We propose a compressed sensing type of reconstruction method that can handle large amounts of inter-frame motion by including diffeomorphic model-based registration steps within the iterative reconstruction. The method is useful for speeding up imaging of moving structures. It is also suitable for application to a newly proposed type of myocardial perfusion imaging that does not use ECG gating. Such an acquisition is a simpler alternative to conventional ECG gated acquisitions but requires severe undersampling of k-space data and more sophisticated reconstruction methods. The new methods and preliminary results are presented.
Contrast agent concentration ([CA]) must be known accurately to quantify dynamic contrast-enhanced (DCE) MR
imaging. Accurate concentrations can be obtained if the longitudinal relaxation rate constant T1 is known both pre- and
post-contrast injection. Post-contrast signal intensity in the images is often saturated and an approximation to T1 can be
difficult to obtain. One method that has been proposed for accurate T1 estimation effectively acquires multiple images
with different effective saturation recovery times (eSRTs) and fits the images to the equation for T1 recovery to obtain T1 values. This was done with a radial saturation-recovery sequence for 2D imaging of myocardial perfusion with DCE
MRI. This multi-SRT method assumes that the signal intensity is constant for different readouts in each image. Here this
assumption is not necessary as a model-based reconstruction method is proposed that directly reconstructs an image of
T1 values from k-space. The magnetization for each ray at each readout pulse is modeled in the reconstruction with
Bloch equations. Computer simulations based on a 72 ray cardiac DCE MRI acquisition were used to test the method.
The direct model-based reconstruction gave accurate T1 values and was slightly more accurate than the multi-SRT
method that used three sub-images.
This paper describes a method for estimating, from dynamic contrast-enhanced MRI raw k-space data of the breast,
parameter maps that model tissue properties associated with a compartmental model of contrast exchange. The contrast
agent kinetics, as represented by these parameter maps, are important in distinguishing benign and malignant tumors.
The proposed model-based reconstruction algorithm estimates tissue parameter maps directly from MRI k-space data,
thereby allowing a new and improved set of spatiotemporal resolution and noise tradeoffs. Realistic noise levels and an
undersampling factor of R=4 appeared to provide reasonable accuracy for the kinetic parameters of interest.
Dynamic MRI perfusion studies have proven to be useful for detecting and characterizing myocardial ischemia. Accurate segmentation of the myocardium in the dynamic contrast-enhanced (DCE) MRI images is an important step for estimation of regional perfusion. Although a great deal of research has been done for segmenting MRI scans of heart wall motion, relatively little work has been done to segment DCE MRI studies. We propose a new semi-automatic robust level set based segmentation technique that uses both spatial and temporal information. The evolution of level sets is based on a spectral speed function which is a function of the Mahalanobis distance between each pixel's time curve and the time curves of user-determined seed points in the myocardium. A curvature penalty term is included in the evolution of the contours to ensure smoothness of the evolving level sets. We also make use of shape information to constrain the evolution of the level sets. Shape models were created by using signed distance maps from manually segmented images and performing principal component analysis. Thus the algorithm has the qualities of evolving an active contour both locally, based on image values and curvature, and globally to a maximum a posteriori estimate of the left ventricle shape in order to segment the left ventricle myocardium from DCE cardiac MRI images.
The algorithm was tested on 16 DCE MRI datasets and compared to manual segmentations. The results matched the manual segmentations.
We are developing a scanner for simultaneous acquisition of x-ray computed tomography (CT) and single photon emission tomography (SPECT) images of small animals such as mice and rats. The scanner uses a cone beam geometry for both the x- ray transmission and gamma emission projections by using an area x-ray detector and pinhole collimator, respectively. The CT and SPECT data set are overlaid to form a coregistered structural-functional 3D image. The CT system includes a single CCD-based x-ray detector and a microfocus x-ray source. The SPECT scanner utilizes tungsten pinhole collimators and arrays of CsI(Tl) scintillation detectors. We describe considerations and the early performance of a prototype scanner.