ECR 2009

Jan 26, 2009

A window in the roof of St. Stephen's Cathedral in the heart of Vienna, Austria, host to ECR 2009.

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CT 5-star quality rating system supports optimization
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The rating framework demonstrated robust reproducibility, with three-reader concordance reaching up to 93% in highly standardized centers.
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READ-DVT model performs well in finding DVT from ultrasound reports
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The AI model accurately identified the presence or absence of deep vein thrombosis on unstructured ultrasound reports.
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Radiological Report 400
Bone scan testing has fallen 35% among Medicare beneficiaries
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Declines in testing were greatest among certain underserved groups.
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The models could provide a framework for identifying which adolescents are at higher risk of developing psychiatric conditions.
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SDMS President Aubrey Rybyinski discusses how clinics can address sonographer vacancies.
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Avoiding the ED cuts urgent CT turnaround time by half
By Will Morton
This is the first reported pilot of an expedited outpatient direct-to-CT imaging program designed to promote ED avoidance.
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VLMs miss most lung nodules on chest x-rays
By Will Morton
Findings indicate poor performance of six VLMs for lung nodule detection on chest x-rays.
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Chatgpt2
Patient-friendly radiology reports improve family comprehension
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Terminology in radiology reports can be difficult for patients and families to comprehend.
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Early CTA leads to improved stroke outcomes for patients with BCVI
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Early CTA is linked to reduced stroke, decreased complications, and reduced intensive care unit length of stay.
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Most histotripsy procedures used for metastatic disease treatment
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Trends suggest that histotripsy has low rates of procedure-related complications and is mostly performed in outpatient settings.
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Imaging turnaround times continued to climb in 2024
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The largest increases were found in ultrasound and x-ray.
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Radiologist Computer Screen X Ray
DBT-based foundational model classifies breast density
By Amerigo Allegretto
DBT-DINO showed strong performance in classifying breast density from DBT images.
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(A) Free-response receiver operating characteristic (FROC) curve for DBT-DINO and DINOv2 (Meta AI) on the detection task. DINOv2 achieved higher sensitivity than DBT-DINO across most false-positive thresholds. (B) Detection rates of DBT-DINO and DINOv2 for lesions that were ultimately found to be benign (n = 70) and lesions that were found to be cancerous (n = 66). Error bars represent 95% CIs estimated via patient-level bootstrapping with 1000 repetitions. Both models showed similar detection rates for cancerous lesions, whereas DINOv2 detected more benign lesions than DBT-DINO. (C) Example bounding box predictions for DBT-DINO and DINOv2 on the detection test set. Each image is a single two-dimensional section from a three-dimensional digital breast tomosynthesis volume; the first column shows a mediolateral oblique view, and the second and third columns show craniocaudal views. Both models struggled to detect very small lesions, such as the 3-mm-diameter lesion shown in the first column. FP = false positive.
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