Pre- and postoperative MR brain imaging with automatic planning and scanning software in tumor patients: an intraindividual comparative study at 3 Tesla.

PURPOSE: To evaluate the feasibility of automatic planning and scanning of brain MR imaging (MRI) protocols on a clinical 3 Tesla system in tumor patients before and after neurosurgical intervention. MATERIALS AND METHODS: Twenty-nine patients with intra-axial lesions were examined with automated planscan software pre- and postoperatively. MR section geometries were determined using intensity-based three-dimensional registration and an extraction of landmarks. The technique involved an active shape model to match the boundaries of anatomical structures and typical shape variations. Insufficient geometries were corrected manually by a trained operator. RESULTS: In 29/29 of the preoperative and 47/58 MRI sessions in total, no manual interaction was necessary. Predominantly minor corrections were necessary in 11/29 postoperative sessions, with critical corrections (> or = 3-mm offcenter change or > or = 5 degrees in alignment of the stacks) in 3/58 sessions. Mean offcenter correction was 1.41 mm (range, 0-7.33 mm), mean angle change toward the midline or commissural line was 1.43 degrees (range, 0-8.05 degrees ). CONCLUSION: Automatic planning and scanning before and after brain surgery yields robust results in most of the patients with substantial shape deviations. The dimensions of necessary geometry corrections are predominantly small. These results are promising to minimize interscan variability in longitudinal studies.

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

A concept is proposed to simplify patient positioning and scan planning to improve ease of use and workflow in MR. After patient preparation in front of the scanner the operator selects the anatomy of interest by a single push-button action. Subsequently, the patient table is moved automatically into the scanner, while real-time 3D isotropic low-resolution continuously moving table scout scanning is performed using patient-independent MR system settings. With a real-time organ identification process running in parallel and steering the scanner, the target anatomy can be positioned fully automatically in the scanner's sensitive volume. The desired diagnostic examination of the anatomy of interest can be planned and continued immediately using the geometric information derived from the acquired 3D data. The concept was implemented and successfully tested in vivo in 12 healthy volunteers, focusing on the liver as the target anatomy. The positioning accuracy achieved was on the order of several millimeters, which turned out to be sufficient for initial planning purposes. Furthermore, the impact of nonoptimal system settings on the positioning performance, the signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) was investigated. The present work proved the basic concept of the proposed approach as an element of future scan automation.

Automated planning of scan geometries in spine MRI scans.

Consistency of MR scan planning is very important for diagnosis, especially in multi-site trials and follow-up studies, where disease progress or response to treatment is evaluated. Accurate manual scan planning is tedious and requires skillful operators. On the other hand, automated scan planning is difficult due to relatively low quality of survey images ("scouts") and strict processing time constraints. This paper presents a novel method for automated planning of MRI scans of the spine. Lumbar and cervical examinations are considered, although the proposed method is extendible to other types of spine examinations, such as thoracic or total spine imaging. The automated scan planning (ASP) system consists of an anatomy recognition part, which is able to automatically detect and label the spine anatomy in the scout scan, and a planning part, which performs scan geometry planning based on recognized anatomical landmarks. A validation study demonstrates the robustness of the proposed method and its feasibility for clinical use.

Spine detection and labeling using a parts-based graphical model.

The detection and extraction of complex anatomical structures usually involves a trade-off between the complexity of local feature extraction and classification, and the complexity and performance of the subsequent structural inference from the viewpoint of combinatorial optimization. Concerning the latter, computationally efficient methods are of particular interest that return the globally-optimal structure. We present an efficient method for part-based localization of anatomical structures which embeds contextual shape knowledge in a probabilistic graphical model. It allows for robust detection even when some of the part detections are missing. The application scenario for our statistical evaluation is spine detection and labeling in magnetic resonance images.

Real-time interactive viewing of 4D kinematic MR joint studies.

Assessment of soft tissue in normal and abnormal joint motion today gets feasible by acquiring time series of 3D MRI images. However, slice-by-slice viewing of such 4D kinematic images is cumbersome, and does not allow appreciating the movement in a convenient way. Simply presenting slice data in a cine-loop will be compromised by through-plane displacements of anatomy and "jerks" between frames, both of which hamper visual analysis of the movement. To overcome these limitations, we have implemented a demonstrator for viewing 4D kinematic MRI datasets. It allows to view any user defined anatomical structure from any viewing perspective in real-time. Smoothly displaying the movement in a cine-loop is realized by image post processing, fixing any user defined anatomical structure after image acquisition.