Organ-focused mutual information for nonrigid multimodal registration of liver CT and Gd–EOB–DTPA-enhanced MRI

Accurate detection of liver lesions is of great importance in hepatic surgery planning. Recent studies have shown that the detection rate of liver lesions is significantly higher in gadoxetic acid-enhanced magnetic resonance imaging (Gd–EOB–DTPA-enhanced MRI) than in contrast-enhanced portal-phase computed tomography (CT); however, the latter remains essential because of its high specificity, good performance in estimating liver volumes and better vessel visibility. To characterize liver lesions using both the above image modalities, we propose a multimodal nonrigid registration framework using organ-focused mutual information (OF-MI). This proposal tries to improve mutual information (MI) based registration by adding spatial information, benefiting from the availability of expert liver segmentation in clinical protocols. The incorporation of an additional information channel containing liver segmentation information was studied. A dataset of real clinical images and simulated images was used in the validation process. A Gd–EOB–DTPA-enhanced MRI simulation framework is presented. To evaluate results, warping index errors were calculated for the simulated data, and landmark-based and surface-based errors were calculated for the real data. An improvement of the registration accuracy for OF-MI as compared with MI was found for both simulated and real datasets. Statistical significance of the difference was tested and confirmed in the simulated dataset (p < 0.01)

Does facial soft tissue protect against zygomatic fractures?: results of a finite element analysis

Introduction: Zygomatic fractures form a major entity in craniomaxillofacial traumatology. Few studies have dealt with biomechanical basics and none with the role of the facial soft tissues. Therefore this study should investigate, whether facial soft tissue plays a protecting role in lateral midfacial trauma

Computional cost of nonrigid registration algorithms based on fluid dynamics

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Advisor: Dr. habil. Fritjhof Kruggel, MPI for Human Cognitive and Brain Sciences, Leipzig

zur Erlangung des akademischen Grades DOCTOR rerum naturalium (Dr. rer. nat.) im Fachgebiet Informatik vorgelegt von Dipl. math. Gert Wollny geboren am 31.08.1968 in Leipzi

Monitoring Structural Change in the Brain: Application to Neurodegeneration

Magnetic resonance imaging (MRI) is used in clinical routine to map the brain&apos;s morphology. Structural changes due to brain growth, aging, surgical intervention or pathological processes may be detected by image registration of time-series imaging data. To monitor structural change a three step approach is pursued here: (1) rigid registration and intensity matching of an initial (reference) and follow-up MRI scans, (2) a non-rigid registration of the scans, and (3) the segmentation of the resulting displacement field. Cros-correlation is used as a similarity measure for rigid registration. Non-rigid registration is based on a fluid dynamical model. The resulting displacement fields are usually large and, therefore, hard to interpret. For a simplified but sufficient description of such vector fields, contraction mapping is proposed to detect vector field singularities. This enables the detection and analysis of singularities of any order as critical points which reflect the topology of the vector field. An application demonstrates how this method helps to increase the understanding of pathological processes in the brain

Segmentation of Vector Fields by Critical Point Analysis: Application to Brain Deformation

MRI examinations may be used to monitor the progress of neurological disease. Arising structural changes can then be quantified using non-rigid registration procedures. However, the interpretation of the resulting large scale vector fields is difficult without further processing. We propose using contraction mapping to detect critical points such as attractors and repellors in order to characterize deforming areas. With the application to time series images we show, that critical points help to get a better perception of the brain deformation and the underlying pathological process.

Visualising deformation fields computed by non-linear image registration

Magnetic resonance imaging (MRI) is used in clinical routine to map the brain&apos;s morphology. Structural changes due to brain growth, ageing, surgical intervention or pathological processes may be detected by non-linear image registration of time-series imaging data. The resulting displacement field is large and therefore, hard to interpret. For a simplified but sufficient description of the displacement field contraction mapping is proposed to detect vector field singularities. This allows the detection and analysis of singularities of any order as critical points which reflect the topology of the vector field. An application demonstrates how this method helps to increase the understanding of pathological processes in the brain