In vivo myocardial tissue characterization of all four chambers using high-resolution quantitative MRI

Quantitative native T(1) Mapping of the myocardium without the application of contrast agents can be used to detect fibrosis in the left ventricle. Spatial resolution of standard native T(1) mapping is limited by cardiac motion and hence is not sufficient to resolve small myocardial structures, such as the right ventricle and the atria. Here, we present a novel MR approach which provides cardiac motion information and native T(1) maps from the same data. Motion information is utilized to optimize data selection for T(1) mapping and a model-based iterative reconstruction scheme ensures high-resolution T(1) maps for the entire heart. Feasibility of the approach was demonstrated in three healthy volunteers. In the T(1) maps, the myocardium of all four chambers can be visualized and T(1) values of the left atrium and right chambers were comparable to left ventricular T(1) values

Neural networks-based regularization for large-scale medical image reconstruction

In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction. Recently, Deep Learning methods using iterative neural networks (NNs) and cascaded NNs have been reported to achieve state-of-the-art results with respect to various quantitative quality measures as PSNR, NRMSE and SSIM across different imaging modalities. However, the fact that these approaches employ the application of the forward and adjoint operators repeatedly in the network architecture requires the network to process the whole images or volumes at once, which for some applications is computationally infeasible. In this work, we follow a different reconstruction strategy by strictly separating the application of the NN, the regularization of the solution and the consistency with the measured data. The regularization is given in the form of an image prior obtained by the output of a previously trained NN which is used in a Tikhonov regularization framework. By doing so, more complex and sophisticated network architectures can be used for the removal of the artefacts or noise than it is usually the case in iterative NNs. Due to the large scale of the considered problems and the resulting computational complexity of the employed networks, the priors are obtained by processing the images or volumes as patches or slices. We evaluated the method for the cases of 3D cone-beam low dose CT and undersampled 2D radial cine MRI and compared it to a total variation-minimization-based reconstruction algorithm as well as to a method with regularization based on learned overcomplete dictionaries. The proposed method outperformed all the reported methods with respect to all chosen quantitative measures and further accelerates the regularization step in the reconstruction by several orders of magnitude

Fast myocardial T(1) mapping using cardiac motion correction

PURPOSE: To improve the efficiency of native and postcontrast high-resolution cardiac T(1) mapping by utilizing cardiac motion correction. METHODS: Common cardiac T(1) mapping techniques only acquire data in a small part of the cardiac cycle, leading to inefficient data sampling. Here, we present an approach in which 80% of each cardiac cycle is used for T(1) mapping by integration of cardiac motion correction. Golden angle radial data was acquired continuously for 8 s with in-plane resolution of 1.3 × 1.3 mm(2). Cine images were reconstructed for nonrigid cardiac motion estimation. Images at different TIs were reconstructed from the same data, and motion correction was performed prior to T(1) mapping. Native T(1) mapping was evaluated in healthy subjects. Furthermore, the technique was applied for postcontrast T(1) mapping in 5 patients with suspected fibrosis. RESULTS: Cine images with high contrast were obtained, leading to robust cardiac motion estimation. Motion-corrected T(1) maps showed myocardial T(1) times similar to cardiac-triggered T(1) maps obtained from the same data (1288 ± 49 ms and 1259 ± 55 ms, respectively) but with a 34% improved precision (spatial variation: 57.0 ± 12.5 ms and 94.8 ± 15.4 ms, respectively, P < 0.0001) due to the increased amount of data. In postcontrast T(1) maps, focal fibrosis could be confirmed with late contrast-enhancement images. CONCLUSION: The proposed approach provides high-resolution T(1) maps within 8 s. Data acquisition efficiency for T(1) mapping was improved by a factor of 5 by integration of cardiac motion correction, resulting in precise T(1) maps

Motion-corrected model-based reconstruction for 2D myocardial T1 mapping

PURPOSE: To allow for T1 mapping of the myocardium within 2.3 s for a 2D slice utilizing cardiac motion-corrected, model-based image reconstruction. METHODS: Golden radial data acquisition is continuously carried out for 2.3 s after an inversion pulse. In a first step, dynamic images are reconstructed which show both contrast changes due to T1 recovery and anatomical changes due to the heartbeat. An image registration algorithm with a signal model for T1 recovery is applied to estimate non-rigid cardiac motion. In a second step, estimated motion fields are applied during an iterative model-based T1 reconstruction. The approach was evaluated in numerical simulations, phantom experiments and in in-vivo scans in healthy volunteers. RESULTS: The accuracy of cardiac motion estimation was shown in numerical simulations with an average motion field error of 0.7 ± 0.6 mm for a motion amplitude of 5.1 mm. The accuracy of T1 estimation was demonstrated in phantom experiments, with no significant difference (p = 0.13) in T1 estimated by the proposed approach compared to an inversion-recovery reference method. In vivo, the proposed approach yielded 1.3 × 1.3 mm T1 maps with no significant difference (p = 0.77) in T1 and SDs in comparison to a cardiac-gated approach requiring 16 s scan time (i.e., seven times longer than the proposed approach). Cardiac motion correction improved the precision of T1 maps, shown by a 40% reduced SD. CONCLUSION: We have presented an approach that provides T1 maps of the myocardium in 2.3 s by utilizing both cardiac motion correction and model-based T1 reconstruction

Robust registration between cardiac MRI images and atlas for segmentation propagation

We propose a new framework to propagate the labels in a heart atlas to the cardiac MRI images for ventricle segmentations based on image registrations. The method employs the anatomical information from the atlas as priors to constrain the initialisation between the atlas and the MRI images using region based registrations. After the initialisation which minimises the possibility of local misalignments, a fluid registration is applied to fine-tune the labelling in the atlas to the detail in the MRI images. The heart shape from the atlas does not have to be representative of that of the segmented MRI images in terms of morphological variations of the heart in this framework. In the experiments, a cadaver heart atlas and a normal heart atlas were used to register to in-vivo data for ventricle segmentation propagations. The results have shown that the segmentations based on the proposed method are visually acceptable, accurate (surface distance against manual segmentations is 1.0 ± 1.0 mm in healthy volunteer data, and 1.6 ± 1.8 mm in patient data), and reproducible (0.7 ± 1.0 mm) for in-vivo cardiac MRI images. The experiments also show that the new initialisation method can correct the local misalignments and help to avoid producing unrealistic deformations in the nonrigid registrations with 21% quantitative improvement of the segmentation accuracy

A subject-specific technique for respiratory motion correction in image-guided cardiac catheterisation procedures

We describe a system for respiratory motion correction of MRI-derived roadmaps for use in X-ray guided cardiac catheterisation procedures. The technique uses a subject-specific affine motion model that is quickly constructed from a short pre-procedure MRI scan. We test a dynamic MRI sequence that acquires a small number of high resolution slices, rather than a single low resolution volume. Additionally, we use prior knowledge of the nature of cardiac respiratory motion by constraining the model to use only the dominant modes of motion. During the procedure the motion of the diaphragm is tracked in X-ray fluoroscopy images, allowing the roadmap to be updated using the motion model. X-ray image acquisition is cardiac gated. Validation is performed on four volunteer datasets and three patient datasets. The accuracy of the model in 3D was within 5 mm in 97.6% of volunteer validations. For the patients, 2D accuracy was improved from 5 to 13 mm before applying the model to 2–4 mm afterwards. For the dynamic MRI sequence comparison, the highest errors were found when using the low resolution volume sequence with an unconstrained model

PET performance evaluation of a pre-clinical SiPM-Based MR-Compatible PET Scanner

We have carried out a PET performance evaluation a silicon photo-multiplier (SiPM) based PET scanner designed for fully simultaneous pre-clinical PET/MR studies. The PET scanner has an inner diameter of 20 cm with an LYSO crystal size of 1.3 by 1.3 by 10 mm. The axial PET field of view (FOV) is 30.2 mm. The PET detector modules, which incorporate SiPMs, have been designed to be MR-compatible allowing them to be located directly within a Philips Achieva 3T MR scanner. The spatial resolution of the system measured using a point source in a non-active background, is just under 2.3 mm full width at half maximum (FWHM) in the transaxial direction when single slice rebinning (SSRB) and 2D filtered back-projection (FBP) is used for reconstruction, and 1.3 mm FWHM when resolution modeling is employed. The system sensitivity is 0.6% for a point source at the center of the FOV. The true coincidence count rate shows no sign of saturating at 30 MBq, at which point the randoms fraction is 8.2%, and the scatter fraction for a rat sized object is approximately 23%. Artifact-free images of phantoms have been obtained using FBP and iterative reconstructions. The performance is currently limited because only one of three axial ring positions is populated with detectors, and due to limitations of the first-generation detector readout ASIC used in the system. The performance of the system as described is sufficient for simultaneous PET-MR imaging of rat-sized animals and large organs within the mouse. This is demonstrated with dynamic PET and MR data acquired simultaneously from a mouse injected with a dual-labeled PET/MR probe