Marc Kohli, MD

CT
How to comply with CMS's CT radiation dose measure
Inconsistency in CT radiation doses represents a modifiable health risk, and doses can be standardized through auditing and feedback to hospitals and physicians.
June 27, 2024
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AI boosts lung nodule detection but doesn't reduce interpretation time
The study finding is surprising -- and merits further research.
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Deep learning improves disease detection on echo exams
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An RT weighs in on MRI safety: We know better, we just don’t do better
Photographs show an example of a Y-90 resin microsphere transarterial radioembolization (TARE) procedure using the generation 2 system. (A) Manual portioning by radiopharmacists of therapeutic radioactivity on a laminar flow bench meeting International Organization for Standardization class 5 cleanroom standards and equipped with lead shielding. (B) Single components of the application box. (C) Assembling by the medical physics expert of application box components and vials according to respective manuals. (D) Application box in angiography intervention room with application lines connected to the microcatheter while the nuclear medicine physician administers the microspheres. (E) Storage of empty vials and waste in an acrylic glass bin by the nuclear medicine physician. (F) Measurement of dose rates on the patient’s liver surface immediately after TARE.
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Radiation exposure low during TARE procedures
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AI boosts lung nodule detection but doesn't reduce interpretation time
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Physics-based technique improves reproducibility of lung density metrics
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Better quantifications of lung density may produce more precise assessments of lung disease severity.
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CT images in a 56-year-old man with preexisting mild centrilobular emphysema at baseline who currently smoked. (A) CT scan imaged with a reduced-dose (RD) acquisition protocol and (B) full-dose (FD) acquisition protocol. (C) The harmonized image obtained with the noise-adaptive median filtering technique. (D) Corresponding lung density histogram.
Photon-counting CT: The art of transition
By Matthew Dedman
In a new column, Matthew Dedman of Siemens Healthineers North America discusses the benefits and challenges of transitioning to photo-counting CT.
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Matthew Dedman of Siemens Healthineers North America.
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The model identified lung malignancies and produced consistent, structured reports, but needs further refinement.
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Brainomix deploys AI stroke platform across WVU Health System
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Brainomix's stroke AI software will be used at 25 rural hospital sites in the West Virginia University (WVU) Health System.
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Picture Health publishes study findings for lung cancer AI biomarker
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ECR: Cardiac imaging is at the 'heart' of precision medicine
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DL segmentation of chest muscle volume shows promise for COPD patients
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Deep learning (DL)-based muscle volume assessment from routine CT scans could help identify COPD patients with comorbid sarcopenia.
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Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.
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