A symposium highlighting the work being done by three National Centers for Biomedical Imaging and Bioengineering (NCBIB) focusing on PET and MR imaging will be held at the 2023 World Molecular Imaging Congress (WMIC) Location: Prague, Czechia Congress dates: September 5th – September 9th 2023
Sat, September 09 7:30 AM – 9:00 AM Club E
The National Institute of Biomedical Imaging and Bioengineering (NIBIB) supports a large network of National Centers for Biomedical Imaging and Bioengineering (NCBIB) through the P41 grant mechanism. Three of these Centers comprise the faction of Molecular Imaging Technology Centers:
The Center for Molecular Imaging Technology & Translation (CMITT), Massachusetts General Hospital, led by Dr. Georges El Fakhri, aims to develop and apply new imaging technologies that will revolutionize the way scientists and physicians view and use PET and MRI.
The Resource for Molecular Imaging Agents in Precision Medicine, Johns Hopkins University, led by Dr. Martin Pomper, encompasses projects that extend from the development of reagents to detect and promote an immune reactive tumor microenvironment to the synthesis of nanodrones to treat cancer and combined small-molecule diagnostic and theranostic agents.
The PET Radiotracer Translation and Resource Center (PET-RTRC), Washington University School of Medicine, led by Dr. Robert Gropler, seeks to develop new PET radiotracers that will image biologic targets modulating the ubiquitous disease processes of inflammation and oxidative stress.
The synergistic model of the P41 mechanism interaction of service and expertise within the Centers and other collaborating laboratories ensures other researchers may gain access to the newest Molecular Imaging Technology. During this session, participants will learn about cutting-edge molecular imaging innovations, in addition to the collaborating, service, and training opportunities disseminating from the three complementary, but non-overlapping programs that may benefit your own research and projects.
Researchers at CMITT are investigating how to use machine learning to predict the sounds of speech from the movements of the tongue. This is important because it could help us to understand how speech is produced and to develop new treatments for speech disorders.
Background: Speech is produced by the movement of the tongue and other muscles in the mouth. Scientists are interested in understanding how these movements relate to the sounds we produce.
Objective: This study aimed to develop a method for predicting the sounds of speech from the movements of the tongue using tagged MRI.
Methods: The researchers used a machine learning technique called “encoder-decoder translation”. This technique allows the model to learn the relationship between two different types of data.
Results: The researchers trained their model on a dataset of 63 pairs of motion fields and speech waveforms. They found that the model was able to predict the sounds of speech with a high degree of accuracy.
Conclusion: The study’s findings suggest that encoder-decoder translation is a promising method for predicting speech from tongue movements. This method could be used to develop new treatments for speech disorders.
Xiaofeng Liu, Fangxu Xing, Jerry L. Prince, Maureen Stone, Georges El Fakhri, Jonghye Woo, “Synthesizing Audio from Tongue Motion During Speech Using Tagged MRI Via Transformer”, arXiv paper: https://arxiv.org/abs/2302.07203 Presented at SPIE Medical Imaging: Deep Dive Oral
Researchers at CMITT are working on ways to update deep learning models for brain tumor segmentation so that they can adapt to changes in data. This is important because medical images are constantly changing, as new scanners and modalities are developed. This work will be presented at MICCAI 2023.
Background: Deep learning models have been used to segment anatomical structures in medical images. However, these models can only perform well if they are trained on data from a single source domain. If the data changes, the model may not be able to adapt and will perform poorly.
Objective: This study aimed to develop a method for updating deep learning models so that they can adapt to changes in data.
Methods: The researchers developed a method using “incremental learning”. This method allows the model to be updated with new data without forgetting what it has already learned.
Results: The researchers evaluated their method on a brain tumor segmentation task. They found that the method was able to well retain the discriminability of previously learned structures, even when the data was changed.
Conclusion: The study’s findings suggest that incremental learning is a promising method for updating deep learning models. This method could be used to improve the performance of deep learning models in a variety of medical imaging tasks.
Xiaofeng Liu, Helen A. Shih, Fangxu Xing, Emiliano Santarnecchi, Georges El Fakhri, Jonghye Woo, “Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI” arXiv paper: https://arxiv.org/abs/2305.19404 To be presented at MICCAI 2023
Researchers at CMITT have been focused on improving the accuracy of PET imaging for detecting tau deposition, a hallmark of Alzheimer’s disease, by addressing the challenges posed by head motion during image acquisition. The researchers developed a motion correction method that effectively compensates for head movement during PET scans. They tested the method on a large dataset of PET studies using an imaging agent called [18F]-MK6240. By comparing motion-corrected and non-motion-corrected images, they found that the correction method reduced blurring and improved the accuracy of quantitative measures. Importantly, it also decreased the variability in tau accumulation rates across subjects, which is valuable for longitudinal studies and clinical trials related to Alzheimer’s disease treatment.
Researchers at CMITT have recently published an article on improving the resolution of brain positron emission tomography (PET) images using a technique called super-resolution (SR). SR aims to improve image quality by leveraging multiple acquisitions of the same target with known sub-resolution shifts. To achieve this, the researchers developed an SR estimation framework specifically for brain PET scans. They utilized a high-resolution infrared tracking camera to accurately measure and continuously track the shifts in real-time. By incorporating the tracking data into a PET reconstruction algorithm, they were able to correct motion and obtain PET images with visibly increased spatial resolution compared to standard static acquisitions. The study conducted experiments using moving phantoms and non-human primates, and quantitative analysis validated the effectiveness of the SR reconstruction method in improving visualization of small structures in brain PET scans. Enhanced PET resolution holds the potential for better detection of neurological disorders like Alzheimer’s disease, as well as providing a more accurate estimation of image-based input functions for quantifying dynamic brain PET studies.
Researchers from the Precision Neuroscience & Neuromodulation (PNN) program led by Dr. Santarnecchi (TR&D 3) have published a review article highlighting changes to the brain during aging and summarizing modifiable risk factors to support a healthy aging process.
Turrini S, Wong B, Eldaief M, Press DZ, Sinclair DA, Koch G, Avenanti A, Santarnecchi E. The multifactorial nature of healthy brain ageing: Brain changes, functional decline and protective factors. Ageing Res Rev. 2023 Apr 27;88:101939.
The article discusses the importance of understanding healthy brain and cognitive aging as a physiological process that should be embraced, rather than only focusing on abnormal cognitive decline or exceptionally positive aging outcomes. The authors review the modifications the human brain physiologically undergoes with advancing age, from the cellular to the macro-scale level, and how these modifications are shared with neurodegenerative diseases like Alzheimer’s. The study review highlights that reducing the risk of cognitive decline should ideally mean adopting lifelong strategies and behaviors to promote brain health. The authors suggest that adopting virtuous lifestyle changes and interventions directly targeting altered brain networks, such as noninvasive brain stimulation, could counteract factors that contribute to cognitive decline, such as smoking, sleep disorders, excessive alcohol consumption, high-stress levels, social isolation, or physical inactivity. Healthy aging is a “process” achieved throughout the lifespan to ensure the best possible outcome for one’s later years, and it is never too early to start taking care of the brain.
As the global population faces a progressive shift towards a higher median age, understanding the mechanisms underlying healthy brain ageing has become of paramount importance for the preservation of cognitive abilities. The first part of the present review aims to provide a comprehensive look at the anatomical changes the healthy brain endures with advanced age, while also summarizing up to date findings on modifiable risk factors to support a healthy ageing process. Subsequently, we describe the typical cognitive profile displayed by healthy older adults, conceptualizing the well-established age-related decline as an impairment of four main cognitive factors and relating them to their neural substrate previously described; different cognitive trajectories displayed by typical Alzheimer’s Disease patients and successful agers with a high cognitive reserve are discussed. Finally, potential effective interventions and protective strategies to promote cognitive reserve and defer cognitive decline are reviewed and proposed.
CMITT will be presenting at the “NIBIB National Technology Centers Webinar Series: Molecular Imaging Technology Centers” on December 12th, 2-3:30 pm EST. Join to learn about our cutting-edge technologies and opportunities for training and collaboration!
There will be three centers presenting:
Center for Molecular Imaging Technology & Translation (CMITT), MGH, led by Dr. El Fakhri
Resource for Molecular Imaging Agents in Precision Medicine, Johns Hopkins, led by Dr. Pomper
The PET Radiotracer Translation and Resource Center (PET-RTRC), Wash U, led by Dr. Gropler
There will be a brief introduction by the Director of National Technology Centers Program, Dr. Shabestari, followed by the three talks by the center leaders and subsequent Q&A session, in which the attendees can directly ask the speakers questions. This will be live but recorded and will be made available on the NIBIB website after the event.
The webinar is open to public, but registration is required:
Demyelination is the loss or damage of the protective myelin sheath around nerve fibers, occurring in multiple sclerosis, traumatic brain and spinal cord injuries, stroke, and dementia. The Brugarolas lab within CMITT and the Gordon Center for Medical Imaging has developed a PET radiotracer, [18F]3F4AP, that targets voltage-gated potassium (K+) channels and has shown promise for imaging demyelinated lesions in animal models of neurological diseases. They have recently published results on the first in human studies, where they looked at the radioactivity distribution and estimated the radiation dose to major organs.
The imaging was well tolerated by the four subjects and there were no significant changes in vitals or adverse effects. The team found that 18F-3F4AP enters the brain and is safe for use in humans, with an acceptable level of radiation dose throughout the body.
The next step will be pursuing two initial clinical studies to investigate its value for imaging multiple sclerosis, traumatic brain injury, mild cognitive impairment, and Alzheimer’s disease.
The award was given for contributions to machine learning-based PET imaging reconstruction, image denoising and attenuation correction as well as advanced PET point-spread function modeling and novel PET system design.
In a new publication in Science Translational Medicine, the Jacobs and Johnson labs have employed longitudinal MR and PET imaging to show that the locus coeruleus is associated with features of Alzheimer’s Disease. Their work suggests that non-invasive imaging may be used to monitor cognitive decline.
The locus coeruleus, a small region in the brainstem, is known to accumulate abnormal tau proteins early in adulthood. This tau protein is one of the important causes of Alzheimer’s disease. Here, the Jacobs et al. used new MRI-methods to visualize the locus coeruleus during life and demonstrated that lower locus coeruleus integrity was associated with the initial accumulation of tau pathology, measured with positron emission tomography, and with memory decline. These findings have important implications for the early detection of Alzheimer’s disease, as they suggest that locus coeruleus MRI-measures have the potential to identify individuals who are at-risk to develop Alzheimer’s disease.