The qualitative and quantitative comparison of pre- and postoperative image data is an important possibility
to validate surgical procedures, in particular, if computer assisted planning and/or navigation is performed.
Due to deformations after surgery, partially caused by the removal of tissue, a non-rigid registration scheme is
a prerequisite for a precise comparison. Interactive landmark-based schemes are a suitable approach, if high
accuracy and reliability is difficult to achieve by automatic registration approaches. Incorporation of a priori
knowledge about the anatomical structures to be registered may help to reduce interaction time and improve
accuracy. Concerning pre- and postoperative CT data of oncological liver resections the intrahepatic vessels
are suitable anatomical structures. In addition to using branching landmarks for registration, we here introduce
quasi landmarks at vessel segments with high localization precision perpendicular to the vessels and low precision
along the vessels. A comparison of interpolating thin-plate splines (TPS), interpolating Gaussian elastic body
splines (GEBS) and approximating GEBS on landmarks at vessel branchings as well as approximating GEBS
on the introduced vessel segment landmarks is performed. It turns out that the segment landmarks provide
registration accuracies as good as branching landmarks and can improve accuracy if combined with branching
landmarks. For a low number of landmarks segment landmarks are even superior.
In navigated liver surgery the key challenge is the registration of pre-operative planing and intra-operative
navigation data. Due to the patients individual anatomy the planning is based on segmented, pre-operative
CT scans whereas ultrasound captures the actual intra-operative situation. In this paper we derive a novel
method based on variational image registration methods and additional given anatomic landmarks. For
the first time we embed the landmark information as inequality hard constraints and thereby allowing for
inaccurately placed landmarks. The yielding optimization problem allows to ensure the accuracy of the
landmark fit by simultaneous intensity based image registration. Following the discretize-then-optimize
approach the overall problem is solved by a generalized Gauss-Newton-method. The upcoming linear system
is attacked by the MinRes solver. We demonstrate the applicability of the new approach for clinical data
which lead to convincing results.
The paper is concerned with image registration algorithms for the alignment of computer tomography
(CT) and 3D-ultrasound (US) images of the liver. The necessity of registration arises from the surgeon's
request to benefit from the planning data during surgery. The goal is to align the planning data, derived
from pre-operative CT-images, with the current US-images of the liver acquired during the surgery.
The registration task is complicated by the fact, that the images are of a different modality, that the
US-images are severely corrupted by noise, and that the surgeon is looking for a fast and robust scheme.
To guide and support the registration, additional pairs of corresponding landmarks are prepared. We
will present two different approaches for registration. The first one is based on the pure alignment of
the landmarks using thin plate splines. It has been successfully applied in various applications and is
now transmitted to liver surgery. In the second approach, we mix a volumetric distance measure with
the landmark interpolation constraints. In particular, we investigate the promising normalized gradient
field distance measure. We use data from actual liver surgery to illustrate the applicability and the
characteristics of both approaches. It turns out that both approaches are suitable for the registration
of multi-modal images of the liver.
KEYWORDS: Temperature metrology, Hemodynamics, Signal to noise ratio, In vivo imaging, Calibration, Blood, Tissues, Thermography, Modulation, Thermal modeling
The promising results, recently obtained in phantom experiments employing the MR-based proton resonance frequency (PRF) method as a non-invasive tool for the temperature monitoring of hyperthermia therapy, are not easily reproduced in vivo. One of the reasons is the impact of perfusion changes on the PRF-measured temperature. In our experiments in vivo, heat was supplied on one side of the volunteers knee or pelvis by a rubber hose with circulating warm water (50iC). The PRF method was calibrated by the constant temperature sensitivity of pure water of 0.011 ppm/iC. MR mapping of perfusion changes was based on T2*-weighted tracking of the first-pass kinetics of contrast agent. The hemodynamic parameters of regional blood volume (rBV) and mean transit time (MTT) were extracted by fitting pixel-by-pixel the first- pass kinetics to the gamma-variate model. Special attention was directed to improve a quality of the automatic non-linear fit at low signal-to-noise values. The distributions of PRF- based temperature changes show large areas of apparently high temperature elevations (exceeding 10iC) in regions close to the heat source, and others with just as large temperature decays in more distant regions. Areas of apparently high temperature elevations correlate with areas of blood flow increase and vice versa. In conclusion, the visible heat- induced PRF changes in vivo are primarily perfusion changes, which mask the much smaller true temperature changes.
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