Archive: 29 September 2021

4D flow imaging as a predictive tool for aortic disease

4D flow magnetic resonance imaging (MRI) has the potential to identify patients with a higher risk of severe complications from aortic degeneration Northwestern Medicine study published. The study employed a new 4D flow MRI heatmap concept to detect abnormal aortic wall shear stress, a known stimulus for arterial wall dysfunction.

This publication showcases data to validate 4D flow imaging as a clinical predictive tool for bicuspid aortopathy. Clinicians can use this imaging tool and biomarker to help be more precise about prophylactic aortic resection.

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Experts give top tips on how to succeed in breast imaging

What personal qualities and attributes do you need to achieve success in breast imaging? How should you approach artificial intelligence (AI)? How can you conduct better research projects? And what can be said about the future?

Committee members of the Young Club of the European Society of Breast Imaging (EUSOBI) put these questions to 11 senior officials and members of the organization. The experts were asked to give their answers in the form of three top tips.

Find out what their tips are:

6 issues radiology must address after COVID-19

A panel at the Association of University Radiologists(AUR) identified six themes that the radiology industry must address post COVID-19.

  1. Individual and organizational resilience.
  2. Patient care disparities and inequities.
  3. Telehealth and remote work functionality.
  4. Prioritizing innovations and technological advances.
  5. Determining societal responsibility of radiology practices and industry.
  6. Need for business models that support partnerships between academia and industry.

Here’s what radiologists should do after they’ve committed an error

The chair of the American College of Radiology’s Commission on Leadership and Practice Development, recently asked radiologists what they should do in case of an error.

After a mistake, calling the referring physician to ensure the patient is taken care of is top priority, in addition to documenting discussions, according to Jennifer C. Broder, MD, vice-chair of radiology quality and safety at Lahey Hospital & Medical Center in Massachusetts.

The next move depends on the type of error. Disclosing the mistake to patients is a must, and rads should consider seeking guidance from experts, including risk managers, to discuss the problem and next steps.

Read other solutions that radiologists gave to this question:

New PET imaging agent alters prostate cancer plan for more than 40% of patients

A recently developed PET imaging agent is earning praise following positive clinical trial results in men with high-risk prostate cancer.

North Billerica, Massachusetts-based Lantheus presented early findings from a study at the recent American Urological Association virtual meeting. The PSMA-targeted agent—piflufolastat F-18—detected disease that had spread outside the prostate in nearly one-third of men.

Additionally, an independent, retrospective review showed the imaging agent led to possible changes in care management strategy for 43.6% of patients, including decisions regarding surgery or radiation therapy.

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What are the 4 greatest challenges facing radiology right now?

Dr. Raman Uberoi is the new Medical Director, Professional Practice (MDPP) for Clinical Radiology at the U.K. Royal College of Radiologists (RCR). In this article, he speaks about the future of radiology, what leadership means to him, and how he sees the RCR developing over the coming years.

Some of the main challenges the following are the main challenges facing radiology according to him are:

  1. Visibility and making patients and decision-makers, particularly politicians, understand the importance of having strong radiology departments.
  2. The workforce remains a key dilemma for delivering excellent care.
  3. Having the systems and structures to support radiologists in delivering care, which leads to having the right training and governance frameworks, which is particularly pertinent to interventional radiology (IR)
  4. Infrastructure, particularly equipment replacement programs, information technology, and IT networking

Read the complete article:

Newly identified mechanism can accelerate the development of STING-activating drugs using imaging

A new study from scientists at the UCLA Jonsson Comprehensive Cancer Center found that emerging drugs that activate the protein STING, substantially alter the activity of metabolic pathways responsible for generating the nucleotide building blocks for DNA. 

Researchers found that alterations occur in cancer cells and can be visualized using FLT positron emission tomography (PET) imaging, marking the first time the effects of these drugs have been traced using a noninvasive imaging technique.

Understanding how STING agonists impact metabolic processes can help accelerate the clinical development of STING activating drugs in various therapeutic settings and guide the design of novel biomarkers and combination therapies.

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Radiology leaders share 5 pearls of wisdom for navigating burnout

COVID-19 has “without a doubt” amplified burnout, according to Cheri Canon, MD, a radiologist at the University of Alabama at Birmingham. 

And while most rads were spared direct contact with COVID-19 patients, abrupt changes to everyday life outside of work have also contributed to burnout. But healthcare providers often come together during crises, and Canon says she is energized by seeing the remarkable care from providers around her.

Read more about ways that radiologists can manage burnout during these times:

New deep learning method boosts MRI results without requiring new data

A team of researchers from Washington University in St. Louis has found a new deep learning method that can minimize artifacts and other noise in MRI images that come from movement and a short image-acquisition time.

Deep learning learns directly from the training data how to determine the signal from artifacts and noise, or variations in signal intensity in an image. Many existing deep learning-based MRI reconstruction methods are able to remove artifacts and noise but they learn from a ground truth reference, which can be difficult to obtain.

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Deep learning model automates brain tumor classification

Recently, scientists from the US developed a model capable of classifying numerous intracranial tumour types without the need for a scalpel. The model, called a convolutional neural network (CNN), uses deep learning – a type of machine learning algorithm found in image recognition software – to recognize these tumours in MR images, based on hierarchical features such as location and morphology. The team’s CNN could accurately classify several brain cancers with no manual interaction.

This network is the first step toward developing an artificial intelligence-augmented radiology workflow that can support image interpretation by providing quantitative information and statistics

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