Risk factors of radiation dose in patients undergoing peripherally-inserted central catheter procedure using conventional angiography equipment and flat panel detector-based mobile C-arm fluoroscopy.

BACKGROUND: Although peripherally-inserted central catheter (PICC) insertion is commonly performed under fluoroscopic guidance, few reports have addressed performance and dosimetry when PICC is inserted under C-arm fluoroscopy. PURPOSE: To evaluate the risk factors of radiation dose in performing PICC insertion using flat panel detector-based mobile C-arm fluoroscopy and a conventional angiography machine. MATERIAL AND METHODS: Ninety-eight patients underwent the PICC procedure using conventional angiography equipment (n=49) or flat panel detector-based mobile C-arm fluoroscopy (n=49). Data were prospectively analyzed from July to November 2012. Dose-area product (DAP), tube voltage, tube current, fluoroscopy time, and image quality measured on a 5-point scale were estimated and compared using appropriate statistical tests. RESULTS: There were no significant differences in tube voltage, fluoroscopy time, and image quality between conventional angiography and mobile C-arm fluoroscopy. DAP, mean arm tube current, and tube current in chest fluoroscopy were significantly lower in mobile C-arm fluoroscopy than using the conventional angiography machine (P < 0.05). Multivariate analysis identified tube current in chest fluoroscopy, arm tube current, and fluoroscopy equipment as significant risk factors for elevated radiation dose in PICC insertion. CONCLUSION: PICC insertion can be performed using flat panel detector-based mobile C-arm fluoroscopy instead of a conventional angiography machine. Image quality and fluoroscopy time were not different between the two systems and the use of C-arm fluoroscopy significantly reduced radiation dose.

Feasibility study of deep-learning-based bone suppression incorporated with single-energy material decomposition technique in chest X-rays.

OBJECTIVE: To improve the detection of lung abnormalities in chest X-rays by accurately suppressing overlapping bone structures in the lung area. According to literature on missed lung cancer in chest X-rays, such structures are a significant cause of chest-related diagnostic errors. METHODS: This study presents a deep-learning-based bone suppression method where a residual U-Net model is trained for chest X-rays using data set generated from the single-energy material decomposition (SEMD) technique on CT. Synthetic projection images and soft-tissue selective images were obtained from the CT data set via the SEMD, which were then used as the input and label data of the U-Net network. The trained network was tested on synthetic chest X-rays and two real chest radiographs. RESULTS: Bone-suppressed images of the real chest radiographs obtained by the proposed method were similar to the results from the American Association of Physicists in Medicine lung CT data; pulmonary nodules in the soft-tissue selective images appeared more clearly than in the synthetic projection images. The peak signal-to-noise ratio and structural similarity values measured between the output and the corresponding label images were approximately 17.85 and 0.90, respectively. CONCLUSION: The proposed method effectively yielded bone-suppressed chest X-ray images, indicating its clinical usefulness, and it can improve the detection of lung abnormalities in chest X-rays. ADVANCES IN KNOWLEDGE: The idea of using SEMD to obtain large amounts of paired images for deep-learning-based bone suppression algorithms is novel.