Nicola Goatman

MRI
More T, doctor? No thanks
As the European market recovers from economic turmoil, cost is an ever important factor in MRI purchasing decisions, and advanced 1.5-tesla systems are likely to account for a far higher proportion of unit shipments compared with 3-tesla systems over the next five years.
March 11, 2015
2015 03 12 11 46 45 399 Philips Mri Scanner
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Pediatric Imaging
Practice Management
Patient-friendly radiology reports improve family comprehension
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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Administration
Imaging turnaround times continued to climb in 2024
(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.
Breast
DBT-based foundational model classifies breast density
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Patient-friendly radiology reports improve family comprehension
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Early CTA leads to improved stroke outcomes for patients with BCVI
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Most histotripsy procedures used for metastatic disease treatment
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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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DBT-based foundational model classifies breast density
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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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Study authors are calling for better strategies for early detection and intervention for pancreatic cysts that become malignant.
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ED patients prefer empathetic communication for incidental findings
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Incorporating patient preferences into communication may improve understanding and follow-up in addressing incidental findings.
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