BackgroundStem cell therapy has a promising potential for the curing of various degenerative diseases, including congestive heart failure (CHF). In this study, we determined the efficacy of different delivery methods for stem cell administration to the heart for the treatment of CHF. Both positron emission tomography (PET) and magnetic resonance imaging (MRI) were utilized to assess the distribution of delivered stem cells.MethodsAdipose-derived stem cells of male rats were labeled with super-paramagnetic iron oxide (SPIO) and 18 F-fluorodeoxyglucose (FDG). The left anterior descending coronary artery (LAD) of the female rats was occluded to induce acute ischemic myocardial injury. Immediately after the LAD occlusion, the double-labeled stem cells were injected into the ischemic myocardium (n = 5), left ventricle (n = 5), or tail vein (n = 4). In another group of animals (n = 3), the stem cells were injected directly into the infarct rim 1 week after the LAD occlusion. Whole-body PET images and MR images were acquired to determine biodistribution of the stem cells. After the imaging, the animals were euthanized and retention of the stem cells in the vital organs was determined by measuring the cDNA specific to the Y chromosome.ResultsPET images showed that retention of the stem cells in the ischemic myocardium was dependent on the cell delivery method. The tail vein injection resulted in the least cell retention in the heart (1.2% ± 0.6% of total injected cells). Left ventricle injection led to 3.5% ± 0.9% cell retention and direct myocardial injection resulted in the highest rate of cell retention (14% ± 4%) in the heart. In the animals treated 1 week after the LAD occlusion, rate of cell retention in the heart was only 4.5% ±1.1%, suggesting that tissue injury has a negative impact on cell homing. In addition, there was a good agreement between the results obtained through PET-MR imaging and histochemical measurements.ConclusionPET-MR imaging is a reliable technique for noninvasive tracking of implanted stem cells in vivo. Direct injection of stem cells into the myocardium is the most effective way for cell transplantation to the heart in heart failure models.

A real-time reconstruction technique was developed for adaptive radiotherapy using a Linac-MRI system. Our CS-PCA algorithm can achieve tumor contours with DC greater than 0.9 and NMSE less than 0.06 at acceleration factors of up to, and including, 10×. The reconstruction speed for the Split Bregman CS ranged from 200 to 260 ms, whereas the CS-PCA reconstruction speed ranged from 5 to 20 ms implemented using nonoptimized MATLAB code.

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Investigate 3D (spatial and temporal) convolutional neural networks (CNNs) for real-time on-the-fly magnetic resonance imaging (MRI) reconstruction. In particular, we investigated the applicability of training CNNs on a patient-by-patient basis for the purpose of lung tumor segmentation. Data were acquired with our 3 T Philips Achieva system. A retrospective analysis was performed on six non-small cell lung cancer patients who received fully sampled dynamic acquisitions consisting of 650 free breathing images using a bSSFP sequence. We retrospectively undersampled the six patient’s data by 5×  and 10×  acceleration. The retrospective data was used to quantitatively compare the CNN reconstruction to gold truth data via the Dice coefficient (DC) and centroid displacement to compare the tumor segmentations. Reconstruction noise was investigated using the normalized mean square error (NMSE). We further validated the technique using prospectively undersampled data from a volunteer and motion phantom. The retrospectively undersampled data at 5×  and 10×  acceleration was reconstructed using patient specific trained CNNs. The patient average DCs for the tumor segmentation at 5×  and 10×  acceleration were 0.94 and 0.92, respectively. These DC values are greater than the inter- and intra-observer segmentations acquired by radiation oncologist experts as reported in a previous study of ours. Furthermore, the patient specific CNN can be trained in under 6 h and the reconstruction time was 65 ms per image. The prospectively undersampled CNN reconstruction data yielded qualitatively acceptable images. We have shown that 3D CNNs can be used for real-time on-the-fly dynamic image reconstruction utilizing both spatial and temporal data in this proof of concept study. We evaluated the technique using six retrospectively undersampled lung cancer patient data sets, as well as prospectively undersampled data acquired from a volunteer and motion phantom. The reconstruction speed achieved for our current implementation was 65 ms per image.

Accelerated MRI involves undersampling k-space, creating unwanted artifacts when reconstructing the data. While the strategy of incoherent k-space acquisition is proven for techniques such as compressed sensing, it may not be optimal for all techniques. This study compares the use of coherent low-resolution (coherent-LR) and incoherent undersampling phase-encoding for real-time 3D CNN image reconstruction. Data were acquired with our 3 T Philips Achieva system. A retrospective analysis was performed on six non-small cell lung cancer patients who received dynamic acquisitions consisting of 650 free breathing images using a bSSFP sequence. We retrospectively undersampled the data by 5x and 10x acceleration using the two phase-encoding schemes. A quantitative analysis was conducted evaluating the tumor segmentations from the CNN reconstructed data using the Dice coefficient (DC) and centroid displacement. The reconstruction noise was evaluated using the structural similarity index (SSIM). Furthermore, we qualitatively investigated the CNN reconstruction using prospectively undersampled data, where the fully sampled training data set is acquired separately from the accelerated undersampled data. The patient averaged DC, centroid displacement, and SSIM for the tumor segmentation at 5x and 10x was superior using coherent low-resolution undersampling. Furthermore, the patient-specific CNN can be trained in under 6 h and the reconstruction time was 54 ms per image. Both the incoherent and coherent-LR prospective CNN reconstructions yielded qualitatively acceptable images; however, the coherent-LR reconstruction appeared superior to the incoherent reconstruction. We have demonstrated that coherent-LR undersampling for real-time CNN image reconstruction performs quantitatively better for the retrospective case of lung tumor segmentation, and qualitatively better for the prospective case. The tumor segmentation mean DC increased for all six patients at 5x acceleration and the temporal (dynamic) variance of the segmentation was reduced. The reconstruction speed achieved for our current implementation was 54 ms, providing an acceptable frame rate for real-time on-the-fly MR imaging.