Contrast agent Gd-EOB-DTPA (EOB·Primovist®) for low-field magnetic resonance imaging of canine focal liver lesions.

Contrast-enhanced magnetic resonance (MR) imaging with a new liver-specific contrast agent gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid (Gd-EOB-DTPA; EOB·Primovist®) was studied in 14 normal beagles and 9 dogs with focal liver lesions. Gd-EOB-DTPA accumulates in normally functioning hepatocytes 20 min after injection. As with Gd-DTPA, it is also possible to perform a dynamic multiphasic examination of the liver with Gd-EOB-DTPA, including an arterial phase and a portal venous phase. First, a reliable protocol was developed and the appropriate timings for the dynamic study and the parenchymal phase in normal dogs using Gd-EOB-DTPA were determined. Second, the patterns of these images were evaluated in patient dogs with hepatic masses. The optimal time of arterial imaging was from 15 s after injection, and the optimal time for portal venous imaging was from 40 s after injection. Meanwhile, the optimal time to observe changes during the hepatobiliary phase was from 20 min after injection. In patient dogs, 11 lesions were diagnosed as malignant tumors; all were hypointense to the surrounding normal liver parenchyma during the hepatobiliary phase. Even with a low-field MR imaging unit, the sequences afforded images adequate to visualize the liver parenchyma and to detect tumors within an appropriate scan time. Contrast-enhanced MR imaging with Gd-EOB-DTPA provides good demarcation on low-field MR imaging for diagnosing canine focal liver lesions.

Breaking the language barrier: machine assisted diagnosis using the medical speech translator.

In this paper, we describe and evaluate an Open Source medical speech translation system (MedSLT) intended for safety-critical applications. The aim of this system is to eliminate the language barriers in emergency situation. It translates spoken questions from English into French, Japanese and Finnish in three medical subdomains (headache, chest pain and abdominal pain), using a vocabulary of about 250-400 words per sub-domain. The architecture is a compromise between fixed-phrase translation on one hand and complex linguistically-based systems on the other. Recognition is guided by a Context Free Grammar Language Model compiled from a general unification grammar, automatically specialised for the domain. We present an evaluation of this initial prototype that shows the advantages of this grammar-based approach for this particular translation task in term of both reliability and use.