Articles tagged with Radiology
A new study found that turning off MRIs overnight reduces their energy use by 25-33% and switching to 'power save' mode decreases energy use by 46-51%. This technique could save U.S. healthcare between $8 million and nearly $11 million annually.
A new study found that an AI-based algorithm was superior at detecting extranodal extension in oropharynx cancer compared to expert radiologists. This technology aims to improve treatment outcomes by predicting which patients are suitable for surgery or chemotherapy and radiation.
A study by University of Cambridge researchers found that minority ethnic groups are less successful in applying to specialty training programmes in the NHS. Female applicants had a higher success rate overall, but certain specialities tend to appeal more to one gender or the other.
A study presented at the ARRS Annual Meeting reveals institutional variability in projection order and image acquisition timing for CEM protocol. Earlier-obtained recombined imaging is significantly preferred for cancer lesion characterization, with a trend towards prioritizing imaging the side of pathology.
A study found that sleeve gastrectomy decreases bone mineral density and strength in adolescents and young adults. Participants who underwent weight loss surgery lost significant amounts of weight and experienced reductions in abdominal fat tissue and muscle mass.
The American Roentgen Ray Society presents its 2023 Honorary Lecture on Advanced High-Resolution CT, dedicated to the late Dr. W. Richard Webb, who transformed thoracic imaging with his foundational work in HRCT. The lecture explores multiple conditions diagnosed via HRCT and the radiologist's role on multidisciplinary teams.
A special report highlights radiology's role in combatting climate change, emphasizing the importance of sustainable practices in healthcare. Radiologists are well-positioned to innovate more sustainable methods of care, such as telemedicine and energy-efficient equipment purchasing.
Jeong Min Lee, KSR President and professor at Seoul National University Hospital, is honored with ARRS membership. He has published over 470 scientific articles and serves on the editorial board of several publications.
Researchers found that correctly occluded left atrial appendages minimized left atrial flow stasis and thrombogenicity using 4D flow MRI and 3D phantoms. The study provides a clinical goal for the procedure in patients with atrial fibrillation.
A study at ECCMID shows AI software can detect TB from chest X-rays with sensitivity and specificity comparable to that of trained radiologists. The technology has the potential to improve diagnosis in low-resource settings where radiologists are scarce.
A novel technique of three-dimensional (3D) slice-to-volume (SVR) MRI allows for precise delineation and measurement of the fetal optic pathway (FOP). The study found that FOP was visualized in 9 cases in unprocessed fetal MRIs, compared to 55 cases in 3D SVR images.
Dr. Erik K. Paulson, MD, chair of Duke University's radiology department, has been elected as the 123rd President of ARRS for a one-year term starting April 2023. He succeeds Gary J. Whitman, who served from 2022 to 2023.
Hokkaido University researchers evaluate a material used to build model arteries, finding it suitable for medical education and surgical practice. The study also highlights the potential for improved assessment of 3D printing technology in creating highly accurate models of individual patient's artery structure.
A study published in Radiology found that digital breast tomosynthesis improved breast cancer screening performance and increased radiologists' interpretive accuracy compared to digital mammography. The results showed higher detection rates, sensitivity, and specificity for DBT, with 97.6% of assessed radiologists meeting recommended p...
A new imaging technique using a novel nuclear medicine tracer has been developed to improve the treatment of intestinal strictures in Crohn's disease patients. The technique allows for precise differentiation between inflammation and fibrosis, enabling targeted therapy.
A new AI-powered worklist reprioritization tool has been shown to significantly reduce both report turnaround time and wait time for pulmonary embolus-positive CT pulmonary angiography examinations. The study found a reduction of 47.6 minutes in report turnaround time and 21.4 minutes in wait time, with no significant difference in rea...
A new PET/CT scanner prototype has shown promising results in preclinical testing, offering higher resolution and accurate tumor assessment. The scanner aims to enhance treatment planning for head and neck cancers, which have a low five-year survival rate and high recurrence rate.
A large-scale study found that diagnostic errors in neuroradiology were associated with longer interpretation times and higher shift volumes. The study also revealed a significant increase in diagnostic errors during weekend work, highlighting the need for targeted quality improvement interventions.
A national survey of radiologists reveals moral distress is widespread, often stemming from high case volumes and lack of administrative support. The study highlights the need for education of leadership regarding causes of distress among frontline workers to address this issue.
Researchers found that DALL-E 2 has learned relevant representations of x-ray images and shows potential for text-to-image generation, but its performance varies with CT, MRI, and ultrasound image prompts. Synthetic data generated by DALL-E 2 could accelerate deep learning tools in radiology and address data sharing concerns.
A study of US newspaper articles found that lung cancer screening was generally positively covered between 2010 and 2022. However, crucial aspects such as enrollment criteria and cost issues were frequently omitted, highlighting a need for radiologists to take an active role in media coverage.
A large study of over 1 million women found DBT had a higher breast cancer detection rate and lower false positive rates compared to 2D digital mammography. The results suggest that DBT may be the more effective screening method for early breast cancer detection.
A recent study found that US zip codes in extreme socioeconomic disadvantage have limited access to accredited medical imaging facilities and centers of excellence. This lack of access can lead to delayed or missed diagnoses, exacerbating existing health disparities.
Researchers developed AI models based on UNet and MobileNet architectures to analyze standardized abnormalities in CT images, accurately identifying object presence and confidence. These models achieved an absolute percentage error of less than 5 percent, comparable to human professionals.
Axillary lymphadenopathy after a COVID-19 vaccine booster dose resolves at a mean of 102 days. Findings support follow-up intervals of at least 12 weeks to avoid delaying screening mammography for suspected vaccine-related lymphadenopathy.
A study published in Radiology found that an AI tool can accurately identify normal and abnormal chest X-rays with a high degree of sensitivity. The tool identified abnormal chest X-rays with a 99.1% sensitivity rate, outperforming clinical board-certified radiologists.
A novel AI architecture, relational reasoning network, accurately identifies anatomical landmarks in CT scans for orthodontic treatments. The model learns spatial relationships between landmarks without explicit image segmentation, achieving accuracy comparable to conventional methods.
A new consensus statement calls for routine radiological screening of contact children in suspected child physical abuse cases to prevent further harm. Siblings are at risk of experiencing physical abuse, with a strong association between index and contact children.
Using a dual-source CT scanner eliminates the need for beta-blockers and decreases nondiagnostic examinations, resulting in shorter CT completion times. The study found no difference in ED length of stay but demonstrated significant benefits in reducing nondiagnostic examinations.
The American Roentgen Ray Society (ARRS) has awarded four radiologists with the 2023 Resident/Fellow in Radiology Awards for their exceptional research, education, and administrative work. Drs. Elliot T. Varney, Vincius Alves, Luca Pasquini, and Pallavi Srivastava will present their research findings at the upcoming ARRS Annual Meeting...
Researchers found that IVC filters were effective in preventing pulmonary embolisms (PE) in patients with deep vein blood clots, even when used prophylactically. Approximately half of the patients had their filters removed without complication or recurrence of DVT or PE.
Automated AI body composition tools achieved high technical adequacy rates (97.8-99.1%) in a large sample of external abdominal CT scans. The results support the generalizability and potential for widespread adoption of these tools, which can help build trust among radiologists and physicians.
Researchers identified a higher rate of premature arteriovenous graft failure among African American patients undergoing dialysis access interventions. The study, published in Radiology, found that these patients were more vulnerable to experiencing early dialysis access dysfunction and failure.
Research found PIRI to be an independent predictor of infarct growth rate and 90-day outcomes in large-vessel occlusive stroke. The study suggests PIRI may help identify patients who could benefit from late-window endovascular thrombectomy.
Ultrafast MRI provides beneficial information for predicting DCIS upgrade to invasive cancer, including time to enhancement and lesion size. A study found that UF-MRI can accurately predict lesions diagnosed as DCIS at percutaneous biopsy that will be upgraded to invasive cancer at surgery.
Researchers found that AI significantly improved the detection of lung nodules on chest X-rays, detecting actionable nodules at a rate of 0.59% compared to 0.25% without AI assistance. The study suggests that AI may work consistently across different populations.
A systematic review and meta-analysis found that MRI-based surveillance after surgical treatment can detect clinically occult local recurrences, potentially improving patient outcomes. The study included 19 studies and showed a significant association between high-intensity surveillance and the detection of local recurrences.
A deep learning model distinguishes colon carcinoma from acute diverticulitis in CT images, improving diagnostic performance and reducing false-negative and false-positive findings. The model's sensitivity and specificity were significantly improved, supporting radiologists in patient care.
Researchers created monthly infant brain atlases to track normative trends in brain development. The atlases reveal changes in brain structure, cortical geometry, and tissue contrast, shedding light on conditions like ADHD and dyslexia. The study aims to facilitate discovery of new insights into child cognition and social behavior.
A new study published in Radiology: Cardiothoracic Imaging shows that photon-counting detector CT can acquire high-quality images at lower contrast media volume than conventional CT scanners. The technology reduces the amount of contrast needed for CT angiography, improving image quality and reducing environmental impact.
Scientists at Göttingen University have created a novel approach to produce X-ray images in color from a single exposure, eliminating the need for focusing and scanning. This breakthrough method uses an X-ray color camera and a specially structured plate to capture the intensity pattern of fluorescing atoms in a sample.
Researchers used deep learning AI to analyze chest X-rays of patients with acute chest pain syndrome, identifying those at high risk for life-threatening conditions. The model significantly improved prediction accuracy beyond conventional clinical markers, allowing for more accurate triage and potential deferral of additional testing.
Ringlike peripheral high iodine concentration maps from dual-energy CT can help guide management in patients with known lung cancer and an indeterminate solitary nodule. This finding showed excellent interobserver agreement, high specificity, and independently predicted pulmonary metastasis.
A recent study found that AI failed to pass a radiology qualifying examination, with an average accuracy of 79.5%. However, the researchers suggest that further training and revision could lead to improved results.
A new study published in the American Journal of Roentgenology found significant cost savings by eliminating sedation during pediatric brain MRI examinations. The analysis calculated costs for sedated, nonsedated, and limited MRI exams at three US hospitals, revealing that labor costs were the largest expense category.
Jon A. Jacobson, MD, FACR, has been recognized as the 2023 ARRS Distinguished Educator for his exceptional contributions to radiological education and innovative educational activities. He is a renowned expert in musculoskeletal ultrasound and MRI, with over 250 peer-reviewed publications.
The American Roentgen Ray Society has awarded the 2023 ARRS Gold Medal to Dr. Bernard F. King, Jr., MD, FACR, FSAR, in recognition of his distinguished service to radiology. Dr. King has made significant contributions to the field, including the development of contrast media recognition and treatment protocols.
A team led by Abhinav Jha is developing a novel low-count quantitative single photon emission computed tomography (LC-QSPECT) method to measure the concentration of radiopharmaceutical material in tumors and vital organs. This technique aims to provide accurate measurements of absorbed dose from radiation therapy, helping plan treatmen...
Veterinary experts warn that AI algorithms used in radiology and imaging can provide faulty or incomplete diagnoses, posing risks to patient care. The absence of regulatory oversight for veterinary AI products increases concerns over accuracy, transparency, and potential harm.
A study published in the American Journal of Roentgenology found that reconstruction using BI64 kernel and 0.4-mm slice thickness yielded improved bronchial division identification and pulmonary fissure sharpness without loss in pulmonary vessel sharpness or pathology conspicuity.
Researchers have developed a new X-ray technology that visualizes lung tissue microstructure, providing additional information for accurate diagnosis. Dark-field X-ray images can differentiate between diseased and healthy lung tissue, potentially replacing computed tomography (CT) for repeated examinations.
For adrenal lesions evaluated by single-phase dual-energy CT, fat fraction had significantly higher sensitivity than virtual noncontrast attenuation at both clinically optimal threshold and traditional ≤10 HU threshold. Fat fraction-derived metrics can help definitively diagnose incidental adrenal lesions as adenomas.
Researchers found a strong association between hotter weather, more sunshine, and higher volumes of polytrauma CT scans. The study used machine learning algorithms to forecast daily polytrauma CT occurrence, predicting 73% of high-demand days and 83% of low-demand days.
A new machine learning fusion model has been developed to diagnose ovarian cancer more accurately by combining ultrasound and photoacoustic tomography imaging. The model achieved an accuracy of 90% in detecting ovarian lesions, outperforming previous methods.
Researchers at USC's Keck School of Medicine are collaborating with Polaris Dawn to develop a novel method for collecting X-rays in outer space. They aim to harness ambient radiation to create images using analog X-ray film, which could improve medical care on long-duration spaceflights.
A large-scale international study found that patients diagnosed with lung cancer at an early stage via CT screening have a 20-year survival rate of 80 percent. This is compared to the average five-year survival rate for all lung cancer patients, which is 18.6 percent.
Researchers evaluated modified CEUS using perfluorobutane for diagnosing hepatocellular carcinoma in high-risk patients. The study found no significant difference in sensitivity, specificity, or accuracy between modified CEUS and CT/MRI LI-RADS v2018.
Researchers from the University of Ottawa found an association between marijuana smoking and chronic damage to airways, including higher rates of paraseptal emphysema and airway inflammatory changes. The study suggests that marijuana smoking may lead to additional lung damage beyond what is seen in tobacco smokers.
The IOF and ESCEO position paper recommends retaining the current WHO diagnostic criteria for osteoporosis, which has served the field well. The paper suggests that distinguishing between diagnostic and intervention thresholds can help improve treatment of patients at high risk of fractures.
A multicenter study found that left ventricular global longitudinal strain is a significant independent predictor of all-cause mortality and/or heart-failure hospitalization in patients with ischemic or nonischemic dilated cardiomyopathy. The study used cardiac MRI feature tracking to calculate six myocardial strain parameters.