Soft Tissue Imaging Research Articles

X-ray microtomography imaging enables accurate visualization and quantification of the small-intestinal villi microvasculature - celiac disease as a model.

Vasculature of the small bowel mucosa, with a significant role in nutrient absorption and gut homeostasis, has been suggested to undergo remodeling in various gastrointestinal disorders, including celiac disease. However, due to its spatial organization within the mucosa, conventional 2D histological methods are of limited value in studying the intestinal vasculature reliably. X-ray microtomography (micro-CT) is a promising tool for soft tissue imaging, as it enables digital 3D reconstruction of various tissue samples, including endoscopically obtained small-bowel mucosal biopsies. In this proof-of-concept study, we utilize micro-CT imaging combined with iodine staining in revealing the 3D mucosal microvascular structures using celiac disease as a model. Furthermore, we present a unique image analysis workflow that enables the quantification of the microvascular network with explanatory parameters in untreated and treated celiac disease patients as well as in non-celiac disease controls. The calculation of these parameters has been unachievable previously using 2D image processing methods. The workflow produced results that showed noticeable differences in the microvascular structures from the point of diagnosis to after treatment. This unique method has potential to be used with various intestinal diseases and other applications where the mucosal vascular structures need to be visualized.

Advancements in Magnetic Resonance Imaging: Transforming Non-Invasive Diagnosis and Treatment Monitoring in Radiology

MRI is a crucial tool in radiology, enabling non-invasive diagnosis, disease staging, and treatment monitoring magnetic fields and radio waves. It produces detailed images by leveraging the magnetic properties of hydrogen atoms, allowing for precise anatomical insights without ionizing radiation. Techniques such as functional MRI (fMRI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI) enhance tumor evaluation and understanding, aiding in the identification of high-risk patients and personalized treatments. MRI’s capabilities extend to imaging soft tissues, particularly beneficial for neurological diagnoses and research. Advanced methods provide insights into brain activity, tissue microstructure, and disease mechanisms, especially in cerebral small vessel disease (cSVD). Recent innovations, such as ultrahigh field MRI (7T), improve image resolution and facilitate the assessment of vascular health. These advancements enhance diagnostic accuracy and treatment strategies, ultimately improving patient outcomes.

An MRI-guided stereotactic neurosurgical robotic system for semi-enclosed head coils.

Magnetic resonance imaging (MRI) offers high-quality soft tissue imaging without radiation exposure, which allows stereotactic techniques to significantly improve outcomes in cranial surgeries, particularly in deep brain stimulation (DBS) procedures. However, conventional stereotactic neurosurgeries often rely on mechanical stereotactic head frames and preoperative imaging, leading to suboptimal results due to the invisibility and the contact with patient's head, which may cause additional harm. This paper presents a frameless, MRI-guided stereotactic neurosurgical robotic system. The robot features a seven-degree-of-freedom (7-DOF) remote center of motion, with five DOFs for preoperative trajectory alignment to the target lesion and two DOFs for defining the depth and twisting motion of the needle during insertion, thus to minimize tissue damage. The system employs interactive MRI guidance for real-time visualization of the puncture process, showing great potential in reducing surgery time, enhancing targeting accuracy, and improving safety. Experiments were conducted on the proposed system to evaluate signal-to-noise ratio (SNR) and geometric distortion. During the simultaneous operation and imaging, the system demonstrated less than 10.02% SNR attenuation and less than 0.1% geometric distortion, ensuring image usability. The free-space positioning accuracy of the system was evaluated using a laser tracker, revealing a tip position repeatability error within 0.3 ± 0.1mm.

Utility of photon-counting detectors for MV-kV dual-energy computed tomography imaging.

High soft-tissue contrast imaging is essential for effective radiotherapy treatment. This could potentially be realized using both megavoltage and kilovoltage x-ray sources available on some therapy treatment systems to perform "MV-kV" dual-energy (DE) computed tomography (CT). However, noisy megavoltage images obtained with existing energy-integrating detectors (EIDs) are a limiting factor for clinical translation. We explore the potential for non-spectral photon-counting detectors (PCDs) to improve MV-kV image quality simply by equally weighting all MV photons rather than up-weighting the less informative, lower contrast high-energy photons as in an EID. Three computational methods were applied to compare non-spectral PCDs with EIDs in MV-kV DE imaging. A single-line integral estimation theory approach was used to calculate the basis material signal-to-noise ratio (SNR) of tissue (1 to 50cm) and bone (0.1 to 10cm). CT images of a tissue cylinder with seven bone inserts (densities 1.0 to ) were simulated to assess material decomposition accuracy. Multiple noisy simulations of an anthropomorphic phantom were performed to generate pixel-by-pixel noise profiles. PCDs yielded a 15% to 45% improvement in single-line integral SNR for both materials. In CT simulations, similar material decomposition accuracy was achieved, with both EIDs and PCDs slightly underestimating bone density. However, PCDs yield a higher contrast-to-noise ratio and more uniform noise texture than EIDs in virtual monoenergetic images. We demonstrate the potential for improved MV-kV DE CT imaging using non-spectral PCDs and quantify the degree of improvement in a range of object compositions. This work motivates the experimental assessment of PCDs for megavoltage imaging and the potential clinical translation of PCDs to radiotherapy imaging.

Updates on the MR safety guidelines - Essentials for radiologists.

Magnetic Resonance Imaging (MRI) is a sophisticated diagnostic tool that utilizes the magnetic properties of biological tissue to generate detailed images of internal structures without the use of ionizing radiation. Despite its benefits in providing high-contrast images of soft tissues, the strong magnetic fields used in MRI present a unique safety challenge. Increasing MRI-related accidents and the prevalence of patients with metallic implants in recent years underscore the critical need for stringent MR safety protocols. This article reviews the latest 2024 updates in the MRI safety manual by the American College of Radiology (ACR), highlighting the comprehensive efforts to manage risks associated with MRI, including projectile and burn incidents, patients with medical devices, and emerging complex MRI environments. The manual emphasizes the importance of specialized training for healthcare professionals to navigate the complexities of MRI safety to ensure patient and staff safety. This review also touches on the dynamic landscape of MRI safety standards, driven by technological advances and evolving clinical practices, aiming to provide a thorough understanding of current best practices in MRI safety management. LIST OF UPDATES.

Deep Depthwise Residual Network for Knee Meniscus Segmentation From Magnetic Resonance Imaging

ABSTRACTThe menisci within the knee are essential for various anatomical functions, including load‐bearing, joint stability, cartilage protection, shock absorption, and lubrication. Magnetic resonance imaging (MRI) provides highly detailed images of internal organs and soft tissues, which are indispensable for physicians and radiologists assessing the meniscus. Given the multitude of images in each MRI sequence and diverse MRI data, the segmentation of the meniscus presents considerable challenges through image processing methods. The region‐specific characteristics of the meniscus can vary from one image to another within the sequence. Consequently, achieving automatic and accurate segmentation of meniscus in knee MRI images is a crucial step in meniscus analysis. This paper introduces the “UNet with depthwise residual network” (DR‐UNet), a depthwise convolutional neural network, designed specifically for meniscus segmentation in MRI images. The proposed architecture significantly improves the accuracy of meniscus segmentation compared to different segmentation networks. The training and testing phases utilized fat suppression turbo‐spin‐echo (FS TSE) MRI sequences collected from 100 distinct knee joints using a Siemens 3 Tesla MRI machine. Additionally, we employed data augmentation techniques to expand the dataset strategically, addressing the challenge of a substantial training dataset requirement. The DR‐UNet model demonstrated impressive meniscus segmentation performance, achieving a Dice similarity coefficient range of 0.743–0.9646 and a Jaccard index range of 0.653–0.869, thereby showcasing its advanced segmentation capabilities.

Phase-contrast visualization of human tissues using superimposed wavefront imaging of diffraction-enhanced x-rays.

Phase-contrast computed tomography (CT) using high-brilliance, synchrotron-generated x-rays enable three-dimensional (3D) visualization of microanatomical structures within biological specimens, offering exceptionally high-contrast images of soft tissues. Traditional methods for phase-contrast CT; however, necessitate a gap between the subject and the x-ray camera, compromising spatial resolution due to penumbral blurring. Our newly developed technique, Superimposed Wavefront Imaging of Diffraction-enhanced x-rays (SWIDeX), leverages a Laue-case Si angle analyzer affixed to a scintillator to convert x-rays to visible light, capturing second-order differential phase contrast images and effectively eliminating the distance to the x-ray camera. This innovation achieves superior spatial resolution over conventional methods. In this paper, the imaging principle and CT reconstruction algorithm based on SWIDeX are presented in detail and compared with conventional analyzer-based imaging (ABI). It also shows the physical setup of SWIDeX that provides the resolution preserving second-order differential images for reconstruction. We compare the spatial resolution and the sensitivity of SWIDeX to conventional ABI. To demonstrate high-spatial resolution achievable by SWIDeX, the internal structures of four human tissues-ductal carcinoma in situ, normal stomach, normal pancreas, and intraductal papillary mucinous neoplasm of the pancreas-were visualized using an imaging system configured at the Photon Factory's BL14B beamline under the High Energy Accelerator Research Organization (KEK). Each tissue was thinly sliced after imaging, stained with hematoxylin and eosin (H&E) for conventional microscope-based pathology. A comparison of SWIDeX-CT and pathological images visually demonstrates the effectiveness of SWIDeX-CT for biological tissue imaging. SWIDeX could generate clearer 3D images than existing analyzer-based phase-contrast methods and accurately delineate tissue structures, as validated against histopathological images. SWIDeX can visualize important 3D structures in biological soft tissue with high spatial resolution and can be an important tool for providing information between the disparate scales of clinical and pathological imaging.

Advancing Diagnostics: The Role of Ultrasound and MR Imaging in Evaluating Musculotendinous Pathologies of the Shoulder Joint – A Comprehensive Literature Review

Musculotendinous pathologies of the shoulder joint are a common source of pain and functional impairment, often requiring precise imaging for accurate diagnosis and management. This review explores the roles of ultrasound (US) and magnetic resonance imaging (MRI) in evaluating these conditions, focusing on their diagnostic strengths, limitations, and clinical applications. Ultrasound offers dynamic, real-time assessment with high accuracy for superficial structures, making it a cost-effective option for evaluating rotator cuff tears and tendinopathies. MRI, on the other hand, is the gold standard for comprehensive imaging of deep soft tissues, labral injuries, and subtle intra-articular abnormalities. Recent advances in both modalities, including AI-powered ultrasound and faster MRI sequences, have enhanced diagnostic capabilities. This review synthesizes evidence from the past decade, comparing the sensitivity, specificity, and cost-effectiveness of US and MRI while discussing scenarios where their integration can provide the most benefit. The findings emphasize the need for modality selection tailored to clinical presentations, the expertise of practitioners, and healthcare resource availability. Future research should aim to standardize imaging protocols and explore innovative multimodal approaches to improve diagnostic accuracy further.

CT radiation dose reduction with tin filter for localisation/characterisation level image quality in PET-CT: a phantom study

BackgroundThe tin filter has allowed radiation dose reduction in some standalone diagnostic computed tomography (CT) applications. Yet, ‘low-dose’ CT scans are commonly used in positron emission tomography (PET)-CT for lesion localisation/characterisation (L/C), with higher noise tolerated. Thus, dose reductions permissible with the tin filter at this image quality level may differ. The aim was to determine the level of CT dose reduction permitted with the tin filter in PET-CT, for comparable image quality to the clinical reference standard (CRS) L/C CT images acquired with standard filtration.Materials and methodsA whole-body CT phantom was scanned with standard filtration in CRS protocols, using 120 kV with 20mAs-ref for bone L/C (used in 18F-Sodium Fluoride (NaF) PET-CT) and 40mAs-ref for soft tissue L/C (used in 18F-Fluorodeoxyglucose (FDG) PET-CT), followed by tin filter scans at 100 kV (Sn100kV) and 140 kV (Sn140kV) with a range of mAs settings. For each scan, effective dose (ED) in an equivalent-sized patient was calculated, and image quality determined in 5 different tissues through quantitative (contrast-to-noise ratio) and qualitative (visual) analyses. The relative dose reductions which could be achieved with the tin filter for comparable image quality to CRS images were calculated.ResultsQuantitative analysis demonstrated dose savings of 50–76% in bone, 27–51% in lung and 8–61% in soft tissue with use of the tin filter at Sn100kV. Qualitative analysis demonstrated dose reductions using Sn100kV in general agreement with the dose reductions indicated by quantitative analysis. Overall, CT dose reductions of around 85% were indicated for NaF bone PET-CT, allowing whole-body CT at just 0.2mSv ED, and a 30–40% CT dose reduction for FDG PET-CT using Sn100kV (1.7-2.0mSv), providing comparable image quality to current CRS images with standard filtration. Sn140kV demonstrated limited value in CT dose reduction.ConclusionsLarge CT dose reductions can be made using the tin filter at Sn100kV, when imaging bone, lung and soft tissue at L/C level CT image quality in PET-CT. As well as reducing the risk of inducing a cancer in later life, such dose reductions may also impact PET-CT practice, such as justifying cross-sectional over planar imaging or justifying PET-CT in younger patients.

Minimally interactive segmentation of soft-tissue tumors on CT and MRI using deep learning.

Segmentations are crucial in medical imaging for morphological, volumetric, and radiomics biomarkers. Manual segmentation is accurate but not feasible in clinical workflow, while automatic segmentation generally performs sub-par. To develop a minimally interactive deep learning-based segmentation method for soft-tissue tumors (STTs) on CT and MRI. The interactive method requires the user to click six points near the tumor's extreme boundaries in the image. These six points are transformed into a distance map and serve, with the image, as input for a convolutional neural network. A multi-center public dataset with 514 patients and nine STT phenotypes in seven anatomical locations, with CT or T1-weighted MRI, was used for training and internal validation. For external validation, another public dataset was employed, which included five unseen STT phenotypes in extremities on CT, T1-weighted MRI, and T2-weighted fat-saturated (FS) MRI. Internal validation resulted in a dice similarity coefficient (DSC) of 0.85 ± 0.11 (mean ± standard deviation) for CT and 0.84 ± 0.12 for T1-weighted MRI. External validation resulted in DSCs of 0.81 ± 0.08 for CT, 0.84 ± 0.09 for T1-weighted MRI, and 0.88 ± 0.08 for T2-weighted FS MRI. Volumetric measurements showed consistent replication with low error internally (volume: 1 ± 28 mm3, r = 0.99; diameter: - 6 ± 14 mm, r = 0.90) and externally (volume: - 7 ± 23 mm3, r = 0.96; diameter: - 3 ± 6 mm, r = 0.99). Interactive segmentation time was considerably shorter (CT: 364 s, T1-weighted MRI: 258s) than manual segmentation (CT: 1639s, T1-weighted MRI: 1895s). The minimally interactive segmentation method effectively segments STT phenotypes on CT and MRI, with robust generalization to unseen phenotypes and imaging modalities. Question Can this deep learning-based method segment soft-tissue tumors faster than can be done manually and more accurately than other automatic methods? Findings The minimally interactive segmentation method achieved accurate segmentation results in internal and external validation, and generalized well across soft-tissue tumor phenotypes and imaging modalities. Clinical relevance This minimally interactive deep learning-based segmentation method could reduce the burden of manual segmentation, facilitate the integration of imaging-based biomarkers (e.g., radiomics) into clinical practice, and provide a fast, semi-automatic solution for volume and diameter measurements (e.g., RECIST).

Efficient labeling for fine-tuning chest X-ray bone-suppression networks for pediatric patients.

Pneumonia, a major infectious cause of morbidity and mortality among children worldwide, is typically diagnosed using low-dose pediatric chest X-ray [CXR (chest radiography)]. In pediatric CXR images, bone occlusion leads to a risk of missed diagnosis. Deep learning-based bone-suppression networks relying on training data have enabled considerable progress to be achieved in bone suppression in adult CXR images; however, these networks have poor generalizability to pediatric CXR images because of the lack of labeled pediatric CXR images (i.e., bone images vs. soft-tissue images). Dual-energy subtraction imaging approaches are capable of producing labeled adult CXR images; however, their application is limited because they require specialized equipment, and they are infrequently employed in pediatric settings. Traditional image processing-based models can be used to label pediatric CXR images, but they are semiautomatic and have suboptimal performance. We developed an efficient labeling approach for fine-tuning pediatric CXR bone-suppression networks capable of automatically suppressing bone structures in CXR images for pediatric patients without the need for specialized equipment and technologist training. Three steps were employed to label pediatric CXR images and fine-tune pediatric bone-suppression networks: distance transform-based bone-edge detection, traditional image processing-based bone suppression, and fully automated pediatric bone suppression. In distance transform-based bone-edge detection, bone edges were automatically detected by predicting bone-edge distance-transform images, which were then used as inputs in traditional image processing. In this processing, pediatric CXR images were labeled by obtaining bone images through a series of traditional image processing techniques. Finally, the pediatric bone-suppression network was fine-tuned using the labeled pediatric CXR images. This network was initially pretrained on a public adult dataset comprising 240 adult CXR images (A240) and then fine-tuned and validated on 40 pediatric CXR images (P260_40labeled) from our customized dataset (named P260) through five-fold cross-validation; finally, the network was tested on 220 pediatric CXR images (P260_220unlabeled dataset). The distance transform-based bone-edge detection network achieved a mean boundary distance of 1.029. Moreover, the traditional image processing-based bone-suppression model obtained bone images exhibiting a relative Weber contrast of 93.0%. Finally, the fully automated pediatric bone-suppression network achieved a relative mean absolute error of 3.38%, a peak signal-to-noise ratio of 35.5dB, a structural similarity index measure of 98.1%, and a bone-suppression ratio of 90.1% on P260_40labeled. The proposed fully automated pediatric bone-suppression network, together with the proposed distance transform-based bone-edge detection network, can automatically acquire bone and soft-tissue images solely from CXR images for pediatric patients and has the potential to help diagnose pneumonia in children.

Optimal transport assisted full waveform inversion for multiparameter imaging of soft tissues in ultrasound computed tomography

Ultrasound computed tomography (USCT) has emerged as a promising platform for imaging tissue properties, offering non-ionizing and operator-independent capabilities. In this work, we demonstrate the feasibility of obtaining quantitative images of multiple acoustic parameters (sound speed and impedance) for soft tissues using full waveform inversion (FWI), which are justified with both numerical and experimental cases. A 3D reconstruction based on a series of 2D slice images is presented for the experimental case of ex vivo soft tissues. To improve the robustness of the reconstruction process, a hierarchical FWI strategy is adopted, gradually iterating from low to high frequencies. In parallel, we employ a graph-space optimal transport misfit function, avoiding convergence into local minima and minimizing inversion artifacts caused by skin-related supercritical reflections. Our method first carries out sound speed inversion based on transmitted waves in the low and middle frequency bands, and then uses all types of waves in the high frequency band for simultaneous inversion of both sound speed and impedance. Compared to conventional strategies, the proposed approach can accurately reconstruct physical models consistent with the actual soft tissue sample. These high-resolution ultrasound images of acoustic parameters are promising to allow for quantitative differentiation among different types of tissues (e.g., muscles and fats). These results have significant implications for advancing our understanding of tissue properties and for potentially contributing to disease diagnosis through USCT, which is a flexible and cost-effective alternative to X-ray computed tomography or magnetic resonance imaging at no significant sacrifices for resolution.

Neck lumps: Early Detection with Dental Magnetic Resonance Imaging?

Cervical lymphadenopathy can indicate a range of diseases, including infections, inflammation, and neoplastic processes. Accurate diagnosis is crucial for identifying the underlying cause and determining appropriate treatment. Diagnostic procedures include clinical examination, ultrasound, fine needle aspiration, biopsy, and imaging techniques. While conventional imaging methods such as X-ray and computed tomography (CT) have proven useful, they exhibit limitations in sensitivity and specificity, especially for soft tissue structures. Magnetic resonance imaging (MRI) provides superior soft tissue contrast, offering additional critical information for differential diagnosis. This article presents a case study of a 29-year-old male with a cervical mass. Advanced dental MRI protocols, including DESS, SPACE-SPAIR, and SPACE-STIR sequences, have significantly enhanced the imaging capabilities of the den-tomaxillofacial region, enabling the generation of high-resolution images of both hard and soft tissues. Especially, MR OPGs have demonstrated the capacity to identify early signs of potential pathology. These protocols have the potential to enhance early detection, thereby con-tributing to accurate diagnosis and effective treatment planning.

Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review

ObjectiveTo conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection.DesignWe searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided.ResultsSeveral studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT, and the 3D microanatomy characterisation of human tuberculosis lung using μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented.ConclusionThe literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo.

036. Sclerotic Therapy as The Management Of Arteriovenous Malformation on Hand: A Case Report

Background: An arteriovenous malformation (AVM) is an abnormal connection between arteries and veins, bypassing the capillary system. There are a number of consequences associated with this illness, such as bleeding and neurological impairments. The situation is complex, which emphasizes the necessity for individualized treatment plans. This case report a 18 years old female with AVM condition. Case: A 18 years old female was admitted to to Prof. IGNG Ngoerah General Hospital with a chief complaint of lump on the left finger. The lump on the left finger has been felt since >10 years ago. The lump appeared suddenly and went bigger each day. Complaints worsened until the patient felt pain in the left finger. On examination of the local status, multiple masses were found on third and fourth digits of the left hand, with unclear border and vein ectasis. MSCT Angiography of the left superior extremity found a soft tissue image of hemangioma on the palmar aspect and interdigiti III-IV to the palmar-dorsal aspect of digit III and the palmar aspect of digiti IV of the left hand and multiple non-suspicious lymphadenopathy in the left axilla region. The scleroterapy treatment was performed and 3 months results shown decreased size of the masses without any complication. Conclusion: An arteriovenous malformation (AVM) of the left hand fingers is a rare case found in Prof Ngoerah Hospital. The sclerotic therapy found to be an effective management for this case.

Enhancing spinal MRI segmentation with an asymmetric U-Net architecture

Spinal diseases are among the most prevalent health issues in modern society, significantly impacting patients’ quality of life. Diagnosing conditions such as disc herniation and spinal deformity requires advanced medical imaging techniques, including X-rays, magnetic resonance imaging (MRI), computed tomography, and nuclear magnetic resonance. Spine MRI is particularly crucial due to its ability to provide high-resolution images of soft tissues, essential for accurate diagnosis. However, the manual segmentation of spine MRI images is labor-intensive and inadequate for large-scale quantitative analysis. Thus, developing automated spinal MRI segmentation methods is critical to alleviating doctors’ workload and enhancing diagnostic efficiency. In this study, we propose a novel asymmetric U-Net architecture designed to improve the precision of reconstructing complex structures and details by increasing the depth of the upsampling side. The model incorporates adjacent-scale skip connections to control parameters while maintaining high segmentation accuracy. In addition, residual connections on the upsampling side prevent gradient vanishing, thereby enhancing the network’s feature learning and representation capabilities. Experimental results indicate that this method significantly reduces training time and increases model accuracy compared to traditional approaches, marking a substantial advancement in automated spinal MRI segmentation. This innovative approach holds promise for improving clinical outcomes and optimizing the workflow in medical imaging departments.

Medical imaging for the diagnosis, recurrence and metastasis evaluation of clear cell sarcoma

Clear cell sarcoma (CCS) of soft tissue is extremely rare, accounting for approximately 1% of all soft tissue tumours. It is very difficult to diagnose CCS based on clinical manifestations. Magnetic resonance imaging (MRI) provides high-resolution images of soft tissues and pathological features such as mucus, necrosis, bleeding, and fat through high and low signals on T1 weighted image (T1WI) and T2 weighted image (T2WI). On the other hand, the paramagnetism of melanin in CCS shortens the relaxation time of T1 and T2, and high signal intensity on T1WI and low signal intensity on T2WI can be found. This is different from most other soft tissue sarcomas. At present, the treatment method for CCS is surgical resection. MRI can effectively display the tumour edge, extent of surrounding oedema, and extent of fat involvement, which is highly important for guiding surgical resection and predicting postoperative recurrence. As an invasive sarcoma, CCS has a high risk of metastasis. Regardless of the pathological condition of the resected tumour, MRI or computed tomography (CT) should be performed every 1-2 years to assess recurrence at the primary site and to screen for metastasis in the lungs, liver, and bones. If necessary, PET-CT can be performed to evaluate the overall condition of the patient.

Synchrotron X-ray imaging of soft biological tissues - principles, applications and future prospects.

Synchrotron-based tomographic phase-contrast X-ray imaging (SRµCT or SRnCT) is a versatile isotropic three-dimensional imaging technique that can be used to study biological samples spanning from single cells to human-sized specimens. SRµCT and SRnCT take advantage of the highly brilliant and coherent X-rays produced by a synchrotron light source. This enables fast data acquisition and enhanced image contrast for soft biological samples owing to the exploitation of phase contrast. In this Review, we provide an overview of the basics behind the technique, discuss its applications for biologists and provide an outlook on the future of this emerging technique for biology. We introduce the latest advances in the field, such as whole human organs imaged with micron resolution, using X-rays as a tool for virtual histology and resolving neuronal connections in the brain.

Comparative Evaluation of Laser System to Conventional Surgical Approaches in Periodontal Healing Using Optical Coherence Tomography

Background: Optical coherence tomography (OCT) is an emerging, radiation-free diagnostic tool in dentistry, providing high-resolution, real-time imaging of both hard and soft tissues, including periodontal areas, for more accurate postoperative evaluations. This study aims to investigate the efficacy of OCT on periodontal tissues in animals by comparing the healing effects of laser therapy with those of conventional surgical instruments. Methods: Six rabbits underwent periodontal surgery using a laser, scalpel, and punch to perform an apically positioned flap on the mandibular anterior incisors and to create a tongue ulcer on the dorsal surface of the tongue. Visual and OCT evaluations were conducted on days 1, 2, 3, 7, and 14. Results: In periodontal surgery, the laser exhibited slightly faster healing compared to other methods. In tongue ulcer formation, the scalpel and punch groups demonstrated slightly faster healing than that of the laser. However, both methods ultimately showed similar healing outcomes. Conclusions: In the dental field, OCT is emerging as a valuable tool for assessing healing, including early stages of healing, in periodontal therapy.

Feasibility study of YSO/SiPM based detectors for virtual monochromatic image synthesis.

The development of photon-counting CT systems has focused on semiconductor detectors like cadmium zinc telluride (CZT) and cadmium telluride (CdTe). However, these detectors face high costs and charge-sharing issues, distorting the energy spectrum. Indirect detection using Yttrium Orthosilicate (YSO) scintillators with silicon photomultiplier (SiPM) offers a cost-effective alternative with high detection efficiency, low dark count rate, and high sensor gain. This work aims to demonstrate the feasibility of the YSO/SiPM detector (DexScanner L103) based on the Multi-Voltage Threshold (MVT) sampling method as a photon-counting CT detector by evaluating the synthesis error of virtual monochromatic images. In this study, we developed a proof-of-concept benchtop photon-counting CT system, and employed a direct method for empirical virtual monochromatic image synthesis (EVMIS) by polynomial fitting under the principle of least square deviation without X-ray spectral information. The accuracy of the empirical energy calibration techniques was evaluated by comparing the reconstructed and actual attenuation coefficients of calibration and test materials using mean relative error (MRE) and mean square error (MSE). In dual-material imaging experiments, the overall average synthesis error for three monoenergetic images of distinct materials is 2.53% ±2.43%. Similarly, in K-edge imaging experiments encompassing four materials, the overall average synthesis error for three monoenergetic images is 4.04% ±2.63%. In rat biological soft-tissue imaging experiments, we further predicted the densities of various rat tissues as follows: bone density is 1.41±0.07 g/cm3, adipose tissue density is 0.91±0.06 g/cm3, heart tissue density is 1.09±0.04 g/cm3, and lung tissue density is 0.32±0.07 g/cm3. Those results showed that the reconstructed virtual monochromatic images had good conformance for each material. This study indicates the SiPM-based photon-counting detector could be used for monochromatic image synthesis and is a promising method for developing spectral computed tomography systems.