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Skeletal ciliopathies: a pattern recognition approach.
Handa, A, Voss, U, Hammarsjö, A, Grigelioniene, G, Nishimura, G
Japanese journal of radiology. 2020;(3):193-206
Abstract
Ciliopathy encompasses a diverse group of autosomal recessive genetic disorders caused by mutations in genes coding for components of the primary cilia. Skeletal ciliopathy forms a subset of ciliopathies characterized by distinctive skeletal changes. Common skeletal ciliopathies include Jeune asphyxiating thoracic dysplasia, Ellis-van Creveld syndrome, Sensenbrenner syndrome, and short-rib polydactyly syndromes. These disorders share common clinical and radiological features. The clinical hallmarks comprise thoracic hypoplasia with respiratory failure, body disproportion with a normal trunk length and short limbs, and severely short digits occasionally accompanied by polydactyly. Reflecting the clinical features, the radiological hallmarks consist of a narrow thorax caused by extremely short ribs, normal or only mildly affected spine, shortening of the tubular bones, and severe brachydactyly with or without polydactyly. Other radiological clues include trident ilia/pelvis and cone-shaped epiphysis. Skeletal ciliopathies are commonly associated with extraskeletal anomalies, such as progressive renal degeneration, liver disease, retinopathy, cardiac anomalies, and cerebellar abnormalities. In this article, we discuss the radiological pattern recognition approach to skeletal ciliopathies. We also describe the clinical and genetic features of skeletal ciliopathies that the radiologists should know for them to play an appropriate role in multidisciplinary care and scientific advancement of these complicated disorders.
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Fundamentals of artificial intelligence for ophthalmologists.
Ahmad, BU, Kim, JE, Rahimy, E
Current opinion in ophthalmology. 2020;(5):303-311
Abstract
PURPOSE OF REVIEW As artificial intelligence continues to develop new applications in ophthalmic image recognition, we provide here an introduction for ophthalmologists and a primer on the mechanisms of deep learning systems. RECENT FINDINGS Deep learning has lent itself to the automated interpretation of various retinal imaging modalities, including fundus photography and optical coherence tomography. Convolutional neural networks (CNN) represent the primary class of deep neural networks applied to these image analyses. These have been configured to aid in the detection of diabetes retinopathy, AMD, retinal detachment, glaucoma, and ROP, among other ocular disorders. Predictive models for retinal disease prognosis and treatment are also being validated. SUMMARY Deep learning systems have begun to demonstrate a reliable level of diagnostic accuracy equal or better to human graders for narrow image recognition tasks. However, challenges regarding the use of deep learning systems in ophthalmology remain. These include trust of unsupervised learning systems and the limited ability to recognize broad ranges of disorders.
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Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System.
Ashraf, MN, Hussain, M, Habib, Z
Current medical imaging. 2020;(4):397-426
Abstract
Diabetic Retinopathy (DR) is a major cause of blindness in diabetic patients. The increasing population of diabetic patients and difficulty to diagnose it at an early stage are limiting the screening capabilities of manual diagnosis by ophthalmologists. Color fundus images are widely used to detect DR lesions due to their comfortable, cost-effective and non-invasive acquisition procedure. Computer Aided Diagnosis (CAD) of DR based on these images can assist ophthalmologists and help in saving many sight years of diabetic patients. In a CAD system, preprocessing is a crucial phase, which significantly affects its performance. Commonly used preprocessing operations are the enhancement of poor contrast, balancing the illumination imbalance due to the spherical shape of a retina, noise reduction, image resizing to support multi-resolution, color normalization, extraction of a field of view (FOV), etc. Also, the presence of blood vessels and optic discs makes the lesion detection more challenging because these two artifacts exhibit specific attributes, which are similar to those of DR lesions. Preprocessing operations can be broadly divided into three categories: 1) fixing the native defects, 2) segmentation of blood vessels, and 3) localization and segmentation of optic discs. This paper presents a review of the state-of-the-art preprocessing techniques related to three categories of operations, highlighting their significant aspects and limitations. The survey is concluded with the most effective preprocessing methods, which have been shown to improve the accuracy and efficiency of the CAD systems.
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Ophthalmic diagnosis using deep learning with fundus images - A critical review.
Sengupta, S, Singh, A, Leopold, HA, Gulati, T, Lakshminarayanan, V
Artificial intelligence in medicine. 2020;:101758
Abstract
An overview of the applications of deep learning for ophthalmic diagnosis using retinal fundus images is presented. We describe various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk, optic cup, blood vessels as well as detection of lesions are reviewed. Recent deep learning models for classification of diseases such as age-related macular degeneration, glaucoma, and diabetic retinopathy are also discussed. Important critical insights and future research directions are given.
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APT-weighted MRI: Techniques, current neuro applications, and challenging issues.
Zhou, J, Heo, HY, Knutsson, L, van Zijl, PCM, Jiang, S
Journal of magnetic resonance imaging : JMRI. 2019;(2):347-364
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Abstract
Amide proton transfer-weighted (APTw) imaging is a molecular MRI technique that generates image contrast based predominantly on the amide protons in mobile cellular proteins and peptides that are endogenous in tissue. This technique, the most studied type of chemical exchange saturation transfer imaging, has been used successfully for imaging of protein content and pH, the latter being possible due to the strong dependence of the amide proton exchange rate on pH. In this article we briefly review the basic principles and recent technical advances of APTw imaging, which is showing promise clinically, especially for characterizing brain tumors and distinguishing recurrent tumor from treatment effects. Early applications of this approach to stroke, Alzheimer's disease, Parkinson's disease, multiple sclerosis, and traumatic brain injury are also illustrated. Finally, we outline the technical challenges for clinical APT-based imaging and discuss several controversies regarding the origin of APTw imaging signals in vivo. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019;50:347-364.
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Evidence Supporting LI-RADS Major Features for CT- and MR Imaging-based Diagnosis of Hepatocellular Carcinoma: A Systematic Review.
Tang, A, Bashir, MR, Corwin, MT, Cruite, I, Dietrich, CF, Do, RKG, Ehman, EC, Fowler, KJ, Hussain, HK, Jha, RC, et al
Radiology. 2018;(1):29-48
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Abstract
The Liver Imaging Reporting and Data System (LI-RADS) standardizes the interpretation, reporting, and data collection for imaging examinations in patients at risk for hepatocellular carcinoma (HCC). It assigns category codes reflecting relative probability of HCC to imaging-detected liver observations based on major and ancillary imaging features. LI-RADS also includes imaging features suggesting malignancy other than HCC. Supported and endorsed by the American College of Radiology (ACR), the system has been developed by a committee of radiologists, hepatologists, pathologists, surgeons, lexicon experts, and ACR staff, with input from the American Association for the Study of Liver Diseases and the Organ Procurement Transplantation Network/United Network for Organ Sharing. Development of LI-RADS has been based on literature review, expert opinion, rounds of testing and iteration, and feedback from users. This article summarizes and assesses the quality of evidence supporting each LI-RADS major feature for diagnosis of HCC, as well as of the LI-RADS imaging features suggesting malignancy other than HCC. Based on the evidence, recommendations are provided for or against their continued inclusion in LI-RADS. © RSNA, 2017 Online supplemental material is available for this article.
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Multiparametric or practical quantitative liver MRI: towards millisecond, fat fraction, kilopascal and function era.
Unal, E, Idilman, IS, Karçaaltıncaba, M
Expert review of gastroenterology & hepatology. 2017;(2):167-182
Abstract
New advances in liver magnetic resonance imaging (MRI) may enable diagnosis of unseen pathologies by conventional techniques. Normal T1 (550-620 ms for 1.5 T and 700-850 ms for 3 T), T2, T2* (>20 ms), T1rho (40-50 ms) mapping, proton density fat fraction (PDFF) (≤5%) and stiffness (2-3kPa) values can enable differentiation of a normal liver from chronic liver and diffuse diseases. Gd-EOB-DTPA can enable assessment of liver function by using postcontrast hepatobiliary phase or T1 reduction rate (normally above 60%). T1 mapping can be important for the assessment of fibrosis, amyloidosis and copper overload. T1rho mapping is promising for the assessment of liver collagen deposition. PDFF can allow objective treatment assessment in NAFLD and NASH patients. T2 and T2* are used for iron overload determination. MR fingerprinting may enable single slice acquisition and easy implementation of multiparametric MRI and follow-up of patients. Areas covered: T1, T2, T2*, PDFF and stiffness, diffusion weighted imaging, intravoxel incoherent motion imaging (ADC, D, D* and f values) and function analysis are reviewed. Expert commentary: Multiparametric MRI can enable biopsyless diagnosis and more objective staging of diffuse liver disease, cirrhosis and predisposing diseases. A comprehensive approach is needed to understand and overcome the effects of iron, fat, fibrosis, edema, inflammation and copper on MR relaxometry values in diffuse liver disease.
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Flow cytometry what you see matters: Enhanced clinical detection using image-based flow cytometry.
McFarlin, BK, Gary, MA
Methods (San Diego, Calif.). 2017;:1-8
Abstract
Image-based flow cytometry combines the throughput of traditional flow cytometry with the ability to visually confirm findings and collect novel data that would not be possible otherwise. Since image-based flow cytometry borrows measurement parameters and analysis techniques from microscopy, it is possible to collect unique measures (i.e. nuclear translocation, co-localization, cellular synapse, cellular endocytosis, etc.) that would not be possible with traditional flow cytometry. The ability to collect unique outcomes has led many researchers to develop novel assays for the monitoring and detection of a variety of clinical conditions and diseases. In many cases, investigators have innovated and expanded classical assays to provide new insight regarding clinical conditions and chronic disease. Beyond human clinical applications, image-based flow cytometry has been used to monitor marine biology changes, nano-particles for solar cell production, and particle quality in pharmaceuticals. This review article summarizes work from the major scientists working in the field of image-based flow cytometry.
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Susceptibility-weighted imaging: current status and future directions.
Liu, S, Buch, S, Chen, Y, Choi, HS, Dai, Y, Habib, C, Hu, J, Jung, JY, Luo, Y, Utriainen, D, et al
NMR in biomedicine. 2017;(4)
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Susceptibility-weighted imaging (SWI) is a method that uses the intrinsic nature of local magnetic fields to enhance image contrast in order to improve the visibility of various susceptibility sources and to facilitate diagnostic interpretation. It is also the precursor to the concept of the use of phase for quantitative susceptibility mapping (QSM). Nowadays, SWI has become a widely used clinical tool to image deoxyhemoglobin in veins, iron deposition in the brain, hemorrhages, microbleeds and calcification. In this article, we review the basics of SWI, including data acquisition, data reconstruction and post-processing. In particular, the source of cusp artifacts in phase images is investigated in detail and an improved multi-channel phase data combination algorithm is provided. In addition, we show a few clinical applications of SWI for the imaging of stroke, traumatic brain injury, carotid vessel wall, siderotic nodules in cirrhotic liver, prostate cancer, prostatic calcification, spinal cord injury and intervertebral disc degeneration. As the clinical applications of SWI continue to expand both in and outside the brain, the improvement of SWI in conjunction with QSM is an important future direction of this technology. Copyright © 2016 John Wiley & Sons, Ltd.
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Thyroid Ultrasound: State of the Art. Part 2 - Focal Thyroid Lesions.
Dighe, M, Barr, R, Bojunga, J, Cantisani, V, Chammas, MC, Cosgrove, D, Cui, XW, Dong, Y, Fenner, F, Radzina, M, et al
Medical ultrasonography. 2017;(2):195-210
Abstract
Accurate differentiation of focal thyroid nodules (FTL) and thyroid abnormalities is pivotal for proper diagnostic and therapeutic work-up. In these two part articles, the role of ultrasound techniques in the characterization of FTL and evaluation of diffuse thyroid diseases is described to expand on the recently published World Federation in Ultrasound and Medicine (WFUMB) thyroid elastography guidelines and review how this guideline fits into a complete thyroid ultrasound exam.