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Diagnostic Radiology - Science topic
Explore the latest questions and answers in Diagnostic Radiology, and find Diagnostic Radiology experts.
Questions related to Diagnostic Radiology
And if diagnostic radiology will be irrelevant ultimately, may be in two or three decades?
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A very Emergency case, The Mother of my best friend, the results of her MRI said that she had nodular peritoneal thickening that suggested peritoneal serosal carcinoma, and ovarian cancer, also she has ascites, what is the source of ascites in case of high CA-125? We are suggesting a surgery to remove the ovarian, peritoneal biopsy and taking different samples to histology laboratory for culture and characterization, Any Informations would be helpful and well Appreciated, Many Thanks
Ali
I have been studying about X-ray Fluoroscopy imaging technique lately and it's immense use in medical diagnosis and therapy. However I find there are many disadvantages to it, such as we can only see a 2D image of a 3D object. Minor details are hard to acquire and/or observe on screen. Monochromatic display is also another problem as I see it.
What type of improvements are being currently being researched to this imaging technique? I have hit dead ends in my search through google, pubmed, Radiographics Journal & so on.
Please let me know. Thank you.
This question is not intended for detection of subdural hematomas and hygroms in case overdraindage is suspected. Also I think MRIs are not sensitive to guide fine adjustment. So they offer little benefi in this direction, except in case overdrainage is signficant; like meningeal enhancement as an example.
The question is meant for the yearly follow up of a patient whom we think is well adjusted. How to comment on the decrease or increase of the hyperdensities of trans ependymal diapedsis as an indicator of improvement or worsening. What are the results of research on this point?
Call for Book Chapters:
Intelligent Diagnosis of Lung Cancer and Respiratory Diseases
Editors:
Wellington Pinheiro dos Santos, Federal University of Pernambuco, Brazil
Juliana Carneiro Gomes, Polytechnique School of The University of Pernambuco, Brazil
Maíra Araújo de Santana, Polytechnique School of The University of Pernambuco, Brazil
Valter Augusto de Freitas Barbosa, Federal University of Pernambuco, Brazil
Introduction
The series of books Intelligent Systems in Radiology aims to present the principles and advances of diagnostic techniques in Radiology based on Artificial Intelligence, from the perspective of the advent of Digital Health. The series consists of three books. Each of them is divided into two parts: one dedicated to theoretical foundations and the other to radiological applications in the real world. This call for chapters is dedicated to the first volume.
The first book, Intelligent Diagnosis of Lung Cancer and Respiratory Diseases, is dedicated to the diagnosis of diseases of the respiratory tract or those that seriously affect the respiratory system. In the first part, the physiological foundations of the respiratory system and the formation of radiographic images and x-ray computed tomography are presented. Principles of respiratory diseases are also presented, including lung cancer, viral and bacterial pneumonia, tuberculosis, and Covid-19. In addition, the principles of pattern recognition and machine learning and the main theoretical and practical tools are also briefly presented, and libraries in the programming languages Python, Java and Matlab are also commented. The second part presents innovative works and systematic reviews of intelligent applications in the diagnosis of lung cancer, tuberculosis, viral and bacterial pneumonias, and Covid-19.
No publication fee will be demanded from the authors of the accepted chapters.
The Objective of the Book
This book series is intended for readers interested in intelligent systems to support diagnosis in Radiology. The series is composed by three books. The first one, Intelligent Diagnosis of Lung Cancer and Respiratory Diseases, the focus of this call, is dedicated to diagnosis of respiratory diseases. The second book covers the diagnosis and treatment of neurodegenerative diseases. The last book is dedicated to Neuroscience applications, from clinical to affective computing applications. All books present comprehensible theoretical fundamentals both from clinical and computer engineering perspectives.
Target Audience
This book is intended to everyone who needs to understand how radiological images, neuroscience and artificial intelligence could work together to generate solutions in the context of intelligent diagnosis support and applied neuroscience and how intelligent systems could process and analyze images to improve early diagnosis and, consequently, prognosis of diseases.
Recommended Topics
Contributors may submit proposals on topics that include, but are not limited to, those listed below. The chapters may take various forms.
Part I: Fundamentals
1. Physiology of the respiratory system
2. Fundamentals of x-ray images and computerized tomography
3. Principles of lung cancer and respiratory diseases
4. Principles of pattern recognition and machine learning
5. Principles of image processing
6. Computer-aided image diagnosis
7. Computational tools and tutorials on Python, Java and Matlab
Part II: Applications
1. Lung cancer
2. Tuberculosis
3. Viral and bacterial pneumonias
4. Covid-19
5. Emergent imaging techniques
Submission process
Potential contributors are invited to submit, on or before January 31, 2021, an abstract of 300 – 400 words proposal (excluding references) that presents the intended contributions of their chapter, intended approach and methodology.
In addition, authors should provide the following:
· Proposed titles of their chapters
· The theme (see above) of their intended chapters
· Full names
· E-mail addresses and
· Affiliations
Chapters submitted must not have been published, accepted for publication, or under consideration for publication anywhere else.
Proposals and full chapters should be submitted via EasyChair according to the following link:
By February 15, 2021, potential authors will be notified about the status of their proposed chapters. When accepted, the authors will receive further information regarding the submission process, including the formatting guidelines.
Full chapters should be submitted on or before April 16, 2021 in a single attached Word or LaTeX file with the Copyright Letter. References should follow IEEE standards. The authors should follow the formatting rules in this link:
Final submissions should be approximately 4,000-5,000 words in length, excluding references, figures, tables, and appendices. All chapters will be peer-reviewed. No fees will be demanded from the authors at any stage.
Full chapters are expected to be at least 25 pages in length, font size of 10pt for the abstract, 12pt for the body text, and single-spaced paragraphs.
Key deadlines
• January 31, 2021 - Proposal submission deadline (300-400 words)
• February 15, 2021 - Notification of acceptance of proposal
• April 16, 2021 - First draft of full chapter submission
• April 30, 2021 - Revision submission
• May 14, 2021 - Final acceptance notification
• December 2021 - Publication
Publisher
The book will be published by Bentham Science Publisher until December 2021.
Please address any questions you may have to Prof. Wellington Pinheiro dos Santos - wellington.santos@ufpe.br.
I am interested in obtaining a spectral CT dataset to test a reconstruction algorithm.
Thanks.
I am trying to evaluate the performance of a screening tool for meningitis. For this purpose, I have separate data on bacterial, viral and TB meningitis. I am interested in calculating the aforementioned statistics separately for these as they are clinically very different from each other. However, my control arm consists of patients who were suspected of meningitis but were found to not have the disease. I am not sure whether is it appropriate to use the same control arm as "negatives" while calculating these statistics separately for each type of meningitis. Please let me know if you have any suggestions. Many thanks in advance!
I think if frequency is increased resolution increased, is it correct?
Hello,
I am trying to build an application to scrape information off of the DICOM images, this is mainly CT dose values and store them in a MySQL database.
Just wanted to ask if anyone tried this and can help to set up.
Thanks.
A young patient (28 years) presented to our casualty with gunshot right chest within 1 hour of injury with stable vitals except tachycardia. The entry wound was in the right lower chest in mid-clavicular line tracting towards right side of abdomen. The bullet was found to lie in the pelvis on x-ray abdomen erect and lateral views but there was no pneumoperitoneum. Patient had right pneumothorax on x-ray of chest. Due to the mechanism of injury, direction of bullet tract and location of bullet, bowel injury and diaphragmatic injury was presumed; and patient underwent laparotomy within 3 hours of injury but per-operatively no bowel, solid organ injury or hemoperitoneum was found. The bullet could be palpated retroperitoneally against the right pubic bone. Limited retroperitoneal exploration on right side which revealed no retroperioteal injury or hematoma. No diaphragmatic rent was found. How this location of bullet can be explained on the basis of above findings? Patient was discharged after 6 days without any complications.
In medical imaging we are using ultrasound for detecting abnormalities in unborn babies. Can we do same by USCT?
I could come across a few studies that perform AP/PA Chest X-ray view classification using machine learning. However I'm puzzled if this is a truly noteworthy/real problem in the clinical setting. I would like to know whether the radiologists find it hard to distinguish between AP/PA in the clinical setting and an automated system is really required in this regard.
Can anyone share edx-files (Phoenix) for MRI avanto 1.5T about the abdomen (hepatobiliary system, gall) ?
a. Allow them to transfer into DR.
b. Make them finish their IR residency commitment first.
IR residency will be challenged by being one of the most specialized residencies in the NRMP match and yet not having a required clerkship in the medical school curriculum. How do you plan on ensuring that students have adequate exposure to IR prior to making their career decisions?
a. Heavily. We will likely recruit at least one resident per year on the Independent Pathway.
b. Sporadically. We will likely use it only to fill in gaps every few years.
c. Seldomly. We will likely never use that pathway to recruit.
d. We haven’t thought about this.
e. We won’t have an IR residency.
Do you currently participate in any of the following activities to improve medical student exposure to IR:
a. IR Student Interest Group
b. IR Sub-Internship
c. IR Electives
d. Participation in an IR Medical Student Symposium
e. IR faculty teaching in the M1-M3 medical school curriculum
f. Encourage student engagement in the SIR RFS
Given the varitey of imaging methods available between clinical methods that are use to detect various type of cancer, so it's important to note whether these methods are aggressive or not and how much harmful they are?
Certainly, imaging techniques by radiation can have destructive effects on the cancerous tumor, But does colonoscopy or endoscopy imaging also have negative effects on the cancerous tumor?
And whether doing it in the false and inexperience manner will have negative effects?
I apply algorithm to segment the CT lung -axial images from background and these images are taken from https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI
LIDC-IDRI - The Cancer Imaging Archive (TCIA) Public ...
Summary The Lung Image Database Consortium image collection (LIDC-IDRI) consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with ...
My question is that if anyone know a sit for CT segmented lungs in order to evaluate my algorithm.
Are there scoring method used in abdominal CT to assess image quality as well as pathology in the abdominal organs?
In Image segmentation, there is a method named 'Active Contour model' which is used to segment deformable model.In 'Active contour model', there are some techniques where the relation of a particular point is found out and it is multiplied by outward unit normal vector of a curve.
What is the best way to find that?or Do the geometrical method like finding tangent and then normal is to be used?
Is there any relation of outward unit normal vector with Signed distance map?
Dear all,
Hi,
Does anybody knows segmentation ground truth of Breast Ultrasound images
Thank you in advance.
Is any data-base that list f factor (roentgen-to-rad conversion factor) for different materials?
Are you a family physician or pediatrician? if yes, what would be your main reasons to request an advanced imaging test in pediatric patients?
(Please consider orthopedic cases only)
Thank you!
I am looking for a colleague to share experience and advice.
Hi every body
In our radiotherapy center, there is a Varian Linac 2100C/D with energy 6MV. However the Flattening Filter Free (FFF) mode is not available. I need PDD, profiles at several depth measured for fields 2x2, 4x4, 6x6, 8x8, 10x10, 20x20, 30x30 and 40x40 and also Sc and Scp for Varian 2100C/D 6MV energy without Flattening Filter (FFF) for comparing to simulation data obtained by Beannrc code.
Best regards
Radiographically, keartocystic odntogenic tumors ( multilocular or multilobular )have a similar radiogrphic image to ameloblastoma, are there a diagnostic radiological features that help to differentiate between the 2 lesion ?
I think direct measurement with anthropomorphic phantom is the best way, but this method needs expensive phantoms and is time consuming according to calibrate and read TLDs. Is an alternative method to asses organ doses with less difficulties?
Anybody interested in a simple tool for automated brain tumor segmentation from multimodal MRI images, please check out our BraTumIA (Brain Tumor Image Analysis) software. It can be downloaded from http://www.istb.unibe.ch/content/research/medical_image_analysis/software/index_eng.html
Hi all,
If I form an array of individual point acoustic sources of same frequency and same output pressure with equal spacing between each other, how will the total pressure from the array depend on the number of individual elements if all the elements are triggered together? Is it a linear relation? The individual point sources will be placed close proximity to each other and all the sources will be triggered at the same instant so that the phase difference can be avoided.
Please help me if anyone has an expertise in this area.
Thanks in advance,
Jose
Contrast agent for CEUS: SonoVue
1,5 Tesla MRI
I am particularly worried about the lack of interest of the authorities in Israel ,on the image quality standards and accreditation of medical physicists in diagnostic radiology.Do you have the same situation in your country?Can you suggest ways to improve this situation?
In pulmonary CT angiography, different protocols are applied with different contrast volumes. However, if we know the physiological average amount of CM that fills the pulmonary artery down to the tertiary branches, we can do good exam.
In timing the scan, we need to exclude the CM-filled SVC and the left side of the heart in order to avoid artifacts. So knowing the physiological CM volume on average patient and adjusting the scan time given the size of the chest, and heart rate, a good timing of pulmonary angiography can be performed.
One more technical factor is calculating the scan time in testing bolus technique. This method ensures a maximum concentration of CM in the pulmonary trunk. But giving that we start the scan in the caudo-cranial direction, the CM may be not maximum by the time PA is scanned. It is said that 5 seconds should be added to the time to peak, I don't understand why 5 second? In fact, rationally some seconds should be deducted from the time to peak because this time is consumed by scanning the chest from the base till it reaches the pulmonary artery.
At a certain transducer power level the maximum intensity as well as the average intensity in a volume of interest drop rapidly (instead of in a linear manner, as I would have expected). I wondered whether this is due to some kind of rescaling in 3D-US software, but couldn't find any information on that.
Image below shows the same effect observed on a Philips SONOS 7500 3DUS.
I am working with the following setup: Ultrasonix SonixTablet, running 'Porta SDK 6.07' on it, 4DL14-5/38 linear 4D transducer. Checked the manual for answers. I am taking pictures of metallic surgery tools in soft tissue, such as ex vivo pig hearts.
I'd be happy about any information(paper, book,..) related to that topic.
Thank you for your help
As a radiologist, interested in cancer imaging, i came across a publication
(Atoum, 2012), that could show correlation between elevated CA 15-3 and stage II-III breast cancer.
Of course CA 15-3 cannot detect early stage breast cancer and is therefore not recommended in screening (Duffy, 2006).
But sometimes it can be challenging to find a 2-3 cm diffusely infiltrating breast cancer in asymptomatic - especially premenopausal - women with dense tissue (Buist, 2004. Kolb, 2002).
A serum marker might be helpful to select patients for further evaluation, e.g. Breast-MRI, tomosynthesis or ultrasound, in order to find “medium size” breast cancers, that otherwise would go undetected by mammography for quite a while with worsening prognosis.
Some molecules are secreted from the gastro-intestinal mucosa to the gastro-intestinal lumen, and eventually ends up in the feces. Some positron emission tomography (PET) tracers may be secreted into the GI-lumen in this fashion, and therefore potentially confound the use of these tracers for imaging cancers and other pathology in the GI-tract. I would appreciate some good references for papers or book chapters, which describe the principles and mechanisms of this type of secretion. Thanks.
I am studying automatic segmentation of pathologies in the brain, for which I would need a manually segmented and labelled set of CT brain scans. Does anybody know of such a segmented dataset?
Is there a GATE actor that will let me complete a simulation once a certain number of events occurs in a particular detector? I am looking to determine dose delivery based on clinical PET scans which have been performed.
Gangionic plexus (GP) have an associated with initiation and maintenance of AF. Is there a way to accurately locate them with out high intensity frequency (HIF) such as nuclear imaging - MIBG?
If there is a accurate method of locating GP then what methods are currently being employed in the clinical setting which are reproducible and accurate at the same time?
If you are a clinical medical physicist, what are the major challenges in your department in order to perform invivo dosimetry for each individual patient when he/she is receiving radiotherapy or undergoing for a diagnostic radiological procedure?
Most radiological equipment and accessories used in diagnostic radiology are composed of lead e.g lead glass, lead aprons, grids etc. Lead is known to be harmful based on some research findings. So how can we best dispose out-of -use lead materials or is it proper just to dispose them to the environment?
Can anyone recommend a good publication regarding an influence of the number of counts on SPECT image quality?
Digital Subtraction Angiography (DSA) images suffer from inhomogeneous contrast agent distributions within the vessels caused by unequally distributed contrast agent.
Is it right/ wrong to apply anti-concentration diffusion model for 2D DSA images? and why?
"Nominal anode input power" is a term describing characteristics of x-ray tubes, defined in IEC 60613 standard. Does it have any official translation to German?
Personal dosimeters (viz. TLD) are used for monitoring radiation exposure. As in the current Radiology curriculum, residents hardly work in an environment where radiation monitoring would be required (except DSA), what is the current guideline for personal dose monitoring?
From a 41 year old healthy male in the emergency department due to encephalopathic state with confusion, then seizures. Lumbar puncture with 4 cells (mononuclear) and normal protein and glucose count. Still no answer of OCBs or viral serology. Toxic screen positive for Benzodiazepines and Cannabis and known use of synthetic cannabinoids.
Anyone have an idea?
Here the attached MRI:
Thanks.
Can any cardio thoracic surgeon and radiologist please tell if they experience any difficulty in detecting the nodal arteries during angiograms?
Do you see the origin and the course?
Shielding design goals are used in the design or evaluation of barriers constructed for the protection of patients, staff and the general public.
I'm researching mummies and I am trying to find pathologies for my study. I am not sure whether it is the angle of the x ray causing these 2 circles in the orbit or whether it is an anomaly.
I am doing a mini-systematic review to answer the question 'can cone beam computed tomography improve the risk assessment for inferior nerve injury during lower third molar removal compared to conventional 2d panoramic tomography alone?" My search is limited to studies between 2009 to 2013 March, but my overall finding is that yes cbct is a requisite for the most accurate risk assessment for presurgery treatment planning where an intimate relationship is shown between the inferior alveolar nerve canal and the impacted third molar - does anyone agree/disagree and further questions to be asked?
I have a quick question regarding the recently published Supplements to the "European Guidelines 4th edition". There is a PDF for download here:
I believe that "Table A5.5: Additional g-factors" is wrong. Seems that someone replaced "Breast thickness (cm)" with "Breast thickness (mm)" and multiplied g-factors by 10. Am I right, or am I missing something?
In those cases, a contrast CT scan seems to be more effective to locate and diagnose the nature of the bleeding. What do you think?
I'm conducting a study on three groups of subjects, 1 control group (HC) and two different degrees of the same pathology (D1 and D2). I would like to infer the differences of their diffusivity values. I'm using TBSS to do this. I've just compared them, two by two, using unpaired t-tests. Do you suggest to use an ANOVA design before? If yes, which should be the contrast matrix?
I suppose it should be a relatively easy question but I did not find a general agreement about this.
Looking at contrast volumes vs lung volumes - are we getting it right
Ste wedge
Bone Density in Software like Digora
Cone Beam CT software
I need to normalize two MR brain volumes in order to match them on a voxel by voxel basis. That means that I need perfect matching between the two brains, i.e. each point of each structure of the first should coincide with the same point (or at least the nearest voxel) of the second. For example the point (x,y) of the thalamus of the first brain should match the same point of the second one.
I tried to use SPM to do this because in the last few years I used it to conduct VBM analyses but I did not obtain an exact correspondence (the reason for which I reflected a lot on this kind of analysis).
Could you please suggest the best way to reach my objective?
I have an X-Ray system (fluoroscopic, cinescopy, etc) and I need to calculate the Air-Kerma values (Air-Kerma, Air-Kerma Rate, Dose Area product), and I have to be able to do it utilizing the kVA and mAs values obtained in the system.
I'm currently comparing the DTI data of three groups of subjects using TBSS.
At first glance I found significant differences among them by performing unpaired t-tests.
After removing two outliers, due to their clinical condition, and using a multiple comparison correction strategy I didn’t find significant differences in two of the comparisons anymore. I therefore tried to compare them without corrections (using a standard threshold) and I found the expected differences again.
Does anyone has the same problem? Do you think it possible to publish uncorrected data if they are reasonable?
We are thinking of starting to employ the RSNA Mirc system as a teaching file system. Does anybody have any experience they can share? Does it work the way it should, or is a lot of required functionality missing?
There are published data on the indications for referring patients to a CMR study. However, almost all these data have Ben primarily acquired in cardiology environments. So, what are radiologists asked for and what do they think is a valuable question?