions, with a specific emphasis on the creation and curation of high-quality datasets. The collection of diverse and representative medical imaging data involves dealing with issues of patient privacy and data security. Furthermore, ensuring quality and consistency in data, which includes expert labeling and accounting for various patient and imaging characteristics, necessitates substantial planning and resources. Overcoming these obstacles requires meticulous project management and adherence to strict timelines. The article also highlights the potential of crowdsourced annotation to progress medical imaging research. Through the RSNA competitions, an effective global engagement has been realized, resulting in innovative solutions to complex medical imaging problems, thus potentially transforming health care by enhancing diagnostic accuracy and patient outcomes. Keywords: Use of AI in Education, Artificial Intelligence © RSNA, 2024.

Surface engineered nanodiamonds: mechanistic intervention in biomedical applications for diagnosis and treatment of cancer

In terms of biomedical tools, nanodiamonds (ND) are a more recent innovation. Their size typically ranges between 4 to 100 nm. ND are produced via a variety of methods and are known for their physical toughness, durability, and chemical stability. Studies have revealed that surface modifications and functionalization have a significant influence on the optical and electrical properties of the nanomaterial. Consequently, surface functional groups of NDs have applications in a variety of domains, including drug administration, gene delivery, immunotherapy for cancer treatment, and bio-imaging to diagnose cancer. Additionally, their biocompatibility is a critical requisite for theirin vivoandin vitrointerventions. This review delves into these aspects and focuses on the recent advances in surface modification strategies of NDs for various biomedical applications surrounding cancer diagnosis and treatment. Furthermore, the prognosis of its clinical translation has also been discussed.

Recent Progress in Peptide-Based Molecular Probes for Disease Bioimaging

Recent strides in molecular pathology have unveiled distinctive alterations at the molecular level throughout the onset and progression of diseases. Enhancing the in vivo visualization of these biomarkers is crucial for advancing disease classification, staging, and treatment strategies. Peptide-based molecular probes (PMPs) have emerged as versatile tools due to their exceptional ability to discern these molecular changes with unparalleled specificity and precision. In this Perspective, we first summarize the methodologies for crafting innovative functional peptides, emphasizing recent advancements in both peptide library technologies and computer-assisted peptide design approaches. Furthermore, we offer an overview of the latest advances in PMPs within the realm of biological imaging, showcasing their varied applications in diagnostic and therapeutic modalities. We also briefly address current challenges and potential future directions in this dynamic field.

Artificial intelligence in liver imaging: methods and applications

Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current demands for precisely diagnosing and treating liver diseases, and artificial intelligence (AI), which excels at automatically making quantitative assessments of complex medical image characteristics, has made great strides regarding the qualitative interpretation of medical imaging by clinicians. Here, we review the current state of medical-imaging-based AI methodologies and their applications concerning the management of liver diseases. We summarize the representative AI methodologies in liver imaging with focusing on deep learning, and illustrate their promising clinical applications across the spectrum of precise liver disease detection, diagnosis and treatment. We also address the current challenges and future perspectives of AI in liver imaging, with an emphasis on feature interpretability, multimodal data integration and multicenter study. Taken together, it is revealed that AI methodologies, together with the large volume of available medical image data, might impact the future of liver disease care.

Machine learning approach for proton range verification using real-time prompt gamma imaging with Compton cameras: addressing the total deposited energy information gap

Objective.Compton camera imaging shows promise as a range verification technique in proton therapy. This work aims to assess the performance of a machine learning model in Compton camera imaging for proton beam range verification improvement.Approach.The presented approach was used to recognize Compton events and estimate more accurately the prompt gamma (PG) energy in the Compton camera to reconstruct the PGs emission profile during proton therapy. This work reports the results obtained from the Geant4 simulation for a proton beam impinging on a polymethyl methacrylate (PMMA) target. To validate the versatility of such an approach, the produced PG emissions interact with a scintillating fiber-based Compton camera.Main results.A trained multilayer perceptron (MLP) neural network shows that it was possible to achieve a notable three-fold increase in the signal-to-total ratio. Furthermore, after event selection by the trained MLP, the loss of full-energy PGs was compensated by means of fitting an MLP energy regression model to the available data from true Compton (signal) events, predicting more precisely the total deposited energy for Compton events with incomplete energy deposition.Significance.A considerable improvement in the Compton camera's performance was demonstrated in determining the distal falloff and identifying a few millimeters of target displacements. This approach has shown great potential for enhancing online proton range monitoring with Compton cameras in future clinical applications.

Exploiting the Photo-Physical Properties of Metal Halide Perovskite Nanocrystals for Bioimaging

Perovskite nanomaterials have recently been exploited for bioimaging applications due to their unique photo-physical properties, including high absorbance, good photostability, narrow emissions, and nonlinear optical properties. These attributes outperform conventional fluorescent materials such as organic dyes and metal chalcogenide quantum dots and endow them with the potential to reshape a wide array of bioimaging modalities. Yet, their full potential necessitates a deep grasp of their structure-attribute relationship and strategies for enhancing water stability through surface engineering for meeting the stringent and unique requirements of each individual imaging modality. This review delves into this evolving frontier, highlighting how their distinctive photo-physical properties can be leveraged and optimized for various bioimaging modalities, including visible light imaging, near-infrared imaging, and super-resolution imaging.

Practical Applications of Artificial Intelligence in Spine Imaging: A Review

Artificial intelligence (AI), a transformative technology with unprecedented potential in medical imaging, can be applied to various spinal pathologies. AI-based approaches may improve imaging efficiency, diagnostic accuracy, and interpretation, which is essential for positive patient outcomes. This review explores AI algorithms, techniques, and applications in spine imaging, highlighting diagnostic impact and challenges with future directions for integrating AI into spine imaging workflow.

Autoimmune disorders of the gastrointestinal tract: Review of radiological appearances

Autoimmune gastrointestinal (GI) disorders comprise a heterogeneous group of diseases with non-specific clinical manifestations. These are divided into primary and secondary. A high index of clinical suspicion complemented with endoscopic and radiological imaging may allow early diagnosis. Due to the relatively low incidence of autoimmune disorder, the imaging literature is sparse. In this review, we outline the pathogenesis, classification, and imaging appearances of autoimmune GI disorders.

Integrated lithium niobate microwave photonic processing engine

Integrated microwave photonics (MWP) is an intriguing technology for the generation, transmission and manipulation of microwave signals in chip-scale optical systems1,2. In particular, ultrafast processing of analogue signals in the optical domain with high fidelity and low latency could enable a variety of applications such as MWP filters3-5, microwave signal processing6-9 and image recognition10,11. An ideal integrated MWP processing platform should have both an efficient and high-speed electro-optic modulation block to faithfully perform microwave-optic conversion at low power and also a low-loss functional photonic network to implement various signal-processing tasks. Moreover, large-scale, low-cost manufacturability is required to monolithically integrate the two building blocks on the same chip. Here we demonstrate such an integrated MWP processing engine based on a 4 inch wafer-scale thin-film lithium niobate platform. It can perform multipurpose tasks with processing bandwidths of up to 67 GHz at complementary metal-oxide-semiconductor (CMOS)-compatible voltages. We achieve ultrafast analogue computation, namely temporal integration and differentiation, at sampling rates of up to 256 giga samples per second, and deploy these functions to showcase three proof-of-concept applications: solving ordinary differential equations, generating ultra-wideband signals and detecting edges in images. We further leverage the image edge detector to realize a photonic-assisted image segmentation model that can effectively outline the boundaries of melanoma lesion in medical diagnostic images. Our ultrafast lithium niobate MWP engine could provide compact, low-latency and cost-effective solutions for future wireless communications, high-resolution radar and photonic artificial intelligence.

Cardiac Sarcoidosis

Cardiac involvement is a major cause of morbidity and mortality in patients with sarcoidosis. It is important to distinguish between clinical manifest diseases from clinically silent diseases. Advanced cardiac imaging studies are crucial in the diagnostic pathway. In suspected isolated cardiac sarcoidosis, it's key to rule out alternative diagnoses. Therapeutic options can be divided into immunosuppressive agents, guideline-directed medical therapy, antiarrhythmic medications, device/ablation therapy, and heart transplantation.

Medical image identification methods: A review

The identification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Medical image data mainly include electronic health record data and gene information data, etc. Although intelligent imaging provided a good scheme for medical image analysis over traditional methods that rely on the handcrafted features, it remains challenging due to the diversity of imaging modalities and clinical pathologies. Many medical image identification methods provide a good scheme for medical image analysis. The concepts pertinent of methods, such as the machine learning, deep learning, convolutional neural networks, transfer learning, and other image processing technologies for medical image are analyzed and summarized in this paper. We reviewed these recent studies to provide a comprehensive overview of applying these methods in various medical image analysis tasks, such as object detection, image classification, image registration, segmentation, and other tasks. Especially, we emphasized the latest progress and contributions of different methods in medical image analysis, which are summarized base on different application scenarios, including classification, segmentation, detection, and image registration. In addition, the applications of different methods are summarized in different application area, such as pulmonary, brain, digital pathology, brain, skin, lung, renal, breast, neuromyelitis, vertebrae, and musculoskeletal, etc. Critical discussion of open challenges and directions for future research are finally summarized. Especially, excellent algorithms in computer vision, natural language processing, and unmanned driving will be applied to medical image recognition in the future.

Advancing Diagnostics and Patient Care: The Role of Biomarkers in Radiology

The integration of biomarkers into medical practice has revolutionized the field of radiology, allowing for enhanced diagnostic accuracy, personalized treatment strategies, and improved patient care outcomes. This review offers radiologists a comprehensive understanding of the diverse applications of biomarkers in medicine. By elucidating the fundamental concepts, challenges, and recent advancements in biomarker utilization, it will serve as a bridge between the disciplines of radiology and epidemiology. Through an exploration of various biomarker types, such as imaging biomarkers, molecular biomarkers, and genetic markers, I outline their roles in disease detection, prognosis prediction, and therapeutic monitoring. I also discuss the significance of robust study designs, blinding, power and sample size calculations, performance metrics, and statistical methodologies in biomarker research. By fostering collaboration between radiologists, statisticians, and epidemiologists, I hope to accelerate the translation of biomarker discoveries into clinical practice, ultimately leading to improved patient care.

Radiomics in Musculoskeletal Tumors

Sarcomas are heterogeneous rare tumors predominantly affecting the musculoskeletal (MSK) system. Due to significant variations in their natural history and variable response to conventional treatments, the discovery of novel diagnostic and prognostic biomarkers to guide therapeutic decision-making is an active and ongoing field of research. As new cellular, molecular, and metabolic biomarkers continue to be discovered, quantitative radiologic imaging is becoming increasingly important in sarcoma management. Radiomics offers the potential for discovering novel imaging diagnostic and predictive biomarkers using standard-of-care medical imaging. In this review, we detail the core concepts of radiomics and the application of radiomics to date in MSK sarcoma research. Also described are specific challenges related to radiomic studies, as well as viewpoints on clinical adoption and future perspectives in the field.

Magneto-acoustic protein nanostructures for non-invasive imaging of tissue mechanics in vivo

Measuring cellular and tissue mechanics inside intact living organisms is essential for interrogating the roles of force in physiological and disease processes. Current agents for studying the mechanobiology of intact, living organisms are limited by poor light penetration and material stability. Magnetomotive ultrasound is an emerging modality for real-time in vivo imaging of tissue mechanics. Nonetheless, it has poor sensitivity and spatiotemporal resolution. Here we describe magneto-gas vesicles (MGVs), protein nanostructures based on gas vesicles and magnetic nanoparticles that produce differential ultrasound signals in response to varying mechanical properties of surrounding tissues. These hybrid nanomaterials significantly improve signal strength and detection sensitivity. Furthermore, MGVs enable non-invasive, long-term and quantitative measurements of mechanical properties within three-dimensional tissues and in vivo fibrosis models. Using MGVs as novel contrast agents, we demonstrate their potential for non-invasive imaging of tissue elasticity, offering insights into mechanobiology and its application to disease diagnosis and treatment.

Shape Anisotropy-Governed High-Performance Nanomagnetosol for In Vivo Magnetic Particle Imaging of Lungs

Caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has shown extensive lung manifestations in vulnerable individuals, putting lung imaging and monitoring at the forefront of early detection and treatment. Magnetic particle imaging (MPI) is an imaging modality, which can bring excellent contrast, sensitivity, and signal-to-noise ratios to lung imaging for the development of new theranostic approaches for respiratory diseases. Advances in MPI tracers would offer additional improvements and increase the potential for clinical translation of MPI. Here, a high-performance nanotracer based on shape anisotropy of magnetic nanoparticles is developed and its use in MPI imaging of the lung is demonstrated. Shape anisotropy proves to be a critical parameter for increasing signal intensity and resolution and exceeding those properties of conventional spherical nanoparticles. The 0D nanoparticles exhibit a 2-fold increase, while the 1D nanorods have a > 5-fold increase in signal intensity when compared to VivoTrax. Newly designed 1D nanorods displayed high signal intensities and excellent resolution in lung images. A spatiotemporal lung imaging study in mice revealed that this tracer offers new opportunities for monitoring disease and guiding intervention.

Efficacy and safety of near-infrared fluorescence identification of the thoracic duct during left lateral neck dissection

BACKGROUND

Thoracic duct leaks occur in up to 5% of left lateral neck dissections. No one imaging modality is routinely used to identify the thoracic duct intraoperatively. The goal of our study was to evaluate the efficacy and safety of indocyanine green lymphangiography for intraoperative identification of the thoracic duct compared to traditional methods using ambient and evaluate the optimal timing of indocyanine green administration.

METHODS

We enrolled all patients who underwent left lateral neck dissection at our institution from 2018 to 2022 in this prospective clinical trial. After indocyanine green injection into the dorsum of the foot, we performed intraoperative imaging was performed with a near-infrared fluorescence camera. We reported the data using descriptive statistics.

RESULTS

Of the 42 patients we enrolled, 14 had prior neck surgery, and 3 had prior external beam radiation. We visualized the thoracic duct with ambient light in 48% of patients and with near-infrared fluorescence visualization in 64%. In 17% of patients, we could identify the thoracic duct only using near-infrared fluorescence visualization, which occurred within 3 minutes of injection, and were required to re-dose 5 patients. We visualized the thoracic duct with near-infrared fluorescence in all patients with prior neck radiation and 77% of patients with prior neck surgery. One adverse reaction occurred (hypotension), and 5 intraoperative thoracic duct injuries occurred that were ligated. There with no chylous fistulas postoperatively.

CONCLUSION

This trial demonstrates that near-infrared fluorescence identification of the thoracic duct is feasible and safe with indocyanine green lymphangiography, even in patients with prior neck surgery or radiation.

On the challenges and perspectives of foundation models for medical image analysis

This article discusses the opportunities, applications and future directions of large-scale pretrained models, i.e., foundation models, which promise to significantly improve the analysis of medical images. Medical foundation models have immense potential in solving a wide range of downstream tasks, as they can help to accelerate the development of accurate and robust models, reduce the dependence on large amounts of labeled data, preserve the privacy and confidentiality of patient data. Specifically, we illustrate the "spectrum" of medical foundation models, ranging from general imaging models, modality-specific models, to organ/task-specific models, and highlight their challenges, opportunities and applications. We also discuss how foundation models can be leveraged in downstream medical tasks to enhance the accuracy and efficiency of medical image analysis, leading to more precise diagnosis and treatment decisions.

Motion-resistant three-wavelength spatial frequency domain imaging system with ambient light suppression using an 8-tap CMOS image sensor

Significance

We present a motion-resistant three-wavelength spatial frequency domain imaging (SFDI) system with ambient light suppression using an 8-tap complementary metal-oxide semiconductor (CMOS) image sensor (CIS) developed at Shizuoka University. The system addresses limitations in conventional SFDI systems, enabling reliable measurements in challenging imaging scenarios that are closer to real-world conditions.

Aim

Our study demonstrates a three-wavelength SFDI system based on an 8-tap CIS. We demonstrate and evaluate the system's capability of mitigating motion artifacts and ambient light bias through tissue phantom reflectance experiments and in vivo volar forearm experiments.

Approach

We incorporated the Hilbert transform to reduce the required number of projected patterns per wavelength from three to two per spatial frequency. The 8-tap image sensor has eight charge storage diodes per pixel; therefore, simultaneous image acquisition of eight images based on multi-exposure is possible. Taking advantage of this feature, the sensor simultaneously acquires images for planar illumination, sinusoidal pattern projection at three wavelengths, and ambient light. The ambient light bias is eliminated by subtracting the ambient light image from the others. Motion artifacts are suppressed by reducing the exposure and projection time for each pattern while maintaining sufficient signal levels by repeating the exposure. The system is compared to a conventional SFDI system in tissue phantom experiments and then in vivo measurements of human volar forearms.

Results

The 8-tap image sensor-based SFDI system achieved an acquisition rate of 9.4 frame sets per second, with three repeated exposures during each accumulation period. The diffuse reflectance maps of three different tissue phantoms using the conventional SFDI system and the 8-tap image sensor-based SFDI system showed good agreement except for high scattering phantoms. For the in vivo volar forearm measurements, our system successfully measured total hemoglobin concentration, tissue oxygen saturation, and reduced scattering coefficient maps of the subject during motion (16.5 cm/s) and under ambient light (28.9 lx), exhibiting fewer motion artifacts compared with the conventional SFDI.

Conclusions

We demonstrated the potential for motion-resistant three-wavelength SFDI system with ambient light suppression using an 8-tap CIS.

In situ theranostic platform combining highly localized magnetic fluid hyperthermia, magnetic particle imaging, and thermometry in 3D

Theranostic platforms, combining diagnostic and therapeutic approaches within one system, have garnered interest in augmenting invasive surgical, chemical, and ionizing interventions. Magnetic particle imaging (MPI) offers a quite recent alternative to established radiation-based diagnostic modalities with its versatile tracer material (superparamagnetic iron oxide nanoparticles, SPION). It also offers a bimodal theranostic framework that can combine tomographic imaging with therapeutic techniques using the very same SPION. Methods: We show the interleaved combination of MPI-based imaging, therapy (highly localized magnetic fluid hyperthermia (MFH)) and therapy safety control (MPI-based thermometry) within one theranostic platform in all three spatial dimensions using a commercial MPI system and a custom-made heating insert. The heating characteristics as well as theranostic applications of the platform were demonstrated by various phantom experiments using commercial SPION. Results: We have shown the feasibility of an MPI-MFH-based theranostic platform by demonstrating high spatial control of the therapeutic target, adequate MPI-based thermometry, and successful in situ interleaved MPI-MFH application. Conclusions: MPI-MFH-based theranostic platforms serve as valuable tools that enable the synergistic integration of diagnostic and therapeutic approaches. The transition into in vivo studies will be essential to further validate their potential, and it holds promising prospects for future advancements.

Recent developments and applications of ambient mass spectrometry imaging in pharmaceutical research: an overview

The application of ambient mass spectrometry imaging "MSI" is expanding in the areas of fundamental research on drug delivery and multiple phases of the process of identifying and developing drugs. Precise monitoring of a drug's pharmacological workflows, such as intake, distribution, metabolism, and discharge, is made easier by MSI's ability to determine the concentrations of the initiating drug and its metabolites across dosed samples without losing spatial data. Lipids, glycans, and proteins are just a few of the many phenotypes that MSI may be used to concurrently examine. Each of these substances has a particular distribution pattern and biological function throughout the body. MSI offers the perfect analytical tool for examining a drug's pharmacological features, especially in vitro and in vivo effectiveness, security, probable toxic effects, and putative molecular pathways, because of its high responsiveness in chemical and physical environments. The utilization of MSI in the field of pharmacy has further extended from the traditional tissue examination to the early stages of drug discovery and development, including examining the structure-function connection, high-throughput capabilities in vitro examination, and ex vivo research on individual cells or tumor spheroids. Additionally, an enormous array of endogenous substances that may function as tissue diagnostics can be scanned simultaneously, giving the specimen a highly thorough characterization. Ambient MSI techniques are soft enough to allow for easy examination of the native sample to gather data on exterior chemical compositions. This paper provides a scientific and methodological overview of ambient MSI utilization in research on pharmaceuticals.

In Vivo High-Contrast Biomedical Imaging in the Second Near-Infrared Window Using Ultrabright Rare-Earth Nanoparticles

Intravital luminescence imaging in the second near-infrared window (NIR-II) enables noninvasive deep-tissue imaging with high spatiotemporal resolution of live mammals because of the properties of suppressed light scattering and diminished autofluorescence in the long-wavelength region. Herein, we present the synthesis of a downconversion luminescence rare-earth nanocrystal with a core-shell-shell structure (NaYF4@NaYbF4:Er,Ce@NaYF4:Ca). The structure efficiently maximized the doping concentration of the sensitizers and increased Er3+ luminescence while preventing cross relaxation. Furthermore, Ce3+ doping in the middle layer efficiently limited the upconversion pathway and increased downconversion by 24-fold to produce bright 1550 nm luminescence under 975 nm excitation. Finally, optimizing the inert shell coating of NaYF4:Ca and liposome encapsulation reduced the luminescence quenching impact by water and improved biological metabolism. Thus, our synthesized biocompatible, ultrabright NIR-II probes provide high contrast and resolution for through-scalp and through-skull luminescence imaging of mice cerebral vasculature without craniotomy as well as imaging of mouse hindlimb microvessels.

State-of-the-art mass spectrometry imaging applications in biomedical research

Mass spectrometry imaging has advanced from a niche technique to a widely applied spatial biology tool operating at the forefront of numerous fields, most notably making a significant impact in biomedical pharmacological research. The growth of the field has gone hand in hand with an increase in publications and usage of the technique by new laboratories, and consequently this has led to a shift from general MSI reviews to topic-specific reviews. Given this development, we see the need to recapitulate the strengths of MSI by providing a more holistic overview of state-of-the-art MSI studies to provide the new generation of researchers with an up-to-date reference framework. Here we review scientific advances for the six largest biomedical fields of MSI application (oncology, pharmacology, neurology, cardiovascular diseases, endocrinology, and rheumatology). These publications thereby give examples for at least one of the following categories: they provide novel mechanistic insights, use an exceptionally large cohort size, establish a workflow that has the potential to become a high-impact methodology, or are highly cited in their field. We finally have a look into new emerging fields and trends in MSI (immunology, microbiology, infectious diseases, and aging), as applied MSI is continuously broadening as a result of technological breakthroughs.

Switchable LED-based laparoscopic multispectral system for rapid high-resolution perfusion imaging

Significance

Multispectral imaging (MSI) is an approach for real-time, quantitative, and non-invasive tissue perfusion measurements. Current laparoscopic systems based on mosaic sensors or filter wheels lack high spatial resolution or acceptable frame rates.

Aim

To develop a laparoscopic system for MSI-based color video and tissue perfusion imaging during gastrointestinal surgery without compromising spatial or temporal resolution.

Approach

The system was built with 14 switchable light-emitting diodes in the visible and near-infrared spectral range, a 4K image sensor, and a 10 mm laparoscope. Illumination patterns were created for tissue oxygenation and hemoglobin content monitoring. The system was calibrated to a clinically approved laparoscopic hyperspectral system using linear regression models and evaluated in an occlusion study with 36 volunteers.

Results

The root mean squared errors between the MSI and reference system were 0.073 for hemoglobin content, 0.039 for oxygenation in deeper tissue layers, and 0.093 for superficial oxygenation. The spatial resolution at a working distance of 45 mm was 156    μ m . The effective frame rate was 20 fps.

Conclusions

High-resolution perfusion monitoring was successfully achieved. Hardware optimizations will increase the frame rate. Parameter optimizations through alternative illumination patterns, regression, or assumed tissue models are planned. Intraoperative measurements must confirm the suitability during surgery.

An observation of short-wave near-infrared hyperspectral imaging in tracking of invisible post-traumatic subcutaneous lesions

Post-traumatic soft tissue damage could persist for an extended period, and the non-traumatic side could be affected by indirect consequences. Hyperspectral imaging soft abundance scorer can identify these concealed and asymptomatic lesions.

Design of a Compact Hologram System Capable of 3D Lesion Diagnosis in Clinic

MOTIVATION

This paper proposes a small-sized hologram system for the 3D imaging of lesions in a clinical environment. In a general hologram system, the distance between the beam-generating device and the screen (400 mm) and the size of the screen must be increased proportionally to obtain excellent image quality. However, in a clinical environment, the beam spread distance and screen size must be reduced. This paper proposes a method for reducing the beam divergence distance and screen size for clinical applications.

METHODS

To reduce the beam spread distance and screen size, a beam prism with a 45° refractive index is used to reduce the beam spread distance by 1/3. The direction of the bent light must be adjusted such that it can reach the screen accurately. However, because the reflected light may be refracted owing to the material properties of the mirror and cause loss, this problem can be solved by using a full reflection mirror.

RESULTS

The beam spread distance of the designed hologram system is 200 mm. The types of lesions obtained from the 3D images of the hologram include the lung, liver, and colon. The image resolution is 300 × 145.

CONCLUSION

If the proposed method is used in a clinical environment, doctors can improve their understanding of the patient quickly and efficiently; thereby, shortening the treatment time. The proposed hologram system is expected to be useful in treatment rooms, operating rooms, and educational programs in medical schools.

Functional fluorescent nanomaterials for the detection, diagnosis and control of bacterial infection and biofilm formation: Insight towards mechanistic aspects and advanced applications

Infectious diseases resulting from the high pathogenic potential of several bacteria possesses a major threat to human health and safety. Traditional methods used for screening of these microorganisms face major issues with respect to detection time, selectivity and specificity which may delay treatment for critically ill patients past the optimal time. Thus, a convincing and essential need exists to upgrade the existing methodologies for the fast detection of bacteria. In this context, increasing number of newly emerging nanomaterials (NMs) have been discovered for their effective use and applications in the area of diagnosis in bacterial infections. Recently, functional fluorescent nanomaterials (FNMs) are extensively explored in the field of biomedical research, particularly in developing new diagnostic tools, nanosensors, specific imaging modalities and targeted drug delivery systems for bacterial infection. It is interesting to note that organic fluorophores and fluorescent proteins have played vital role for imaging and sensing technologies for long, however, off lately fluorescent nanomaterials are increasingly replacing these due to the latter's unprecedented fluorescence brightness, stability in the biological environment, high quantum yield along with high sensitivity due to enhanced surface property etc. Again, taking advantage of their photo-excitation property, these can also be used for either photothermal and photodynamic therapy to eradicate bacterial infection and biofilm formation. Here, in this review, we have paid particular attention on summarizing literature reports on FNMs which includes studies detailing fluorescence-based bacterial detection methodologies, antibacterial and antibiofilm applications of the same. It is expected that the present review will attract the attention of the researchers working in this field to develop new engineered FNMs for the comprehensive diagnosis and treatment of bacterial infection and biofilm formation.

Progressive Pretraining Network for 3D System Matrix Calibration in Magnetic Particle Imaging

Magnetic particle imaging (MPI) is an emerging technique for determining magnetic nanoparticle distributions in biological tissues. Although system-matrix (SM)-based image reconstruction offers higher image quality than the X-space-based approach, the SM calibration measurement is time-consuming. Additionally, the SM should be recalibrated if the tracer's characteristics or the magnetic field environment change, and repeated SM measurement further increase the required labor and time. Therefore, fast SM calibration is essential for MPI. Existing calibration methods commonly treat each row of the SM as independent of the others, but the rows are inherently related through the coil channel and frequency index. As these two elements can be regarded as additional multimodal information, we leverage the transformer architecture with a self-attention mechanism to encode them. Although the transformer has shown superiority in multimodal fusion learning across several fields, its high complexity may lead to overfitting when labeled data are scarce. Compared with labeled SM (i.e., full size), low-resolution SM data can be easily obtained, and fully using such data may alleviate overfitting. Accordingly, we propose a pseudo-label-based progressive pretraining strategy to leverage unlabeled data. Our method outperforms existing calibration methods on a public real-world OpenMPI dataset and simulation dataset. Moreover, our method improves the resolution of two in-house MPI scanners without requiring full-size SM measurements. Ablation studies confirm the contributions of modeling SM inter-row relations and the proposed pretraining strategy.

ACR Appropriateness Criteria® Congenital or Acquired Heart Disease

Pediatric heart disease is a large and diverse field with an overall prevalence estimated at 6 to 13 per 1,000 live births. This document discusses appropriateness of advanced imaging for a broad range of variants. Diseases covered include tetralogy of Fallot, transposition of great arteries, congenital or acquired pediatric coronary artery abnormality, single ventricle, aortopathy, anomalous pulmonary venous return, aortopathy and aortic coarctation, with indications for advanced imaging spanning the entire natural history of the disease in children and adults, including initial diagnosis, treatment planning, treatment monitoring, and early detection of complications. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

ACR Appropriateness Criteria® Lower Extremity Chronic Venous Disease

Lower extremity venous insufficiency is a chronic medical condition resulting from primary valvular incompetence or, less commonly, prior deep venous thrombosis or extrinsic venous obstruction. Lower extremity chronic venous disease has a high prevalence with a related socioeconomic burden. In the United States, over 11 million males and 22 million females 40 to 80 years of age have varicose veins, with over 2 million adults having advanced chronic venous disease. The high cost to the health care system is related to the recurrent nature of venous ulcerative disease, with total treatment costs estimated >$2.5 billion per year in the United States, with at least 20,556 individuals with newly diagnosed venous ulcers yearly. Various diagnostic and treatment strategies are in place for lower extremity chronic venous disease and are discussed in this document. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Solid and Vascular Neck Masses in Children

Neck masses are frequent in the pediatric population and are usually divided into congenital, inflammatory, and neoplastic. Many of these lesions are cystic and are often benign. Solid masses and vascular lesions are relatively less common, and the imaging appearances can be similar. This article reviews the clinical presentation and imaging patterns of pediatric solid and vascular neck masses.

ACR Appropriateness Criteria® Incidentally Detected Indeterminate Pulmonary Nodule

Incidental pulmonary nodules are common. Although the majority are benign, most are indeterminate for malignancy when first encountered making their management challenging. CT remains the primary imaging modality to first characterize and follow-up incidental lung nodules. This document reviews available literature on various imaging modalities and summarizes management of indeterminate pulmonary nodules detected incidentally. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

ACR Appropriateness Criteria® Pulsatile Abdominal Mass, Suspected Abdominal Aortic Aneurysm: 2023 Update

Abdominal aortic aneurysm (AAA) is defined as abnormal dilation of the infrarenal abdominal aortic diameter to 3.0 cm or greater. The natural history of AAA consists of progressive expansion and potential rupture. Although most AAAs are clinically silent, a pulsatile abdominal mass identified on physical examination may indicate the presence of an AAA. When an AAA is suspected, an imaging study is essential to confirm the diagnosis. This document reviews the relative appropriateness of various imaging procedures for the initial evaluation of suspected AAA. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

ACR Appropriateness Criteria® Imaging After Total Knee Arthroplasty: 2023 Update

Total knee arthroplasty is the most commonly performed joint replacement procedure in the United States. This manuscript will discuss the recommended imaging modalities for six clinical variants; 1. follow-up of symptomatic or asymptomatic patients with a total knee arthroplasty. Initial imaging, 2. Suspected infection after total knee arthroplasty. Additional imaging following radiographs, 3. Pain after total knee arthroplasty. Infection excluded. Suspect aseptic loosening or osteolysis or instability. Additional imaging following radiographs, 4. Pain after total knee arthroplasty. Suspect periprosthetic or hardware fracture. Additional imaging following radiographs, 5. Pain after total knee arthroplasty. Measuring component rotation. Additional imaging following radiographs, and 6. Pain after total knee arthroplasty. Suspect periprosthetic soft-tissue abnormality unrelated to infection, including quadriceps or patellar tendinopathy. Additional imaging following radiographs. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

ACR Appropriateness Criteria® Tinnitus: 2023 Update

Tinnitus is abnormal perception of sound and has many subtypes. Clinical evaluation, audiometry, and otoscopy should be performed before ordering any imaging, as the choice of imaging will depend on various factors. Type of tinnitus (pulsatile or nonpulsatile) and otoscopy findings of a vascular retrotympanic lesion are key determinants to guide the choice of imaging studies. High-resolution CT temporal bone is an excellent tool to detect glomus tumors, abnormal course of vessels, and some other abnormalities when a vascular retrotympanic lesion is seen on otoscopy. CTA or a combination of MR and MRA/MRV are used to evaluate arterial or venous abnormalities like dural arteriovenous fistula, arteriovenous malformation, carotid stenosis, dural sinus stenosis, and bony abnormalities like sigmoid sinus wall abnormalities in cases of pulsatile tinnitus without a vascular retrotympanic lesion. MR of the brain is excellent in detecting mass lesions such as vestibular schwannomas in cases of unilateral nonpulsatile tinnitus. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

ACR Appropriateness Criteria® Abnormal Liver Function Tests

Liver function tests are commonly obtained in symptomatic and asymptomatic patients. Various overlapping lab patterns can be seen due to derangement of hepatocytes and bile ducts function. Imaging tests are pursued to identify underlying etiology and guide management based on the lab results. Liver function tests may reveal mild, moderate, or severe hepatocellular predominance and can be seen in alcoholic and nonalcoholic liver disease, acute hepatitis, and acute liver injury due to other causes. Cholestatic pattern with elevated alkaline phosphatase with or without elevated γ-glutamyl transpeptidase can be seen with various causes of obstructive biliopathy. Acute or subacute cholestasis with conjugated or unconjugated hyperbilirubinemia can be seen due to prehepatic, intrahepatic, or posthepatic causes. We discuss the initial and complementary imaging modalities to be used in clinical scenarios presenting with abnormal liver function tests. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Hyper-Branched Gold Nanoconstructs for Photoacoustic Imaging in the Near-Infrared Optical Window

In plasmonic nanoconstructs (NCs), fine-tuning interparticle interactions at the subnanoscale offer enhanced electromagnetic and thermal responses in the near-infrared (NIR) wavelength range. Due to tunable electromagnetic and thermal characteristics, NCs can be excellent photoacoustic (PA) imaging contrast agents. However, engineering plasmonic NCs that maximize light absorption efficiency across multiple polarization directions, i.e., exhibiting blackbody absorption behavior, remains challenging. Herein, we present the synthesis, computational simulation, and characterization of hyper-branched gold nanoconstructs (HBGNCs) as a highly efficient PA contrast agent. HBGNCs exhibit remarkable optical properties, including strong NIR absorption, high absorption efficiency across various polarization angles, and superior photostability compared to conventional standard plasmonic NC-based contrast agents such as gold nanorods and gold nanostars. In vitro and in vivo experiments confirm the suitability of HBGNCs for cancer imaging, showcasing their potential as reliable PA contrast agents and addressing the need for enhanced imaging contrast and stability in bioimaging applications.

Artificial Intelligence and Infectious Disease Imaging

The mass production of the graphics processing unit and the coronavirus disease 2019 (COVID-19) pandemic have provided the means and the motivation, respectively, for rapid developments in artificial intelligence (AI) and medical imaging techniques. This has led to new opportunities to improve patient care but also new challenges that must be overcome before these techniques are put into practice. In particular, early AI models reported high performances but failed to perform as well on new data. However, these mistakes motivated further innovation focused on developing models that were not only accurate but also stable and generalizable to new data. The recent developments in AI in response to the COVID-19 pandemic will reap future dividends by facilitating, expediting, and informing other medical AI applications and educating the broad academic audience on the topic. Furthermore, AI research on imaging animal models of infectious diseases offers a unique problem space that can fill in evidence gaps that exist in clinical infectious disease research. Here, we aim to provide a focused assessment of the AI techniques leveraged in the infectious disease imaging research space, highlight the unique challenges, and discuss burgeoning solutions.

Artificial intelligence in diagnostic pathology

Digital pathology (DP) is being increasingly employed in cancer diagnostics, providing additional tools for faster, higher-quality, accurate diagnosis. The practice of diagnostic pathology has gone through a staggering transformation wherein new tools such as digital imaging, advanced artificial intelligence (AI) algorithms, and computer-aided diagnostic techniques are being used for assisting, augmenting and empowering the computational histopathology and AI-enabled diagnostics. This is paving the way for advancement in precision medicine in cancer. Automated whole slide imaging (WSI) scanners are now rendering diagnostic quality, high-resolution images of entire glass slides and combining these images with innovative digital pathology tools is making it possible to integrate imaging into all aspects of pathology reporting including anatomical, clinical, and molecular pathology. The recent approvals of WSI scanners for primary diagnosis by the FDA as well as the approval of prostate AI algorithm has paved the way for starting to incorporate this exciting technology for use in primary diagnosis. AI tools can provide a unique platform for innovations and advances in anatomical and clinical pathology workflows. In this review, we describe the milestones and landmark trials in the use of AI in clinical pathology with emphasis on future directions.

Review of microwave imaging algorithms for stroke detection

Microwave imaging is one of the rapidly developing frontier disciplines in the field of modern medical imaging. The development of microwave imaging algorithms for reconstructing stroke images is discussed in this paper. Compared with traditional stroke detection and diagnosis techniques, microwave imaging has the advantages of low price and no ionizing radiation hazards. The research hotspots of microwave imaging algorithms in the field of stroke are mainly reflected in the design and improvement of microwave tomography, radar imaging, and deep learning imaging. However, the current research lacks the analysis and combing of microwave imaging algorithms. In this paper, the development of common microwave imaging algorithms is reviewed. The concept, research status, current research hotspots and difficulties, and future development trends of microwave imaging algorithms are systematically expounded. The microwave antenna is used to collect scattered signals, and a series of microwave imaging algorithms are used to reconstruct the stroke image. The classification diagram and flow chart of the algorithms are shown in this Figure. (The classification diagram and flow chart are based on the microwave imaging algorithms.).

The synergy of cybernetical intelligence with medical image analysis for deep medicine: A methodological perspective

CONCEPTUAL INTRODUCTION

To introduce the concept of cybernetical intelligence, deep learning, development history, international research, algorithms, and the application of these models in smart medical image analysis and deep medicine are reviewed in this paper. This study also defines the terminologies for cybernetical intelligence, deep medicine, and precision medicine.

REVIEW OF METHODS

Through literature research and knowledge reorganization, this review explores the fundamental concepts and practical applications of various deep learning techniques and cybernetical intelligence by conducting extensive literature research and reorganizing existing knowledge in medical imaging and deep medicine. The discussion mainly centers on the applications of classical models in this field and addresses the limitations and challenges of these basic models.

EVALUATION AND DISCUSSIONS

In this paper, the more comprehensive overview of the classical structural modules in convolutional neural networks is described in detail from the perspective of cybernetical intelligence in deep medicine. The results and data of major research contents of deep learning are consolidated and summarized.

CONCLUSION

There are some problems in machine learning internationally, such as insufficient research techniques, unsystematic research methods, incomplete research depth, and incomplete evaluation research. Some suggestions are given in our review to solve the problems existing in the deep learning models. Cybernetical intelligence has proven to be a valuable and promising avenue for advancing various fields, including deep medicine and personalized medicine.

Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives

Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.

Automated chronic wounds medical assessment and tracking framework based on deep learning

Chronic wounds are a latent health problem worldwide, due to high incidence of diseases such as diabetes and Hansen. Typically, wound evolution is tracked by medical staff through visual inspection, which becomes problematic for patients in rural areas with poor transportation and medical infrastructure. Alternatively, the design of software platforms for medical imaging applications has been increasingly prioritized. This work presents a framework for chronic wound tracking based on deep learning, which works on RGB images captured with smartphones, avoiding bulky and complicated acquisition setups. The framework integrates mainstream algorithms for medical image processing, including wound detection, segmentation, as well as quantitative analysis of area and perimeter. Additionally, a new chronic wounds dataset from leprosy patients is provided to the scientific community. Conducted experiments demonstrate the validity and accuracy of the proposed framework, with up to 84.5% in precision.

Quantitative Assessment of Hepatic Steatosis Using Label-Free Multiphoton Imaging and Customized Image Processing Program

Nonalcoholic fatty liver disease is rapidly becoming one of the most common causes of chronic liver disease worldwide and is the leading cause of liver-related morbidity and mortality. A quantitative assessment of the degree of steatosis would be more advantageous for diagnostic evaluation and exploring the patterns of disease progression. Here, multiphoton microscopy, based on the second harmonic generation and 2-photon excited fluorescence, was used to label-free image the samples of nonalcoholic fatty liver. Imaging results confirm that multiphoton microscopy is capable of directly visualizing important pathologic features such as normal hepatocytes, hepatic steatosis, Mallory bodies, necrosis, inflammation, collagen deposition, microvessel, and so on and is a reliable auxiliary tool for the diagnosis of nonalcoholic fatty liver disease. Furthermore, we developed an image segmentation algorithm to simultaneously assess hepatic steatosis and fibrotic changes, and quantitative results reveal that there is a correlation between the degree of steatosis and collagen content. We also developed a feature extraction program to precisely display the spatial distribution of hepatocyte steatosis in tissues. These studies may be beneficial for a better clinical understanding of the process of steatosis as well as for exploring possible therapeutic targets.

Analyzing the influence of oblique incidence on quantitative backscattering tissue polarimetry: a pilot ex vivo study

Significance

Among the available polarimetric techniques, backscattering Mueller matrix (MM) polarimetry provides a promising non-contact and quantitative tool for in vivo tissue detection and clinical diagnosis. To eliminate the surface reflection from the sample cost-effectively, the non-collinear backscattering MM imaging setup always has an oblique incidence. Meanwhile, for practical organ cavities imaged using polarimetric gastrointestinal endoscopy, the uneven tissue surfaces can induce various relative oblique incidences inevitably, which can affect the polarimetry in a complicated manner and needs to be considered for detailed study.

Aim

The purpose of this study is to systematically analyze the influence of oblique incidence on backscattering tissue polarimetry.

Approach

We measured the MMs of experimental phantom and ex vivo tissues with different incident angles and adopted a Monte Carlo simulation program based on cylindrical scattering model for further verification and analysis. Meanwhile, the results were quantitatively evaluated using the Fourier transform, basic statistics, and frequency distribution histograms.

Results

Oblique incidence can induce different changes on non-periodic, two-periodic, and four-periodic MM elements, leading to false-positive and false-negative polarization information for tissue polarimetry. Moreover, a prominent oblique incidence can bring more dramatic signal variations, such as phase retardance and element transposition.

Conclusions

The findings presented in this study give some crucial criterions of appropriate incident angle selections for in vivo polarimetric endoscopy and other applications and can also be valuable references for studying how to minimize the influence further.