ABSTRACT
We present a 41-year-old female with progressive shortness of breath immediately after moving to sea level from high altitude. The patient was found to have a large PDA with systemic RV and PA pressures and pulmonary hypertension, which resolved following PDA closure.
ABSTRACT
PURPOSE: Frequent CT scans to quantify lung involvement in cystic lung disease increases radiation exposure. Beam shaping energy filters can optimize imaging properties at lower radiation dosages. The aim of this study is to investigate whether use of SilverBeam filter and deep learning reconstruction algorithm allows for reduced radiation dose chest CT scanning in patients with lymphangioleiomyomatosis (LAM). MATERIAL AND METHODS: In a single-center prospective study, 60 consecutive patients with LAM underwent chest CT at standard and ultra-low radiation doses. Standard dose scan was performed with standard copper filter and ultra-low dose scan was performed with SilverBeam filter. Scans were reconstructed using a soft tissue kernel with deep learning reconstruction (AiCE) technique and using a soft tissue kernel with hybrid iterative reconstruction (AIDR3D). Cyst scores were quantified by semi-automated software. Signal-to-noise ratio (SNR) was calculated for each reconstruction. Data were analyzed by linear correlation, paired t-test, and Bland-Altman plots. RESULTS: Patients averaged 49.4 years and 100% were female with mean BMI 26.6 ± 6.1 kg/m2. Cyst score measured by AiCE reconstruction with SilverBeam filter correlated well with that of AIDR3D reconstruction with standard filter, with a 1.5% difference, and allowed for an 85.5% median radiation dosage reduction (0.33 mSv vs. 2.27 mSv, respectively, p < 0.001). Compared to standard filter with AIDR3D, SNR for SilverBeam AiCE images was slightly lower (3.2 vs. 3.1, respectively, p = 0.005). CONCLUSION: SilverBeam filter with deep learning reconstruction reduces radiation dosage of chest CT, while maintaining accuracy of cyst quantification as well as image quality in cystic lung disease. CLINICAL RELEVANCE STATEMENT: Radiation dosage from chest CT can be significantly reduced without sacrificing image quality by using silver filter in combination with a deep learning reconstructive algorithm. KEY POINTS: ⢠Deep learning reconstruction in chest CT had no significant effect on cyst quantification when compared to conventional hybrid iterative reconstruction. ⢠SilverBeam filter reduced radiation dosage by 85.5% compared to standard dose chest CT. ⢠SilverBeam filter in coordination with deep learning reconstruction maintained image quality and diagnostic accuracy for cyst quantification when compared to standard dose CT with hybrid iterative reconstruction.
ABSTRACT
Colonic pseudo-obstruction, also called Ogilvie's syndrome, occurs due to impaired intestinal propulsion, and may be caused by electrolyte imbalances such as hypokalemia and some endocrine disorders such as hyperparathyroidism. Secretory diarrhea due to intestinal pseudo-obstruction can cause hypokalemia. Diuretics such as amiloride can be used to treat hypokalemia, however in this case, treatment with amiloride induced hypercalcemia and unmasked hyperparathyroidism. A 73-year-old female with a history of hypertension and parathyroid adenoma presented with recurrent colonic pseudo-obstruction and chronic hypokalemia. Her hypokalemia was treated with amiloride, causing hypercalcemia of 14.4 mg/dL, elevated PTH, and altered mental status. Amiloride was subsequently discontinued with improvement in her symptoms, and her hyperparathyroidism was treated with cinacalcet. To our knowledge, this is the first report of amiloride unmasking hyperparathyroidism and inducing hypercalcemia.
