Towards coronary plaque imaging using simultaneous PET-MR: a simulation study

Coronary atherosclerotic plaque rupture is the main cause of myocardial infarction and the leading killer in the US. Inflammation is a known bio-marker of plaque vulnerability and can be assessed non-invasively using fluorodeoxyglucose-positron emission tomography imaging (FDG-PET). However, cardiac and respiratory motion of the heart makes PET detection of coronary plaque very challenging. Fat surrounding coronary arteries allows the use of MRI to track plaque motion during simultaneous PET-MR examination. In this study, we proposed and assessed the performance of a fat-MR based coronary motion correction technique for improved FDG-PET coronary plaque imaging in simultaneous PET-MR. The proposed methods were evaluated in a realistic four-dimensional PET-MR simulation study obtained by combining patient water-fat separated MRI and XCAT anthropomorphic phantom. Five small lesions were digitally inserted inside the patients coronary vessels to mimic coronary atherosclerotic plaques. The heart of the XCAT phantom was digitally replaced with the patient's heart. Motion-dependent activity distributions, attenuation maps, and fat-MR volumes of the heart, were generated using the XCAT cardiac and respiratory motion fields. A full Monte Carlo simulation using Siemens mMR's geometry was performed for each motion phase. Cardiac/respiratory motion fields were estimated using non-rigid registration of the transformed fat-MR volumes and incorporated directly into the system matrix of PET reconstruction along with motion-dependent attenuation maps. The proposed motion correction method was compared to conventional PET reconstruction techniques such as no motion correction, cardiac gating, and dual cardiac-respiratory gating. Compared to uncorrected reconstructions, fat-MR based motion compensation yielded an average improvement of plaque-to-background contrast of 29.6%, 43.7%, 57.2%, and 70.6% for true plaque-to-blood ratios of 10, 15, 20 and 25:1, respectively. Channelized Hotelling observer (CHO) signal-to-noise ratio (SNR) was used to quantify plaque detectability. CHO-SNR improvement ranged from 105% to 128% for fat-MR-based motion correction as compared to no motion correction. Likewise, CHO-SNR improvement ranged from 348% to 396% as compared to both cardiac and dual cardiac-respiratory gating approaches. Based on this study, our approach, a fat-MR based motion correction for coronary plaque PET imaging using simultaneous PET-MR, offers great potential for clinical practice. The ultimate performance and limitation of our approach, however, must be fully evaluated in patient studies.

Long-term adaptation to dynamics of reaching movements: a PET study

Positron emission tomography (PET) was used to examine changes in the cerebellum as subjects learned to make movements with their right arm while holding the handle of a robot that produced a force field. Brain images were acquired during learning and then during recall at 2 and 4 weeks. We also acquired images during a control task where the force field was not learnable and subjects did not show any improvements across sessions. During the 1st day, we observed that motor errors decreased from the control condition to the learning condition. However, regional cerebral blood flow (rCBF) in the posterior region of the right cerebellar cortex initially increased from the control condition and then gradually declined with reductions in motor error. Correspondingly, rCBF in the ipsilateral deep cerebellar nuclei (DCN) initially decreased from the control condition and then increased with reductions in motor error. If measures of rCBF mainly reflect presynaptic activity of neurons, this result predicts that DCN neurons fire with a pattern that starts high in the control task then decreases as learning proceeds. Similarly, Purkinje cells should generally have their lowest activity in the control task. This pattern is consistent with neurophysiological recordings that find that cerebellar activity during early learning of a motor task may mainly reflect changes in coactivation of muscles of the limbs, rather than a learning specific signal. By the end of the first session, motor errors had reached a minimum and no further improvements were observed. However, across the weeks a region in the anterior cerebellar cortex showed gradual decreases in rCBF that could not be attributed to changes in motor performance. Because patterns of rCBF in the cortex and nuclei were highly anti-correlated, we used structural equation modeling to estimate how synaptic activity in the cerebellar cortex influenced synaptic activity in the DCN. We found a negative correlation with a strength that significantly increased during the 4 weeks. This suggests that, during long-term recall, the same input to the cerebellar cortex would produce less synaptic activity at the DCN, possibly because of reduced cerebellar cortex output to the DCN.