Ben Wilson

IS
Is your MRI scanner the weak link in your security plan?
Cybersecurity for medical imaging equipment is often more critical than for other medical devices because the equipment stores protected health information and communicates directly with PACS and electronic medical record systems. This makes imaging equipment an attractive target for hackers, as it provides the perfect portal to a hospital's most valuable asset: patient information.
July 27, 2017
2017 07 27 15 21 23 906 Computer Error 400
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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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CT
Early CTA leads to improved stroke outcomes for patients with BCVI
Liver Cancer 400
Interventional
Most histotripsy procedures used for metastatic disease treatment
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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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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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