Frequency: Quarterly E- ISSN: 2250-2939 P- ISSN: Awaited Abstracted/ Indexed in: Ulrichâ??s International Periodical Directory, Google Scholar, SCIRUS, Genamics JournalSeek
Quarterly published in print and online â??Inventi Rapid/Impact: Biomedical Imagingâ?? publishes high quality unpublished as well as high impact pre-published research and reviews catering to the needs of researchers and professionals from medical as well as engineering fields. The journal covers fundamental and translational research focused on medical imaging yielding to early detection, diagnostics, and therapy of diseases, as well as of understanding the life sciences. Areas included are: Imaging physics, Tomographic reconstruction algorithms (e.g. CT and MRI), Image processing, Picture archiving and communications systems (PACS), Image perception and observer performance, Ultrasonic imaging, Image-guided procedures etc.
Objectives: To measure phosphorus metabolites in human parotid glands by\n31P-MRS using three-dimensional chemical-shift imaging (3D-CSI), and ascertain\nwhether this method can capture changes in adenosine triphosphate\n(ATP) and phosphocreatine (PCr) levels due to saliva secretion. Study Design:\nThe parotid glands of 20 volunteers were assessed by 31P-MRS using\n3D-CSI on 3T MRI. After obtaining a first (baseline) measurement, the participants\ntook vitamin-C tablets and measurements were obtained twice\nmore, in a continuous manner....................
The registration of intraoperative ultrasound (US) images with preoperative magnetic resonance (MR) images is a challenging\r\nproblem due to the difference of information contained in each image modality. To overcome this difficulty, we introduce a new\r\nprobabilistic function based on the matching of cerebral hyperechogenic structures. In brain imaging, these structures are the\r\nliquid interfaces such as the cerebral falx and the sulci, and the lesions when the corresponding tissue is hyperechogenic. The\r\nregistration procedure is achieved by maximizing the joint probability for a voxel to be included in hyperechogenic structures in\r\nboth modalities. Experiments were carried out on real datasets acquired during neurosurgical procedures. The proposed validation\r\nframework is based on (i) visual assessment, (ii) manual expert estimations , and (iii) a robustness study. Results show that\r\nthe proposed method (i) is visually efficient, (ii) produces no statistically different registration accuracy compared to manualbased\r\nexpert registration, and (iii) converges robustly. Finally, the computation time required by our method is compatible with\r\nintraoperative use....
Skull stripping in brain magnetic resonance imaging (MRI) is an essential step to analyze\nimages of the brain. Although manual segmentation has the highest accuracy, it is a time-consuming\ntask. Therefore, various automatic segmentation algorithms of the brain in MRI have been devised\nand proposed previously. However, there is still no method that solves the entire brain extraction\nproblem satisfactorily for diverse datasets in a generic and robust way. To address these shortcomings\nof existing methods, we propose the use of a 3D-UNet for skull stripping in brain MRI. The 3D-UNet\nwas recently proposed and has been widely used for volumetric segmentation in medical images due\nto its outstanding performance. It is an extended version of the previously proposed 2D-UNet, which\nis based on a deep learning network, specifically, the convolutional neural network. We evaluated\n3D-UNet skull-stripping using a publicly available brain MRI dataset and compared the results with\nthree existing methods (BSE, ROBEX, and Kleesiekâ??s method; BSE and ROBEX are two conventional\nmethods, and Kleesiekâ??s method is based on deep learning). The 3D-UNet outperforms two typical\nmethods and shows comparable results with the specific deep learning-based algorithm, exhibiting a\nmean Dice coefficient of 0.9903, a sensitivity of 0.9853, and a specificity of 0.9953....
Background: Magnetic resonance cholangiopancreatography (MRCP) is an established technique for the evaluation\nof intra- and extrahepatic bile ducts in patients with known or suspected hepatobiliary disease. However, the ideal\nacquisition and reconstruction plane for optimal bile duct evaluation with 3D technique has not been evaluated.\nThe purpose of our study was to compare different acquisition and reconstruction planes of 3D-MRCP for bile duct\nassessment.\nMethods: 34 patients (17f/17 m, mean age 41y) referred for MRCP were included in this prospective IRB-approved\nstudy. Respiratory-triggered 3D-T2w-MRCP sequences were acquired in coronal and axial plane. Coronal and axial\nMIP were reconstructed based on each dataset (resulting in two coronal and two axial MIP, respectively). Three\nreaders in two sessions independently assessed the MIP, regarding visualization of bile ducts and image quality.\nResults were compared (Wilcoxon test). Intra- and interobserver variability were calculated (kappa-statistic).\nResults: In case of coronal data acquisition, visualization of bile duct segments was significantly better on coronal\nreconstructed MIP images as compared to axial reconstructed MIP (p < 0.05). Regarding visualization, coronal MIP of\nthe coronal acquisition were equal to coronal MIP of the axial acquisition (p > 0.05). Image quality of coronal and\naxial datasets did not differ significantly. Intra- and interobserver agreement regarding bile duct visualization were\nmoderate to excellent (?-range 0.55-1.00 and 0.42-0.85, respectively).\nConclusions: The results of our study suggest that for visualization and evaluation of intra- and extrahepatic bile\nduct segments reconstructed images in coronal orientation are preferable. The orientation of the primary dataset\n(coronal or axial) is negligibl...
Background: Hepatic angiomyolipoma is a rare benign mesenchymal tumor. We report an unusual case of a\npatient with multiple hepatic angiomyolipomas exhibiting high 18 F-fluorodeoxyglucose (FDG) uptake.\nCase presentation: A 29-year-old man with a medical history of tuberous sclerosis was admitted to our hospital for\nfever, vomiting, and weight loss. Abdominal dynamic computed tomography revealed faint hypervascular hepatic\ntumors in segments 5 (67 mm) and 6 (10 mm), with rapid washout and clear borders; however, the tumors exhibited\nno definite fatty density. Abdominal magnetic resonance imaging revealed that the hepatic lesions were slightly\nhypointense on T1-weighted imaging, slightly hyperintense on T2-weighted imaging, and hyperintense with no\napparent fat component on diffusion-weighted imaging. FDG-positron emission tomography (PET) imaging revealed\nhigh maximum standardized uptake values (SUVmax) of 6.27 (Segment 5) and 3.22 (Segment 6) in the hepatic\ntumors. A right hepatic lobectomy was performed, and part of the middle hepatic vein was also excised. Histological\nexamination revealed that these tumors were characterized by the background infiltration of numerous inflammatory\ncells, including spindle-shaped cells, and a resemblance to an inflammatory pseudotumor. Immunohistochemical\nevaluation revealed that the tumor stained positively for human melanoma black-45. The tumor was therefore considered\nan inflammatory pseudotumor-like angiomyolipoma. Although several case reports of hepatic angiomyolipoma\nhave been described or reviewed in the literature, only 3 have exhibited high 18 F-FDG uptake on PET imaging\nwith SUVmax ranging from 3.3ââ?¬â??4.0. In this case, increased 18 F-FDG uptake is more likely to appear, particularly if\nthe inflammation is predominant.\nConclusion: Although literature regarding the role of 18 F-FDG-PET in hepatic angiomyolipoma diagnosis is limited\nand the diagnostic value of 18 F-FDG-PET has not yet been clearly defined, the possibility that hepatic angiomyolipoma\nmight exhibit high 18 F-FDG uptake should be considered....
Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there\r\nis an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing\r\nthe structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, largescale\r\ncortical regions of interest (ROIs) are localized by recently developed and validated brain reference system named Dense\r\nIndividualized Common Connectivity-based Cortical Landmarks (DICCCOL) to address the limitations in the identification of\r\nthe brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI)\r\ndata. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the stateof-\r\nthe-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition,\r\nwe compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY) and scale-free\r\ngene duplication model (SF-GD), that have higher similarity with the real structural brain networks in terms of global and local\r\ngraph properties. Our experimental results suggest that among the seven theoretical graphmodels compared in this study, STICKY\r\nand SF-GD models have better performances in characterizing the structural human brain network....
Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an\nimportant technique for the diagnosis of Alzheimerâ??s disease (AD) and for predicting the onset of this\nneurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model\nof great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects.............
Background. Atypical vascular pattern is one of the most important features by differentiating between benign and malignant\npigmented skin lesions. Detection and analysis of vascular structures is a necessary initial step for skin mole assessment; it is a\nprerequisite step to provide an accurate outcomefor thewidely used 7-point checklist diagnostic algorithm. Methods. In this research\nwe present a fully automated machine learning approach for segmenting vascular structures in dermoscopy colour images.The UNet\narchitecture is based on convolutional networks and designed for fast and precise segmentation of images. After preprocessing\nthe images are randomly divided into 146516 patches of 64 Ã? 64 pixels each. Results. On the independent validation dataset\nincluding 74 images our implemented method showed high segmentation accuracy. For the U-Net convolutional neural network,\nan average DSC of 0.84, sensitivity 0.85, and specificity 0.81 has been achieved. Conclusion. Vascular structures due to small size\nand similarity to other local structures create enormous difficulties during the segmentation and assessment process. The use of\nadvanced segmentation methods like deep learning, especially convolutional neural networks, has the potential to improve the\naccuracy of advanced local structure detection....
We propose a medical image segmentation approach based on the Active Shape Model theory. We apply this method for cervical vertebra detection. The main advantage of this approach is the application of a statistical model created after a training stage. Thus, the knowledge and interaction of the domain expert intervene in this approach. Our application allows the use of two different models, that is, a global one (with several vertebrae) and a local one (with a single vertebra). Two modes of segmentation are also proposed: manual and semiautomatic. For the manual mode, only two points are selected by the user on a given image. The first point needs to be close to the lower anterior corner of the last vertebra and the second near the upper anterior corner of the first vertebra. These two points are required to initialize the segmentation process. We propose to use the Harris corner detector combined with three successive filters to carry out the semiautomatic process. The results obtained on a large set of X-ray images are very promising....
Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before being used in a different application. We describe an approach that, with few modifications, can be used in a variety of image segmentation problems. The approach is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. We describe methods for modeling rotations and variations in scales as well as a subset selection for training the classifiers. We show that the performance of our approach in tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar to or better than several algorithms specifically designed for each of these applications....
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