Faculty Bibliography

PURPOSE/OBJECTIVE:Na images. METHODS:Na TPI brain datasets of healthy controls acquired on a 3T Siemens Prisma system were reconstructed using conventional reconstruction, AGR and AGRdm. RESULTS:Our simulations show that compared to conventional reconstructions, AGR and AGRdm show improved bias-noise characteristics in several regions of the brain. Moreover, AGR and AGRdm images show more anatomical detail and less noise in the reconstructions of the experimental data sets. Compared to AGR and the conventional reconstruction, AGRdm shows higher contrast in the sodium concentration ratio between gray and white matter and between gray matter and the brain stem. CONCLUSION/CONCLUSIONS:Na MR imaging at 3T.

PURPOSE/OBJECTIVE:The accuracy of diffusion MRI tractography reconstruction decreases in the white matter regions with crossing fibers. The optic pathways in rodents provide a challenging structure to test new diffusion tractography approaches because of the small crossing volume within the optic chiasm and the unbalanced 9:1 proportion between the contra- and ipsilateral neural projections from the retina to the lateral geniculate nucleus, respectively. METHODS: RESULTS:ODF-FP outperformed by over 100% all the tested methods in terms of the ratios between the contra- and ipsilateral segments of the reconstructed optic pathways as well as the spatial overlap between tractography and MEMRI. CONCLUSION/CONCLUSIONS:In this challenging model system, ODF-Fingerprinting reduced uncertainty of diffusion tractography for complex structural formations of fiber bundles.

Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.

PURPOSE/OBJECTIVE:Orientation Distribution Function (ODF) peak finding methods typically fail to reconstruct fibers crossing at shallow angles below 40°, leading to errors in tractography. ODF-Fingerprinting (ODF-FP) with the biophysical multicompartment diffusion model allows for breaking this barrier. METHODS:A randomized mechanism to generate a multidimensional ODF-dictionary that covers biologically plausible ranges of intra- and extra-axonal diffusivities and fraction volumes is introduced. This enables ODF-FP to address the high variability of brain tissue. The performance of the proposed approach is evaluated on both numerical simulations and a reconstruction of major fascicles from high- and low-resolution in vivo diffusion images. RESULTS:ODF-FP with the suggested modifications correctly identifies fibers crossing at angles as shallow as 10 degrees in the simulated data. In vivo, our approach reaches 56% of true positives in determining fiber directions, resulting in visibly more accurate reconstruction of pyramidal tracts, arcuate fasciculus, and optic radiations than the state-of-the-art techniques. Moreover, the estimated diffusivity values and fraction volumes in corpus callosum conform with the values reported in the literature. CONCLUSION/CONCLUSIONS:The modified ODF-FP outperforms commonly used fiber reconstruction methods at shallow angles, which improves deterministic tractography outcomes of major fascicles. In addition, the proposed approach allows for linearization of the microstructure parameters fitting problem.

BACKGROUND: THE PURPOSE OF THE STUDY/OBJECTIVE:Na MRI dual echo relative to TSC imaging. METHODS:Na MRI) were assessed. RESULTS:Na MRI suppressed the sodium signal within both CSF and necrotic foci. CONCLUSION/CONCLUSIONS:Na MRI of BCS improves tissue conspicuity relative to TSC imaging.

Positron emission tomography and magnetic resonance imaging (PET/MRI) scanners cannot be qualified in the manner adopted for hybrid PET and computed tomography (CT) devices. The main hurdle with qualification in PET/MRI is that attenuation correction (AC) cannot be adequately measured in conventional PET phantoms due to the difficulty in converting the MRI images of the physical structures (e.g., plastic) into electron density maps. Over the last decade, a plethora of novel MR-based algorithms have been developed to more accurately derive the attenuation properties of the human head, including the skull. Although very promising, none of these techniques has yet emerged as an optimal and universally adopted strategy for AC in PET/MRI. In this work, we propose a path for PET/MRI qualification for multicenter brain imaging studies. Specifically, our solution is to separate the head attenuation correction from the other factors that affect PET data quantification and use a patient as a phantom to assess the former. The emission data collected on the integrated PET/MRI scanner to be qualified should be reconstructed using both MR- and CT-based AC methods and whole-brain qualitative and quantitative (both voxel-wise and regional) analyses should be performed. The MR-based approach will be considered satisfactory if the PET quantification bias is within the acceptance criteria specified herein. We have implemented this approach successfully across two PET/MRI scanner manufacturers at two sites.

BACKGROUND AND PURPOSE/OBJECTIVE:Conventionally, early treatment response to stereotactic radiotherapy in intracranial tumors is often determined by structural MR imaging. Tissue sodium concentration is altered by cellular integrity and energy status in cells. In this study, we aimed to investigate the feasibility of sodium MR imaging at 7T for the preliminary evaluation of radiotherapeutic efficacy for intracranial tumors. MATERIALS AND METHODS/METHODS:Data were collected from 16 patients (12 men and 4 women, 24-75 years of age) with 22 intracranial tumors who were treated with stereotactic radiation therapy using CyberKnife at our institution between December 1, 2016, and August 15, 2019. Sodium MR imaging was performed at 7T before and 48 hours, 1 week, and 1 month after CyberKnife radiation therapy. Tissue sodium concentration (TSC) was calculated and analyzed based on manually labeled regions of tumors. RESULTS:<.001)., but the edema zone has some influence. The average TSC ratios of tumor to CSF in the 22 tumors, contralateral normal tissues, edema zones, frontal cortex, and frontal white matter were 0.66 (range, 0.23-1.5), 0.30 (range, 0.15-0.43), 0.58 (range, 0.25-1.21), 0.25 (range, 0.17-0.42), and 0.30 (range, 0.19-0.49), respectively. A total of 12 tumors in 8 patients were scanned at 48 hours, 1 week, and 1 month after treatment. The average TSC at 48 hours after treatment was 0.06 higher than that before treatment and began to decrease at 1 week. The TSC ratios of 10 continued to decline and 2 tumors increased at 1 month, respectively. Tumor volume decreased by 2.4%-99% after 3 months. CONCLUSIONS:Changes in the TSC can be quantified by sodium MR imaging at 7T and used to detect radiobiologic alterations in intracranial tumors at early time points after CyberKnife radiation therapy.

PURPOSE/OBJECTIVE:To describe an approach for detection of respiratory signals using a transmitted radiofrequency (RF) reference signal called Pilot-Tone (PT) and to use the PT signal for creation of motion-resolved images based on 3D stack-of-stars imaging under free-breathing conditions. METHODS:This work explores the use of a reference RF signal generated by a small RF transmitter, placed outside the MR bore. The reference signal is received in parallel to the MR signal during each readout. Because the received PT amplitude is modulated by the subject's breathing pattern, a respiratory signal can be obtained by detecting the strength of the received PT signal over time. The breathing-induced PT signal modulation can then be used for reconstructing motion-resolved images from free-breathing scans. The PT approach was tested in volunteers using a radial stack-of-stars 3D gradient echo (GRE) sequence with golden-angle acquisition. RESULTS:Respiratory signals derived from the proposed PT method were compared to signals from a respiratory cushion sensor and k-space-center-based self-navigation under different breathing conditions. Moreover, the accuracy was assessed using a modified acquisition scheme replacing the golden-angle scheme by a zero-angle acquisition. Incorporating the PT signal into eXtra-Dimensional (XD) motion-resolved reconstruction led to improved image quality and clearer anatomical depiction of the lung and liver compared to k-space-center signal and motion-averaged reconstruction, when binned into 6, 8, and 10 motion states. CONCLUSION/CONCLUSIONS:PT is a novel concept for tracking respiratory motion. Its small dimension (8 cm), high sampling rate, and minimal interaction with the imaging scan offers great potential for resolving respiratory motion.

In the last two decades, it has been shown that anatomically-guided PET reconstruction can lead to improved bias-noise characteristics in brain PET imaging. However, despite promising results in simulations and first studies, anatomically-guided PET reconstructions are not yet available for use in routine clinical because of several reasons. In light of this, we investigate whether the improvements of anatomically-guided PET reconstruction methods can be achieved entirely in the image domain with a convolutional neural network (CNN). An entirely image-based CNN post-reconstruction approach has the advantage that no access to PET raw data is needed and, moreover, that the prediction times of trained CNNs are extremely fast on state of the art GPUs which will substantially facilitate the evaluation, fine-tuning and application of anatomically-guided PET reconstruction in real-world clinical settings. In this work, we demonstrate that anatomically-guided PET reconstruction using the asymmetric Bowsher prior can be well-approximated by a purely shift-invariant convolutional neural network in image space allowing the generation of anatomically-guided PET images in almost real-time. We show that by applying dedicated data augmentation techniques in the training phase, in which 16 [¹⁸F]FDG and 10 [¹⁸F]PE2I data sets were used, lead to a CNN that is robust against the used PET tracer, the noise level of the input PET images and the input MRI contrast. A detailed analysis of our CNN in 36 [¹⁸F]FDG, 18 [¹⁸F]PE2I, and 7 [¹⁸F]FET test data sets demonstrates that the image quality of our trained CNN is very close to the one of the target reconstructions in terms of regional mean recovery and regional structural similarity.