Critical Care Imaging Interpretation

Feb 27th, 2015Dec 31st, 1969
Brisbane, 04
AU
http://lightboxradiology.com.au/course-details.aspx?c=1056
Phone:+61 7 5449 2717
A comprehensive overview of x-ray, ultrasound, CT, and MRI interpretation of commonly encountered disease in the critical care setting.
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True-positive PSMA-PET with positive MRI in 74-year-old male with prostate cancer (Gleason score of 4 + 5; left anterior and posterior apex positive cores). (A) Left posterior peripheral apical to midprostate hyposignal visible on transaxial MRI. (B) No definite CT correlate evident (white arrow). (C) Focal high PSMA expression detectable in left posterior mid to base with SUVmax of 12.4 on transaxial fused delayed pelvic PET/CT (yellow arrow). (D) Focal high PSMA expression found in left midgland to base of prostate on maximum-intensity-projection image (yellow arrow).
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Most surveillance MRI or CT exams did not result in intervention, and treatment rates remained stable during the study period.
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Geonho (Tom) Park, PhD, from Stanford Medicine shares insights from the work he and colleagues have done in wearable ultrasound.
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PET/CT outperforms MRI after focal therapy for prostate cancer
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No accurate noninvasive method has been validated to evaluate treatment response after focal therapy.
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True-positive PSMA-PET with positive MRI in 74-year-old male with prostate cancer (Gleason score of 4 + 5; left anterior and posterior apex positive cores). (A) Left posterior peripheral apical to midprostate hyposignal visible on transaxial MRI. (B) No definite CT correlate evident (white arrow). (C) Focal high PSMA expression detectable in left posterior mid to base with SUVmax of 12.4 on transaxial fused delayed pelvic PET/CT (yellow arrow). (D) Focal high PSMA expression found in left midgland to base of prostate on maximum-intensity-projection image (yellow arrow).
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The 10-year overall survival (OS) and cancer-specific survival (CSS) rates were 68.5% and 75.5%.
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Of the 1,059 FDA-cleared devices used in radiology, three had a registered trial.
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Low-quality axial T2-weighted MRI scans with gradient-weighted class activation mapping. The low-quality MRI scans (top row) show blurred structures and artifacts making zonal and capsule boundaries indistinguishable. On the same scans with gradient-weighted class activation map overlays (bottom row), red indicates regions that were most heavily weighted by the deep learning model toward the low-quality prediction.
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(A) Ga-68 NK224 PET/CT maximum intensity projection image (left), axial PET/CT images (center), and axial CT images (right) in a 43-year-old female participant with lung adenocarcinoma show lesions in the right cerebellum (blue arrow), right frontal lobe (red arrow), and sternum (green arrow) with low, intermediate, and high tracer uptake, respectively. Photomicrographs (scale bar, 100 μm) with hematoxylin-eosin (H&E) staining (center bottom) and programmed death ligand 1 (PD-L1) immunohistochemical (IHC) staining (right bottom) show high PD-L1 expression in the sternal lesion (tumor proportion score ≥ 50%).
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Artificial intelligence (AI) assistance facilitates correct fracture identification in cases initially missed by human readers. A representative case is shown using anteroposterior radiographs in a 29-year-old male patient with wrist trauma (suspected nondisplaced transverse scaphoid fracture). The reader’s initial unaided assessment (top) resulted in a missed fracture, whereas with AI assistance (bottom), the fracture was correctly identified. The green box represents the fracture location as predicted by the AI tool, the blue box represents the reference standard annotation, and the red dots represent individual reader annotations.
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