Towards automatic patient positioning and scan planning using continuously moving table MR imaging

Workflow efficiency of two 1.5 T MR scanners with and without an automated user interface for head examinations

RATIONALE AND OBJECTIVES

Workflow efficiency and workload of radiological technologists (RTs) were compared in head examinations performed with two 1.5 T magnetic resonance (MR) scanners equipped with or without an automated user interface called "day optimizing throughput" (Dot) workflow engine.

MATERIALS AND METHODS

Thirty-four patients with known intracranial pathology were examined with a 1.5 T MR scanner with Dot workflow engine (Siemens MAGNETOM Aera) and with a 1.5 T MR scanner with conventional user interface (Siemens MAGNETOM Avanto) using four standardized examination protocols. The elapsed time for all necessary work steps, which were performed by 11 RTs within the total examination time, was compared for each examination at both MR scanners. The RTs evaluated the user-friendliness of both scanners by a questionnaire. Normality of distribution was checked for all continuous variables by use of the Shapiro-Wilk test. Normally distributed variables were analyzed by Student's paired t-test, otherwise Wilcoxon signed-rank test was used to compare means.

RESULTS

Total examination time of MR examinations performed with Dot engine was reduced from 24:53 to 20:01 minutes (P < .001) and the necessary RT intervention decreased by 61% (P < .001). The Dot engine's automated choice of MR protocols was significantly better assessed by the RTs than the conventional user interface (P = .001).

CONCLUSIONS

According to this preliminary study, the Dot workflow engine is a time-saving user assistance software, which decreases the RTs' effort significantly and may help to automate neuroradiological examinations for a higher workflow efficiency.

Automated scan prescription for MR imaging of deformed and normal livers

PURPOSE

We propose an automated scan prescription to assess normal and deformed livers and demonstrate its efficacy in normal volunteers and in simulated deformed livers.

METHODS

Our automated scan prescription can be used to identify the upper and lower edges of the liver enables in commonly used axial slice positioning. The liver's upper edge is detected by template matching and finally identified by applying an active shape model to a sagittal projection image. The lower edge is detected using a maximum a posteriori (MAP) probability estimate that utilizes statistical information from a region of interest (ROI) placed in the liver. This places no restraints on liver shape and is therefore effective in assessing a deformed liver. Following institutional review and approval, we tested our method in 45 healthy volunteers. We also used clinical information to simulate deformed livers and tested our method with those datasets offline.

RESULTS

We could detect the upper edges within an error range of -3 to 6 mm, even without intensity correction for normal volunteers. Similar detection of the lower edges with maximum 21-mm and 7.84-mm standard deviation for normal volunteers confirmed the superior efficacy of our modified approach for deformed livers to that using our previous method. Clinical use required approximately 10 s' computational time on a Core i5 laptop with 2-GB memory.

CONCLUSION

We propose a method for automated scan prescription in magnetic resonance (MR) imaging of the liver and demonstrate the efficacy of our algorithm for evaluating deformed livers within a practical computation time. Detection of liver edges of various shapes by applying the MAP estimate combined with statistical information from the ROI demonstrated the potential clinical utility of this technique.