Imaging Of Anatomical Structures Research Articles

Intraoperative near-infrared fluorescence visualization of anatomical structures in general surgical and urological practice

Aim: to analyze current literature on the assessment of intraoperative near-infrared fluorescence visualization of anatomical structures in general surgical and urological practice. The experience of using intraoperative near-infrared fluorescence visualization of anatomical structures in general surgical and urological practice is analyzed. The priority areas of the method application are defined, the recent clinical studies in various fields of surgery and urology are reviewed. Conclusions. The method of intraoperative near-infrared fluorescence imaging of anatomical structures with abundant vascularization and for assessing oncological prognosis is the most promising.

ChatGPT's Capabilities for Use in Anatomy Education and Anatomy Research

ChatGPT's Capabilities for Use in Anatomy Education and Anatomy Research

Segmentation of Masticatory Muscles on Ultrasonographic Images Using Artificial Intelligence in Pediatric Population

Objective: The objective of this study is to utilize artificial intelligence for the segmentation of masticatory muscles in ultrasound images. Materials and Methods: The study comprised a cohort of 60 pediatric patients with ultrasonographic images of the masseter, anterior temporal, and lateral pterygoid muscles, 120 images for each muscle, right and left, totalling 360 muscle images. Within the context of this research, the YOLOv8-Seg deep learning model was employed to automatically conduct the segmentation of the masseter, anterior temporal, and lateral pterygoid muscles within ultrasonography images. In this study, an artificial intelligence algorithm (Roboflow, Inc., Des Moines, Iowa, USA) was developed to autonomously carry out the segmentation of the masseter, anterior temporal, and lateral pterygoid muscles. A total of 120 images for each muscle group were randomly divided into training, validation and test sets. Results: For the muscle segmentations on the test data, the true positive (TP), false positive (FP) and false negative (FN) values were 18, 0, 0 for masseter muscle, 18, 0, 0 for temporal muscle and 16, 1, 1 for lateral pterygoid muscle, respectively. The model's F1 score, precision and sensitivity values are 1.0, 1.0 and 1.0 for masseter muscle, 1.0, 1.0 and 1.0 for temporal muscle and 0.92, 0.94 and 0.94 for lateral pterygoid, respectively. Conclusion: In summary, segmentation techniques based on deep learning for analyzing ultrasonography images of anatomical structures like masticatory muscles have great potential in clinical applications. Precise segmentation of muscles through this technology can play a crucial role in the diagnosis and follow-up of diverse medical conditions and diseases.

Design and construction of an 8-channel transceiver coil array for rat imaging at 9.4 T

Ultra-high field (UHF) small animal magnetic resonance imaging (MRI) is a crucial tool permitting investigation of metabolic diseases and identification of imaging biomarkers suitable for clinical diagnosis and translation. Radiofrequency (RF) coils are critical components in enabling acquisition of high-quality rat abdomen MRI data. However, efficient RF coils with high-channel count, capable of sensitive and accelerated rat abdomen imaging at 9.4 T, are not available commercially. The SNR of the commonly-used 9.4 T birdcage coil is relatively weak, particularly in the peripheral area of the subject. In addition, the birdcage is not readily to perform parallel imaging due to unavailability of the required multiple channels. Consequently, the extended scanning duration may cause unnecessary hazards to the rat. In this work, an 8-channel transceiver coil array was designed and constructed to provide good image quality and large coverage for rat abdomen imaging at 9.4 T. The structure and the performance of the developed array was optimized and evaluated by numerical electromagnetic simulations and bench tests, respectively. The MR imaging experiments in phantoms and rat models were also performed on a Bruker 9.4 T preclinical MRI system to validate the feasibility of the proposed design. The coil array supports a one-dimensional acceleration factor up to R = 4, providing good parallel imaging capabilities. These results demonstrated that the proposed 8-channel transceiver coil array for rat imaging has the ability to obtain high spatial resolution of rat abdomen anatomical structure images at 9.4 T.

Flat Detector CT with Cerebral Pooled Blood Volume Perfusion in the Angiography Suite: From Diagnostics to Treatment Monitoring.

C-arm flat-panel detector computed tomographic (CT) imaging in the angiography suite increasingly plays an important part during interventional neuroradiological procedures. In addition to conventional angiographic imaging of blood vessels, flat detector CT (FD CT) imaging allows simultaneous 3D visualization of parenchymal and vascular structures of the brain. Next to imaging of anatomical structures, it is also possible to perform FD CT perfusion imaging of the brain by means of cerebral blood volume (CBV) or pooled blood volume (PBV) mapping during steady state contrast administration. This enables more adequate decision making during interventional neuroradiological procedures, based on real-time insights into brain perfusion on the spot, obviating time consuming and often difficult transportation of the (anesthetized) patient to conventional cross-sectional imaging modalities. In this paper we review the literature about the nature of FD CT PBV mapping in patients and demonstrate its current use for diagnosis and treatment monitoring in interventional neuroradiology.

Assessing Anatomical Changes in Male Reproductive Organs in Response to Larval Crowding Using Micro-computed Tomography Imaging

Ecological conditions shape (adaptive) responses at the molecular, anatomical, and behavioral levels. Understanding these responses is key to predict the outcomes of intra- and inter-specific competitions and the evolutionary trajectory of populations. Recent technological advances have enabled large-scale molecular (e.g., RNAseq) and behavioral (e.g., computer vision) studies, but the study of anatomical responses to ecological conditions has lagged behind. Here, we highlight the role of X-ray micro-computed tomography (micro-CT) in generating in vivo and ex vivo 3D imaging of anatomical structures, which can enable insights into adaptive anatomical responses to ecological environments. To demonstrate the application of this method, we manipulated the larval density of Drosophila melanogaster Meigen flies and applied micro-CT to investigate the anatomical responses of the male reproductive organs to varying intraspecific competition levels during development. Our data is suggestive of two classes of anatomical responses which broadly agree with sexual selection theory: increasing larval density led to testes and ejaculatory duct to be overall larger (in volume), while the volume of accessory glands and, to a lesser extent, ejaculatory duct decreased. These two distinct classes of anatomical responses might reflect shared developmental regulation of the structures of the male reproductive system. Overall, we show that micro-CT can be an important tool to advance the study of anatomical (adaptive) responses to ecological environments.

Identification of Retinal Diseases using blood vessel segmentation Based on Robinson Compass Mask and K-Means Clustering Algorithm

Abstract: In order to discover distinct infection circumstances within blood veins, medical professionals frequently examine tissue layer blood vessels using retinal images. The automatic recognition of retinal veins would make it easier to diagnose numerous diseases. As a result, the time required for comprehensive eye examinations by the optometric MD is reduced. This study's main purpose is to create attached tips for tracing blood veins in the tissue layer. To extract the retinal vas network from anatomical structure images, the proposed methodology was devised. Our method is divided into four stages: first, preprocessing in case you want to organize your dataset for segmentation; second, an image enhancement method that includes CLAHE with Sequential filter; third, a Robinson compass mask for feature extraction; and fourth, a K-means clustering approach agglomeration for higher segmentation. Lastly, a new process step that eliminates incorrectly divided remote regions was added.

Exploring Honeybee Abdominal Anatomy through Micro-CT and Novel Multi-Staining Approaches.

Simple SummaryApis mellifera or western honeybees are insects belonging to the Order Hymenoptera and the most important pollinators worldwide with great implications in natural biodiversity and agriculture due to their importance in pollination and honey production. The characterization of honeybee anatomy with precise tools will allow a better comprehension of the physiology of these insects under different biological conditions. Here, we employed Micro-computed tomography and novel staining methods to define the morphoanatomical characteristics of the worker honeybee abdomen. We defined the 3D and 2Ds structures of the midgut and hindgut and discovered a new cell type called ventricular telocyte, with possible roles in honeybee epithelium maintenance. Overall, we propose that this method will be useful for further investigation of the structure of the honeybee abdomen under a wide variety of environmental conditions.Continuous improvements in morphological and histochemical analyses of Apis mellifera could improve our understanding of the anatomy and physiology of these insects at both the cellular and tissue level. In this work, two different approaches have been performed to add new data on the abdomen of worker bees: (i) Micro-computed tomography (Micro-CT), which allows the identification of small-scale structures (micrometers) with adequate/optimal resolution and avoids sample damage and, (ii) histochemical multi-staining with Periodic Acid-Schiff-Alcian blue, Lactophenol-Saphranin O and pentachrome staining to precisely characterize the histological structures of the midgut and hindgut. Micro-CT allowed high-resolution imaging of anatomical structures of the honeybee abdomen with particular emphasis on the proventriculus and pyloric valves, as well as the connection of the sting apparatus with the terminal abdominal ganglia. Furthermore, the histochemical analyses have allowed for the first-time description of ventricular telocytes in honeybees, a cell type located underneath the midgut epithelium characterized by thin and long cytoplasmic projections called telopodes. Overall, the analysis of these images could help the detailed anatomical description of the cryptic structures of honeybees and also the characterization of changes due to abiotic or biotic stress conditions.

Implementation of tissue clearing and 3‐dimensional volumetric imaging approaches for visualization of anatomical structures in autonomic nuclei

Alterations within central nervous system (CNS) autonomic nuclei contribute to a wide array of diseases. In this context, significant advancements have been made into disease‐related molecular mechanisms in autonomic brain regions. While helpful, it is important to consider that information processing within the CNS occurs through dynamic micro‐architectural structural interactions, such astrocyte‐neuron synaptic connections. However the traditional method for evaluating CNS morphology and synaptic contacts between cells is performed with immunohistochemistry on thin brain sections. An inherent limitation of this approach is not only the narrow 2‐dimensional field of view, but also the lack of optical transparency that minimizes high‐resolution imaging of anatomical structures and cell‐cell interactions. To overcome these limitations, here we sought to optimize tissue clearing approaches combined with 3D volumetric imaging for autonomic CNS nuclei. We focused our initial efforts on the forebrain subfornical organ (SFO), a region lacking a blood‐brain‐barrier, given: 1 Alterations in the SFO have been described to contribute to numerous disease conditions; and 2 The SFO represents a unique technical challenge for 3D imaging due to the complexity of cellular processes and surrounding lateral ventricle space. As a first step, we used a “passive” CLARITY tissue approach in 8‐week‐old C57Bl/6J male mice. Mice were transcardially perfused with an acrylamide‐based hydrogel in standard fixative. Subsequently, a brain section encompassing the entire SFO (~1mm) was obtained, and a series of thermal diffusion steps was performed to place existing protein structures in a “scaffold” and remove the lipid contents ‐ rendering the tissue transparent. Whole‐mount immunohistochemistry for astrocytes was then conducted using GFAP and DAPI as a nuclei marker, followed by 3D volumetric multiphoton imaging to acquire high‐resolution visualization of the entire SFO in three planes (Figure top). In brief, this approach proved to be a significant advancement beyond standard 2D histology, and 3D visualization of intricate astrocyte processes throughout the whole SFO was possible. However, a major limitation was the extensive amount of time required to process a single sample; 2 weeks for tissue clearing and 2 weeks for primary and secondary antibody incubation. Based on this, we adapted our protocol to an “active” CLARITY approach and employed electrophoretic‐based clearing of large brain sections (n=5), which resulted in a reduction in clearing time from 2 weeks to 4 hours. It was also readily apparent that this approach rendered the tissue more transparent than passive clearing, allowing for greater visualization of astrocyte processes (Figure middle). Preliminary analysis with IMARIS software to analyze astrocyte surface area and dendritic processing (Figure bottom) revealed highly complex SFO astrocytic structures with some dendrites extending from soma at the lateral ventricle throughout and surrounding nearly the entire SFO; these processes are not visualized with standard histology. Collectively, these approaches highlight the utility of tissue‐based clearing for 3D visualization of anatomical structures within autonomic nuclei and provide a framework to understand structure‐function relationships within these brain regions in the regulation of physiological and pathophysiological conditions.

MRI and Its Implications in Dentistry

Magnetic Resonance imaging (MRI) has become one of the most powerful diagnostic tools in radiology and diagnostic sciences. It is non-invasive and non-ionizing and the images are highly sensitive and specific with excellent soft tissue contrast. It gives excellent images of anatomic structures differing in proton density and other tissue characteristics. It can be used to assess intracranial and extracranial lesions particularly those involving the soft tissues.
 MRI has the shortcoming of being prone to magnetic susceptibility difference artefacts, caused by the presence of metallic materials such as dental restorative materials, implants and orthodontic appliances. All substances when placed in a magnetic field are magnetized to a degree which varies according to their magnetic susceptibility. Non precious dental alloys have the potential of causing image deformation or image voids. 
 Dentists are not the only professionals implanting metal devices. Heart pacemakers and defibrillators, aneurysm clips, cochlear implants, insulin pumps, vascular stents and artificial joints are widely used in various branches of medicine. In some cases, it is not possible to carry out MRI due to extensive dental hardware with a high ferromagnetic content. Severe image distortion or an inability to perform the MRI scan due to dental restorations is a rare problem, but cannot be completely eliminated. This article has been written to educate the dentists on the implications of metallic dental restorations on MRI scans.
 Keywords: Magnetic resonance imaging, MRI, Dentistry, Dental materials, artefact, artifacts.

Clinical evaluation of super-resolution for brain MRI images based on generative adversarial networks

In magnetic resonance imaging (MRI), reducing long scan times is an urgent issue that could be addressed with super-resolution (SR) techniques. Most of the SR networks using deep neural networks (DNNs) have been evaluated only based on numeric metrics, and the image restoration quality for individual lesions is not evaluated sufficiently. Here, we evaluated the performances of different types of SR networks using DNNs for brain MRI, in terms of diagnostic performance and image quality. We focused on comparing the performance between generative adversarial networks (GANs) and non-GAN networks. There was a trade-off in such restoration quality between GAN- and non-GAN-based SRs, with the GANs being more accurate in restoring images of anatomical structures but less accurate in restoring those of lesions; non-GANs showed the opposite tendency. The non-GAN SRs were preferable in terms of diagnostic performance and image quality. This result suggested that the evaluation of DNN performance for lesions might be changed drastically by adding a clinical evaluation perspective. The dependence of network architecture on network performance obtained in this study will provide guidance for future development of SR DNN for medical images.

Modern approaches to the elimination of artifacts of cone-beam computed tomography of the maxillofacial region

The use of X-ray radiation is the main additional method for complete visualization of hard and soft tissues in dentistry and maxillofacial surgery. Cone-beam computed tomography (CBCT) is of leading importance among other methods. Three-dimensional reformation of CBCT data into a multiplanar or three-dimensional image of anatomical structures allows for a qualitative assessment of the area of interest and to choose the right treatment tactics if necessary. Nevertheless, there are a number of factors or artifacts that lead to distortion, disrupt the image or lead to loss of information during CBCT, in addition, the formation of overdiagnosis is not excluded. Currently, an active search is underway for ways to improve the quality of CBCT images and eliminate artifacts to improve the quality of diagnostics. This review is devoted to the description of the main causes of artifacts and modern methods of their elimination.

Hahn-PCNN-CNN: an end-to-end multi-modal brain medical image fusion framework useful for clinical diagnosis

BackgroundIn medical diagnosis of brain, the role of multi-modal medical image fusion is becoming more prominent. Among them, there is no lack of filtering layered fusion and newly emerging deep learning algorithms. The former has a fast fusion speed but the fusion image texture is blurred; the latter has a better fusion effect but requires higher machine computing capabilities. Therefore, how to find a balanced algorithm in terms of image quality, speed and computing power is still the focus of all scholars.MethodsWe built an end-to-end Hahn-PCNN-CNN. The network is composed of feature extraction module, feature fusion module and image reconstruction module. We selected 8000 multi-modal brain medical images downloaded from the Harvard Medical School website to train the feature extraction layer and image reconstruction layer to enhance the network’s ability to reconstruct brain medical images. In the feature fusion module, we use the moments of the feature map combined with the pulse-coupled neural network to reduce the information loss caused by convolution in the previous fusion module and save time.ResultsWe choose eight sets of registered multi-modal brain medical images in four diease to verify our model. The anatomical structure images are from MRI and the functional metabolism images are SPECT and 18F-FDG. At the same time, we also selected eight representative fusion models as comparative experiments. In terms of objective quality evaluation, we select six evaluation metrics in five categories to evaluate our model.ConclusionsThe fusion image obtained by our model can retain the effective information in source images to the greatest extent. In terms of image fusion evaluation metrics, our model is superior to other comparison algorithms. In terms of time computational efficiency, our model also performs well. In terms of robustness, our model is very stable and can be generalized to multi-modal image fusion of other organs.

A Novel Three-Dimensional Robot Arm Steered Camera for Ear Surgery.

Ear surgery requires magnified imaging of anatomical structures from its beginning to achieve safe and successful surgical outcomes. The historical evolution of magnification in otology has developed from monocular to binocular, and to three-dimensional and even to digital in modern times. Current technological advancements pursue high-quality visualization for the best surgical outcomes but also ergonomic for surgeons. Here, we evaluated the usability of such new technology in common otological surgeries like cochlear implantation and stapedectomy for the first time in patients. A three-dimensional camera mounted to a robot arm has hands-free control by goggles worn by the surgeon on a head mount. The camera at a distance of the patients but can also be draped in a sterile way that it forms a barrier tent between patient and surgical personnel in the theatre. The main reason to evaluate the feasibility of this new exoscope was driven by COVID-19 obligate measures for elective surgery such as hearing restoration. This new technology can be considered an important advantage for the surgeons working in microsurgery to perform their elective operations without aerosolization of the drill rinsing water possibly containing contaminated tissue. From a subjective point of view, the image quality is equivalent to conventional microscopes to provide safe otologic surgery.

Radial sequences and compressed sensing in pediatric body magnetic resonance imaging

Magnetic resonance imaging (MRI) is often an ideal imaging modality for children of any age for any anatomy and for many pathologies. MRI sequences can be prescribed to produce high-resolution images of anatomical structures, characterize tissue composition, and detect physiological states and organ function. Shortening imaging sequences in any manner possible has been a topic of research and development in MRI since its emergence. Selection of imaging sequence parameters influences more than just the appearance and signal qualities of the imaged tissues; these details along with spatial encoding and data readout steps determine the time it takes to acquire an image. As each piece of image data is acquired and encoded with spatial and temporal information it is stored in k-space. As k-space is filled, either completely or partially, a diagnostic image or physiological data can be reconstructed. Shortening the length of time required for the readout step by efficiently filling k-space using compressed sensing and radial techniques is the subject of this manuscript.

Three-dimensional construction of micrometer level in rat stomach by synchrotron radiation

BackgroundThe structural changes of gastric mucosa are considered as an important window of early gastric lesions. This article shows an imaging method of the stomach that does not use imaging agents. X-ray phase-contrast images of different stages of gastric development were taken using micrometer level X-ray in-line phase-contrast imaging (XILPCI) technique on synchrotron radiation facility. The aim of the study was to demonstrate that the imaging technique is an appropriate method for micron imaging of the gastric structures.MethodsThe stomachs of 4-, 6- and 12-week-old rats were removed and cleaned. XILPCI has 1000 times greater soft tissue contrast than that of X-ray traditional absorption radiography. The projection images of the rats stomachs were recorded by an XILPCI charge coupled device (CCD) at 9-μm image resolution.ResultsThe X-ray in-line phase-contrast images of the different stages of rats’ gastric specimens clearly showed the gastric architectures and the details of the gastro-duodenal region. 3-dimensional (3D) stomach anatomical structure images were reconstruction.ConclusionThe reconstructed gastric 3D images can clearly display the internal structure of the stomach. XILPCI may be a useful method for medical research in the future.

Augmented Reality-Assisted Pancreaticoduodenectomy with Superior Mesenteric Vein Resection and Reconstruction.

Introduction Pancreaticoduodenectomy (PD) with superior mesenteric vein (SMV) reconstruction are often required to achieve complete (R0) resection for pancreatic head cancer (PHC) with tumor invasion of the SMV. Augmented reality (AR) technology can be used to assist in determining the extent of SMV involvement by superimposing virtual 3-dimensional (3D) images of the pancreas and regional vasculature on the surgical field. Materials and Methods Three patients with PHC and tumor invasion of the SMV underwent AR-assisted PD with SMV resection and reconstruction following preoperative computed tomography scanning. Preoperative imaging data were used to reconstruct 3D images of anatomical structures, including the tumor, portal vein (PV), SMV, and splenic vein (SV). Using AR software installed on a smart phone, the reconstructed 3D images were superimposed on the surgical field as viewed in a smart phone display to provide intermittent navigational assistance to the surgeon in identifying the boundaries of PHC tumor invasion for resection of the vessels involved. Result All patients successfully completed the operation. Intraoperative AR applications displayed virtual images of the pancreas, SMV, bile duct, common hepatic artery (CHA), and superior mesenteric artery (SMA). Two patients required end-to-end anastomosis for reconstruction of the SMV. One patient required allogenic vascular bypass to reconstruct the SMV-PV juncture with concomitant reconstruction of the SV-SMV confluence by end-to-side anastomosis of the SV and bypass vessel. Postoperative pathology confirmed R0 resections for all patients. Conclusion AR navigation technology based on preoperative CT image data can assist surgeons performing PD with SMV resection and reconstruction.

Near-infrared fluorescent molecular probes for imaging and diagnosis of nephro-urological diseases.

Near-infrared (NIR) fluorescence imaging has improved imaging depth relative to conventional fluorescence imaging in the visible region, demonstrating great potential in both fundamental biomedical research and clinical practice. To improve the detection specificity, NIR fluorescence imaging probes have been under extensive development. This review summarizes the particular application of optical imaging probes with the NIR-I window (700–900 nm) or the NIR-II window (1000–1700 nm) emission for diagnosis of nephron-urological diseases. These molecular probes have enabled contrast-enhanced imaging of anatomical structures and physiological function as well as molecular imaging and early diagnosis of acute kidney injury, iatrogenic ureteral injury and bladder cancer. The design strategies of molecular probes are specifically elaborated along with representative imaging applications. The potential challenges and perspectives in this field are also discussed.

Higher Order Statistical Analysis for Thyroid Texture Classification and Segmentation in 2D ultrasound Images.

Ultrasound (US) imaging is one of the most cost-effective imaging modality that utilizes sound waves for generating medical images of anatomical structure. However, the presence of speckle noise and low contrast in the US images makes it difficult to use for proper classification of anatomical structures in clinical scenarios. Hence, it is important to devise a method that is robust and accurate even in the presence of speckle noise and is not affected by the low image contrast. In this work, a novel approach for thyroid texture characterization based on extracting features utilizing higher order spectral analysis (HOSA) was used. A Support Vector Machine (SVM) was applied on the extracted features to classify the thyroid texture. Since HOSA is a well suited technique for processing non-Gaussian data involving non-linear dynamics, good classification of thyroid texture can be obtained in US images as they also contain non-Gaussian Speckle noise and nonlinear characteristics. A final accuracy of 93.27%, sensitivity of 0.92 and specificity of 0.62 were obtained using the proposed approach.

On the feasibility of transperineal 3D ultrasound image guidance for robotic radical prostatectomy.

Prostate cancer is the most prevalent form of male-specific cancers. Robot-assisted laparoscopic radical prostatectomy (RALRP) using the da Vinci surgical robot has become the gold-standard treatment for organ-confined prostate cancer. To improve intraoperative visualization of anatomical structures, many groups have developed techniques integrating transrectal ultrasound (TRUS) into the surgical workflow. TRUS, however, is intrusive and does not provide real-time volumetric imaging. We propose a proof-of-concept system offering an alternative noninvasive transperineal view of the prostate and surrounding structures using 3D ultrasound (US), allowing for full-volume imaging in any anatomical plane desired. The system aims to automatically track da Vinci surgical instruments and display a real-time US image registered to preoperative MRI. We evaluate the approach using a custom prostate phantom, an iU22 (Philips Healthcare, Bothell, WA) US machine with an xMATRIX X6-1 transducer, and a custom probe fixture. A novel registration method between the da Vinci kinematic frame and 3D US is presented. To evaluate the entire registration pipeline, we use a previously developed MRI to US deformable registration algorithm. Our US calibration technique yielded a registration error of 0.84mm, compared to 1.76mm with existing methods. We evaluated overall system error with a prostate phantom, achieving a target registration error of 2.55mm. Transperineal imaging using 3D US is a promising approach for image guidance during RALRP. Preliminary results suggest this system is comparable to existing guidance systems using TRUS. With further development and testing, we believe our system has the potential to improve patient outcomes by imaging anatomical structures and prostate cancer in real time.