Advances In Medical Imaging Technology Research Articles

Small Blob Detector Using Bi-Threshold Constrained Adaptive Scales.

Recent advances in medical imaging technology bring great promises for medicine practices. Imaging biomarkers are discovered to inform disease diagnosis, prognosis, and treatment assessment. Detecting and segmenting objects from images are often the first steps in quantitative measurement of these biomarkers. The challenges of detecting objects in images, particularly small objects known as blobs, include low image resolution, image noise and overlap among the blobs. This research proposes a Bi-Threshold Constrained Adaptive Scale (BTCAS) blob detector to uncover the relationship between the U-Net threshold and the Difference of Gaussian (DoG) scale to derive a multi-threshold, multi-scale small blob detector. With lower and upper bounds on the probability thresholds from U-Net, two binarized maps of the distance are rendered between blob centers. Each blob is transformed to a DoG space with an adaptively identified local optimum scale. A Hessian convexity map is rendered using the adaptive scale, and the under-segmentation typical of the U-Net is resolved. To validate the performance of the proposed BTCAS, a 3D simulated dataset (n = 20) of blobs, a 3D MRI dataset of human kidneys and a 3D MRI dataset of mouse kidneys, are studied. BTCAS is compared against four state-of-the-art methods: HDoG, U-Net with standard thresholding, U-Net with optimal thresholding, and UH-DoG using precision, recall, F-score, Dice and IoU. We conclude that BTCAS statistically outperforms the compared detectors.

1251P Exploring quantitative biomarkers from different tumour volumes for radiomics in lung cancer

Recent advances in medical imaging technology allow the use of advanced image analysis methods by the extraction of quantitative features from medical images to characterize tumor biology in order to provide informations that may be useful to guide therapies and predict survival. In our previous experience, a radiomics signature mixing semantic and image-based features was able to predict the reduction of the target volume during chemoradiation in LA-NSCLC. The aim of this study is to investigate which 3D ROI contain more quantitative information that can be exploit for a radiomic approach in lung cancer.

Application Of Advanced Medical Imaging Technology In Bangladesh

Introduction: Like other part of the world Bangladesh healthcare industry is currently undergoing major changes in an effort to control costs & increase efficiency without compromising patient care. In the past, this care was delivered through only few healthcare specialists but as the number of specialists are increased the scenario is changing. For last 5 years Bangladesh underwent tremendous advancement in the field of Radiology & Imaging in terms of introduction of cutting edge technologies along with parallel up gradation of the Technologists demands assessment for optimum utilization of these facilities.

Eye Movements in Medical Image Perception: A Selective Review of Past, Present and Future.

The eye movements of experts, reading medical images, have been studied for many years. Unlike topics such as face perception, medical image perception research needs to cope with substantial, qualitative changes in the stimuli under study due to dramatic advances in medical imaging technology. For example, little is known about how radiologists search through 3D volumes of image data because they simply did not exist when earlier eye tracking studies were performed. Moreover, improvements in the affordability and portability of modern eye trackers make other, new studies practical. Here, we review some uses of eye movements in the study of medical image perception with an emphasis on newer work. We ask how basic research on scene perception relates to studies of medical ‘scenes’ and we discuss how tracking experts’ eyes may provide useful insights for medical education and screening efficiency.

Automatic Exposure Control in Chest Radiography

Recent advances in medical imaging technologies have led to the increased use of automatic exposure control (AEC) in digital X-ray equipment. This study is an attempt to measure the dose of exposure based on AEC use and to evaluate the image quality in chest radiography where AEC is used most frequently. We measured the dose of radiation using three (3) units of digital radiation generators for diagnosis in conditions that are frequently used in chest radiography (110kVp, 115kVp, 120kVp, 125kVp). We placed the dosimeter in front of a chest phantom while varying the distance of FID to 110 cm, 140 cm, and 180 cm depending on whether or not AEC was used. The CNR and SNR of the images obtained by setting the lung to signal, 5 regions of Interest (ROIs), and 5 places of media sternum to noise, were measured using the Image J Program. To determine the dose difference based on AEC use, we conducted a t-test, which revealed a significant difference. When AEC was used at FID 110 cm, a high dose of 11.98% appeared. When AEC was used at FID 140cm, a high dose of 8.64% appeared. When AEC was used at FID 180 cm, a high dose of 6.74% appeared. In the t-test for the difference in CNR and SNR based on AEC use, no significant difference was detected. As the distance of FID increased, high values of CNR and SNR were measured.The results of this study show that there is no difference between CNR and SNR regardless of the use of AEC, and the dose was high when using AEC. Therefore, chest X - ray examination should not be based on AEC only. Under appropriate radiological settings, it is possible to reduce unnecessary radiation exposure to the patient.

Automatic Exposure Control in Chest Radiography

Recent advances in medical imaging technologies have led to the increased use of automatic exposure control (AEC) in digital X-ray equipment. This study is an attempt to measure the dose of exposure based on AEC use and to evaluate the image quality in chest radiography where AEC is used most frequently. We measured the dose of radiation using three (3) units of digital radiation generators for diagnosis in conditions that are frequently used in chest radiography (110kVp, 115kVp, 120kVp, 125kVp). We placed the dosimeter in front of a chest phantom while varying the distance of FID to 110 cm, 140 cm, and 180 cm depending on whether or not AEC was used. The CNR and SNR of the images obtained by setting the lung to signal, 5 regions of Interest (ROIs), and 5 places of media sternum to noise, were measured using the Image J Program. To determine the dose difference based on AEC use, we conducted a t-test, which revealed a significant difference. When AEC was used at FID 110 cm, a high dose of 11.98% appeared. When AEC was used at FID 140cm, a high dose of 8.64% appeared. When AEC was used at FID 180 cm, a high dose of 6.74% appeared. In the t-test for the difference in CNR and SNR based on AEC use, no significant difference was detected. As the distance of FID increased, high values of CNR and SNR were measured. The results of this study show that there is no difference between CNR and SNR regardless of the use of AEC, and the dose was high when using AEC. Therefore, chest X - ray examination should not be based on AEC only. Under appropriate radiological settings, it is possible to reduce unnecessary radiation exposure to the patient.

Diffusion of advanced medical imaging technology, CT, and MRI scanners, in Iran: A qualitative study of determinants.

Low-income and middle-income countries have a lot of problems in providing the financial, human, and capital resources for advanced medical imaging technologies (AMITs). Proper distribution of such expensive equipment might increase access to these services and enhance the quality and efficiency of health care. The present study aimed to identify the factors affecting the diffusion of AMITs including CT and MRI in Iran. This is a qualitative research conducted in 2017. A number of purposively chosen policy makers and managers from Ministry of Health, medical universities, hospitals, health insurance, and vendor companies were approached for face-to-face and semistructured interviews. Data analysis was done using thematic analysis method. Three categories of influential factors were identified, including contextual, stakeholder related, and situational factors. With regards to the stakeholders, the ministry and physicians played the most important role in the diffusion process of the AMITs. Health insurance organizations and vendor companies played a minor role in distribution of the equipment. Application of political pressure by the authorities on the Ministry of Health to issue licenses for the acquisition of AMITs was taken as the most important factor and the greatest challenge in the diffusion of devices. The results sought to assist health system authorities to manage diffusion of AMITs effectively and assign an equitable access to the expensive resources. The diffusion management together with utilization management could largely improve efficiency and cost containment. Medical technology diffusion management de facto needs policy instruments in both supply and demand sides.

Optimization of Phase-Change Contrast Agents for Targeting MDA-MB-231 Breast Cancer Cells

Breast cancer remains a leading cause of death for women throughout the world. Recent advances in medical imaging technologies and tumor targeting agents signify vast potential for progress toward improved management of this global problem. Phase-change contrast agents (PCCAs) are dynamic imaging agents with practical applications in both the research and clinical settings. PCCAs possess characteristics that allow for cellular uptake where they can be converted from liquid-phase PCCAs to gaseous microbubbles via ultrasound energy. Previously, we reported successful internalization of folate-targeted PCCAs in MDA-MB-231 breast cancer cells followed by ultrasound-mediated activation to produce internalized microbubbles. This study examines the binding, internalization and activation of folate-receptor targeted PCCAs in MDA-MB-231 breast cancer cells as a function of gaseous core compositions, incubation time and ultrasound exposure period. In vitro results indicate that internalization and ultrasound-mediated activation of PCCAs were significantly greater using a 50:50 mixture of decafluorobutane:dodecafluoropentane compared with other core compositions: 50:50 octafluoropropane:decafluorobutane (p < 0.0001), decafluorobutane (p < 0.04) and dodecafluoropentane (p < 0.0001). Furthermore, it was found that PCCAs composed of perfluorocarbons with higher boiling points responded with greater activation efficiency when exposed to 12 s of ultrasound exposure as opposed to 4 s of ultrasound exposure. When evaluating different incubation times, it was found that incubating the PCCAs with breast cancer cells for 60 min did not produce significantly greater internalization and activation compared with incubation for 10 min; this was concluded after comparing the number of microbubbles present per cell before ultrasound versus post-ultrasound, and finding a ratio of intracellular microbubbles post-ultrasound/pre-ultrasound, 3.46 versus 3.14, respectively. The data collected in this study helps illustrate further optimization of folate-receptor targeted PCCAs for breast cancer targeting and imaging.

A Hybrid Approach of Using Particle Swarm Optimization and Volumetric Active Contour without Edge for Segmenting Brain Tumors in MRI Scan

Segmentation of brain tumors in magnetic resonance imaging is a one of the most complex processes in medical image analysis because it requires a combination of data knowledge with domain knowledge to achieve highly results. Such that, the data knowledge refers to homogeneity, continuity, and anatomical texture. While the domain knowledge refers to shapes, location, and size of the tumor to be delineated. Due to recent advances in medical imaging technologies which produce a massive number of cross-sectional slices, this makes a manual segmentation process is a very intensive, time-consuming and prone to inconsistences. In this study, an automated method for recognizing and segmenting the pathological area in MRI scans has been developed. First the dataset has been pre-processed and prepared by implementing a set of algorithms to standardize all collected samples. A particle swarm optimization is utilized to find the core of pathological area within each MRI slice. Finally, an active contour without edge method is utilized to extract the pathological area in MRI scan. Results reported on the collected dataset includes 50 MRI scans of pathological patients that was provided by Iraqi Center for Research and Magnetic Resonance of Al Imamain Al-Kadhimain Medical City in Iraq. The achieved accuracy of the proposed method was 92% compared with manual delineation.

A Hybrid Approach of Using Particle Swarm Optimization and Volumetric Active Contour without Edge for Segmenting Brain Tumors in MRI Scan

Segmentation of brain tumors in magnetic resonance imaging is a one of the most complex processes in medical image analysis because it requires a combination of data knowledge with domain knowledge to achieve highly results. Such that, the data knowledge refers to homogeneity, continuity, and anatomical texture. While the domain knowledge refers to shapes, location, and size of the tumor to be delineated. Due to recent advances in medical imaging technologies which produce a massive number of cross-sectional slices, this makes a manual segmentation process is a very intensive, time-consuming and prone to inconsistences. In this study, an automated method for recognizing and segmenting the pathological area in MRI scans has been developed. First the dataset has been pre-processed and prepared by implementing a set of algorithms to standardize all collected samples. A particle swarm optimization is utilized to find the core of pathological area within each MRI slice. Finally, an active contour without edge method is utilized to extract the pathological area in MRI scan. Results reported on the collected dataset includes 50 MRI scans of pathological patients that was provided by Iraqi Center for Research and Magnetic Resonance of Al Imamain Al-Kadhimain Medical City in Iraq. The achieved accuracy of the proposed method was 92% compared with manual delineation.

Discovering anomalous patterns in large digital pathology images.

Advances in medical imaging technology have created opportunities for computer-aided diagnostic tools to assist human practitioners in identifying relevant patterns in massive, multiscale digital pathology slides. This work presents Hierarchical Linear Time Subset Scanning, a novel statistical method for pattern detection. Hierarchical Linear Time Subset Scanning exploits the hierarchical structure inherent in data produced through virtual microscopy in order to accurately and quickly identify regions of interest for pathologists to review. We take a digital image at various resolution levels, identify the most anomalous regions at a coarse level, and continue to analyze the data at increasingly granular resolutions until we accurately identify its most anomalous subregions. We demonstrate the performance of our novel method in identifying cancerous locations on digital slides of prostate biopsy samples and show that our methods detect regions of cancer in minutes with high accuracy, both as measured by the ROC curve (measuring ability to distinguish between benign and cancerous slides) and by the spatial precision-recall curve (measuring ability to pick out the malignant areas on a slide which contains cancer). Existing methods need small scale images (small areas of a slide preselected by the pathologist for analysis, eg, 32 × 32 pixels) and may not work effectively on large, raw digitized images of size 100K × 100K pixels. In this work, we provide a methodology to fill this significant gap by analyzing large digitized images and identifying regions of interest that may be indicative of cancer.

Imaging and the completion of the omics paradigm in breast cancer.

Within the field of oncology, “omics” strategies—genomics, transcriptomics, proteomics, metabolomics—have many potential applications and may significantly improve our understanding of the underlying processes of cancer development and progression. Omics strategies aim to develop meaningful imaging biomarkers for breast cancer (BC) by rapid assessment of large datasets with different biological information. In BC the paradigm of omics technologies has always favored the integration of multiple layers of omics data to achieve a complete portrait of BC. Advances in medical imaging technologies, image analysis, and the development of high-throughput methods that can extract and correlate multiple imaging parameters with “omics” data have ushered in a new direction in medical research. Radiogenomics is a novel omics strategy that aims to correlate imaging characteristics (i. e., the imaging phenotype) with underlying gene expression patterns, gene mutations, and other genome-related characteristics. Radiogenomics not only represents the evolution in the radiology–pathology correlation from the anatomical–histological level to the molecular level, but it is also a pivotal step in the omics paradigm in BC in order to fully characterize BC. Armed with modern analytical software tools, radiogenomics leads to new discoveries of quantitative and qualitative imaging biomarkers that offer hitherto unprecedented insights into the complex tumor biology and facilitate a deeper understanding of cancer development and progression. The field of radiogenomics in breast cancer is rapidly evolving, and results from previous studies are encouraging. It can be expected that radiogenomics will play an important role in the future and has the potential to revolutionize the diagnosis, treatment, and prognosis of BC patients. This article aims to give an overview of breast radiogenomics, its current role, future applications, and challenges.

Fluorescence Molecular Imaging and Tomography of Matrix Metalloproteinase-Activatable Near-Infrared Fluorescence Probe and Image-Guided Orthotopic Glioma Resection.

Malignant gliomas are major causes of cancer-related mortality and morbidity. Traditional surgery usually leads to incomplete resection of gliomas resulting in the high incidence of tumor recurrence. Advanced medical imaging technology, such as fluorescence imaging-guided surgery, combined with tumor-specific imaging probes allows the identification of tumor margins and improved surgery. However, there are two pressing issues that need to be addressed: first, few fluorescence imaging probes can specifically target gliomas; second, fluorescence molecular imaging (FMI) cannot get the in-depth information of deep-seated gliomas; both of which affect the complete removal of the gliomas. In this study, the biodistribution of smart matrix metalloproteinase (MMP) targeting near-infrared (NIR) fluorescent probe MMPSense 750 FAST (MMP-750) was examined in both U87MG-GFP-fLuc glioma xenograft and orthotopic mouse models using FMI. Then, CT and FMI images of orthotopic gliomas were acquired for the reconstruction of fluorescence molecular tomography (FMT) using a randomly enhanced adaptive subspace pursuit (REASP) algorithm. Furthermore, the resection of orthotopic glioma was performed using the fluorescence surgical navigation system after the injection of the MMP-750 probe. After surgery, bioluminescence imaging (BLI) and hematoxylin and eosin staining were carried out to confirm the precision resection of the tumor. FMI results showed that the MMP-750 probe can specifically target U87MG glioma in vivo. FMT presented the spatial information of the orthotopic glioma using the REASP reconstruction algorithm. Furthermore, MMP-750 could effectively delineate the tumor margin during glioma surgery leading to a complete resection of the tumors. The smart MMP-750 specifically targets the glioma and FMT of MMP-750 provides 3D information for the spatial localization of the glioma. MMP-750 can work as an ideal fluorescence probe for guiding the intraoperative surgical resection of the glioma, possessing clinical translation.

Brain Image Segmentation using Level Set : An Hybrid Approach

Advances in medical imaging technologies have given rise for effective diagnostic procedures. The acquisition promptness and resolution enhancements of imaging modalities have given physicians more information, less invasively about their patients. Active contours are used to segment, match and track images of an atomic structure by manipulating constraints derived from the image data together with prior knowledge about the location, size, and shape of these structures. The level set method is referred as a part of active contour family. The major disadvantages of level set method are initialization of controlling parameters and time complexity. The proposed method adopts Robust Spatial Kernel Fuzzy C-Means (RSKFCM) and Lattice Boltzmann Method (LBM) to overcome these drawbacks. RSKFCM is based on standard Fuzzy C-Means algorithm which uses Gaussian RBF kernel function as distance metric and incorporates spatial information. The LBM uses the energy function to determine and reduce the actual processing time which addresses the time complexity. The proposed system combines both RSKFCM and LBM to form a hybrid approach, and the system is tested on a large set of MRI brain images and the experimental results are found to be improved with respect to time complexity.

Classification Based on Neuroimaging Data by Tensor Boosting.

Recent advances in medical imaging technologies generate a high volume of imaging data. Classification of cognitive outcome and disease status based on brain images is one of the most important tasks in neuroimaging studies. However it poses great challenge to the current classification methods due to the extremely high dimensionality and low signal to noise ratio in brain image data. In this article we propose a tensor boosting algorithm for classification based on neuroimaging data. The method is off-the-shelf, computationally simple and amenable to various modalities of neuroimaging data. The method is applied to an EEG data set from an alcoholism study and an MRI data set from an ADHD Global Competition and shows significantly improved classification performance.

A comparison of atherosclerotic lesion severity in the carotid bulb versus coronary arteries

With recent advances in medical imaging technology, it is becoming possible to detect atherosclerotic lesions in carotid vessels with predictable certainty. The purpose of this study was to grossly compare, in the same individual, the severity of atherosclerotic lesions in the carotid sinuses to lesions of the coronary arteries. Fifty embalmed cadavers were screened for atherosclerotic lesions of the common carotid arteries, the carotid sinus, the right coronary artery, the left coronary artery and the anterior atrioventricular coronary artery. Specimens with grossly identifiable atherosclerotic lesions were then prepared for histopathologic evaluation by standard paraffin embedding techniques. After processing, they were sectioned by microtome at five micrometers and then stained with Hemotoxylin and Eosin. Lesions were evaluated histologically and classified by both an expert histologist and a board certified pathologist using the American Heart Association plaque type classification system. A phi‐correlation was performed to detect correlations between plaque severities in multiple anatomic sites. 74% of cadavers (n=37) had detectable plaques within their carotid vessels. Statistical results suggest that plaque classification varies by region and may be predictable by individual factors.

The Accuracy of Cone Beam Computed Tomographybased Registration and Multislice Computed Tomography-based Registration for Oral and Maxillofacial Surgery: A Systematic Review

Objectives: Advances in medical imaging technologies have paved the way for facilitating great challenges in the field of oral and maxillofacial surgery. Medical image registration has become an essential step for a variety of procedures. The goal of this review is to systematically try to answer the following question: Is CBCT based registration and MSCT based registration reliable for maxillofacial surgery? Methods: The PubMed databases, the Lilacs databases and the Chochrane library for systematic reviews were searched till 1 June 2016. Results: The search yielded ten articles evaluating MSCT and CBCT registration techniques after application of certain exclusion criteria. Conclusion: Only one study was comparing the accuracy of CBCT and MSCT registration (markers) for image guided surgery, two studies evaluating CBCT landmark registration technique, four studies evaluating CBCT voxel based registration technique one study comparing the accuracy of CBCT surface based registration and voxel based registration and one study comprised two parts: evaluation of CBCT landmark registration and CBCT voxel based registration.

Wall-to-lumen ratio of intracranial arteries measured by indocyanine green angiography.

Background:The wall-to-lumen ratio (WLR) is an important parameter in vascular medicine because it indicates the character of vascular wall as well as the degree of stenosis. Despite the advances in medical imaging technologies, it is still difficult to measure the thin-walled normal intracranial arteries, and the reports on the WLR of normal intracranial artery are limited. It might be possible to calculate the WLR using the indocyanine green (ICG) angiography, which is used to observe intracranial vessels during microsurgery.Purpose:To evaluate the WLR of normal intracranial arteries using ICG angiography.Materials and Methods:From the three cases in which ICG angiography was recorded with a ruler during microsurgery, 20 measurement points were chosen for the analysis. The ICG was injected intravenously with a dose of 0.2 mg/kg, and the vessels were inspected at high magnification using an operating microscope equipped with near-infrared illumination system. The vessel outer diameter and the luminal diameter were measured using the images before and after the ICG arrival based on the pixel ratio method using a ruler as reference, respectively. The WLR was calculated as 0.5 × (vessel outer diameter − vessel luminal diameter).Results:The WLR (mean ± standard deviation) of normal intracranial arteries was 0.086 ± 0.022. The WLR tended to be high in small arteries.Conclusion:The WLR of normal intracranial arteries calculated using ICG angiography was consistent with the WLR reported in the previous reports based on human autopsy.

Tomosynthesis Imaging

<i>Tomosynthesis Imaging</i>

Increased precision of orthotopic and metastatic breast cancer surgery guided by matrix metalloproteinase-activatable near-infrared fluorescence probes.

Advanced medical imaging technology has allowed the use of fluorescence molecular imaging-guided breast cancer surgery (FMI-guided BCS) to specifically label tumour cells and to precisely distinguish tumour margins from normal tissues intra-operatively, a major challenge in the medical field. Here, we developed a surgical navigation system for real-time FMI-guided BCS. Tumours derived from highly metastatic 4T1-luc breast cancer cells, which exhibit high expression of matrix metalloproteinase (MMP) and human epidermal growth factor receptor 2 (HER2), were established in nude mice; these mice were injected with smart MMP-targeting and “always-on” HER2-targeting near-infrared (NIR) fluorescent probes. The fluorescence signal was imaged to assess in vivo binding of the probes to the tumour and metastatic sites. Then, orthotopic and metastatic breast tumours were precisely removed under the guidance of our system. The post-operative survival rate of mice was improved by 50% with the new method. Hematoxylin and eosin staining and immunohistochemical staining for MMP2 and CD11b further confirmed the precision of tumour dissection. Our method facilitated the accurate detection and complete removal of breast cancer tumours and provided a method for defining the molecular classification of breast cancer during surgery, thereby improving prognoses and survival rates.