Jennifer Ni Mhuircheartaigh
University College Hospital
4 Papers
19 Citations
Jennifer Ni Mhuircheartaigh is an academic researcher from University College Hospital. The author has contributed to research in topics: Supine position & Pulmonary embolism. The author has an hindex of 4, co-authored 4 publications.
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Papers
Pulmonary CT Angiography Protocol Adapted to the Hemodynamic Effects of Pregnancy
TL;DR: A pulmonary CTA protocol optimized for pregnancy significantly improved image quality by increasing pulmonary arterial opacification, improving diagnostic adequacy, and decreasing transient interruption of the contrast bolus by unopacified blood from the IVC.
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Hypotonic MR duodenography with water ingestion alone: feasibility and technique.
Carmel G. Cronin,Geraldine Dowd,Jennifer Ni Mhuircheartaigh,Eithne DeLappe,Ruaridh H. Allen,Clare Roche,Joseph M. Murphy +6 more
TL;DR: Per-oral, single-contrast, hypotonic MR duodenography is a feasible, simple, fast mode of investigation of the duodenum, which does not involve radiation and represents a useful technique in the armamentarium of the radiologist.
20
Isolated pharmacomechanical thrombolysis plus primary stenting in a single procedure to treat acute thrombotic superior vena cava syndrome.
Gerard J. O’Sullivan,Jennifer Ni Mhuircheartaigh,David Ferguson,Eithne DeLappe,Conor O'Riordan,Ann Michelle Browne +5 more
TL;DR: Combining IPMT with immediate stenting during the same session is an effective method for managing acute thrombotic SVC syndrome and limiting the exposure time and number of interventions performed on seriously ill patients.
17
MRI small-bowel follow-through: prone versus supine patient positioning for best small-bowel distention and lesion detection.
Carmel G. Cronin,Derek G. Lohan,Jennifer Ni Mhuircheartaigh,David A McKenna,Nasser Alhajeri,Clare Roche,Joseph M. Murphy +6 more
TL;DR: Although use of the prone position results in superior small-Bowel distention during MRI small-bowel follow-through, both the prone and supine positions are equal in terms of lesion detection and feature visualization.