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Improving PET Quantitation with Denoising, Motion Compensation, and Deblurring


This article was published in the Nuclear & Plasma Sciences Society newsletter of September 2017

Positron emission tomography (PET) enables 3D visualization of vital physiological information, e.g., metabolism, blood flow, and neuroreceptor concentration by using targeted radioisotope-labeled tracers. Quantitative interpretation of PET images is crucial both in diagnostic and therapeutic contexts. As a result of its unique functional capabilities, PET imaging plays a pivotal role in diagnostics and in therapeutic assessment in many areas of medicine, including oncology, neurology, and cardiology. Accurate quantitation requires correction of PET raw data and/or images for a number of physical effects. These include attenuation correction, randoms and scatter correction, subject motion correction, and partial volume correction. We have developed a range of techniques that address the PET denoising, motion compensation, deblurring problems. Several of these methods greatly enhance the quantitative capabilities of PET particularly by incorporating information from an anatomical imaging modality such as magnetic resonance imaging (MRI).

Faced with a fundamental tradeoff between radiation dose and image noise, PET data is inherently noisy. The high levels of noise in PET images pose a challenge to accurate quantitation. This issue is particularly well pronounced at the early time frames of dynamic PET images, which are usually short to capture rapid changes in tracer uptake patterns. In order to improve image quality and quantitative accuracy, statistical image reconstruction algorithms model the Poisson characteristics of PET data and employ numerical optimization algorithms to solve the corresponding optimization problem [1, 2]. Common reconstruction procedures, such as ordered subsets expectation maximization, are therefore routinely followed by a post-filtering step for denoising the reconstructed image. A range of strategies have been proposed for post-reconstruction denoising of both static and dynamic PET images [3, 4]. In recent years, image denoising based on non-local means (NLM) has become popular [5]. Unlike conventional neighborhood filters, which use local similarities, in this technique, the search for voxels similar to a given voxel is no longer restricted to its immediate vicinity.

Full article in PDF

CRA Certificate Recipient


Congratulations to Christina Shambaugh who was just awarded the title Certified Research Administrator (CRA) by the Research Administrator's Certification Council.

The CRA designation is awarded to individuals who met the eligibility requirements of the Research Administrators Certification Council's and have demonstrated a level of knowledge necessary for a person to be a sponsored research administrator.

Christina has approximately 15 years of professional experience in research administration.

Christina Shambaugh BA, CRA

Seminar: A Projected Filter Algorithm for Dynamic SPECT


Dr. Youssef Qranfal has served as Professor of Applied Mathematics at Wentworth Institute of Technology in Boston, Massachusetts since September 2015. His recent research focuses on optimization, operation research, statistics, and their applications. Prior to starting his career at WIT, Dr. Qranfal has worked in industry as an engineer in applied mathematics and computer science. He has authored many technical papers on applied mathematics to various fields such as medical imaging. They have been published in peer-reviewed journals, presented at technical conferences, and appeared in the proceedings of those conferences.

Images and visualization have become increasingly important in many areas of science and technology. Advances in hardware and software have allowed computerized image processing to become a standard tool in many scientific applications, including medical imaging. In this talk, Dr. Qranfal demonstrated how he models and solves the inverse problem of reconstructing a dynamic medical image where the signal strength changes substantially over the time required for data acquisition. His group uses a stochastic approach based on a Markov process to model the problem. Dr. Qranfal and his collaborators introduced a novel proximal approach and applied it during the Kalman filter algorithm to ensure positivity and spatial regularization. They have tested their method for the case of image reconstruction in time-dependent single photon emission computed tomography (SPECT). According to Dr. Qranfal, numerical results corroborate the effectiveness of his approach.

Prof. Qranfal discusses reconstruction of time-varying SPECT images

Seminar: High-Resolution MR Elastography of the Human Hippocampus


Curtis L. Johnson, PhD, is an Assistant Professor in the Department of Biomedical Engineering and the Department of Psychological and Brain Sciences at the University of Delaware. He received his PhD in Mechanical Engineering in 2013 from the University of Illinois at Urbana-Champaign where he worked to develop techniques for magnetic resonance elastography (MRE). His research is in high-resolution MRE for assessing the structure, function, and health of the human brain for applications in neurology, neurosurgery, and neuroscience. He is a Junior Fellow of the International Society for Magnetic Resonance in Medicine (ISMRM) and serves as the Secretary of the ISMRM MR Elastography Study Group.

Magnetic resonance elastography (MRE) is an emerging technique for noninvasively characterizing the quantitative mechanical properties of tissues in vivo. These mechanical properties are highly sensitive to the structural integrity of tissue, and MRE has shown promise in diagnosing and staging diseases of many organs, including liver, breast, heart, and brain.

However, the ability to accurately characterize specific neuroanatomical structures has been limited by poor spatial resolution and the need for high signal-to-noise ratio in reasonable scan times. Specialized approaches to obtaining high-resolution brain MRE data are needed to improve the sensitivity and specificity of mechanical property measures.

In this talk Dr. Curtis Johnson discussed the work of his group in (1) high-resolution MRE techniques to target the hippocampus and (2) applications of hippocampal MRE in characterizing neurological conditions and the structural contributions to memory performance.

Dr. Curtis Johnson discussing his work using magnetic resonance elastography to characterize the mechanical properties of tissue

Seminar: Research Agreement Types

As a Senior Agreement Advisor and Contracts Team Lead, Rebecca Dufur, J.D., M.A, drafts, reviews, and negotiates research agreements from state and federal government agencies, academic institutions, public and private foundations, and other non-profit organizations. She worked in clinical research for over 12 years, during which time she attended Suffolk University Law School as an evening student. Rebecca has been with Partners Research Management since 2014.

There are several types of agreements associated with research. They vary according to factors such as the the nature of the research and the funding source.

Rebecca Dufur discussed the fundamental differences between grants and contracts, and both funded and unfunded research agreement types including subcontracts, professional services agreements (PSAs), memorandum of understanding (MOUs), confidential disclosure agreement (CDAs), data use agreements (DUAs) and other research support agreements.

Rebecca Dufur discusses types of research agreements

2017 Gordon Science Symposium Featuring Dr. Rudolph Tanzi


Dr. Rudolph Tanzi is a Professor of Neurology at Harvard Medical School and and the Director of the Genetics and Aging Research Unit at Massachusetts General Hospital (MGH). He co-discovered three of the first Alzheimer’s disease genes and has identified several others in the Alzheimer’s Genome Project, which he directs. Dr. Tanzi was the keynote speaker of the 2017 Gordon Science Symposium and its annual David Elmaleh Lecture. Below is the summary of his inaugural address titled “Alzheimer’s disease: a story of genes, glia, and germs”.

Dr. Rudolph Tanzi delivering the Inaugural Elmaleh Lecture of the 2017 Gordon Science Symposium

Alzheimer’s disease (AD) is the most common form of dementia affecting the elderly and is characterized by global cognitive decline. AD is strongly influenced by both genetic factors and lifestyle. While certain rare gene mutations, e.g. in the APP, PSEN1 and PSEN2 genes guarantee onset of AD before 60 years old, most cases of AD (>97%) involve genetic susceptibility factors, e.g. APOE, and lifestyle, e.g. diet, exercise, sleep, intellectual and social engagement, stress levels, and brain trauma. Most recently we have found that low-grade infections, e.g. bacterial, fungal, viral, in the brain may also play a role by rapidly nucleating beta-amyloid deposition as an antimicrobial protection response of the brain's innate immune system. Genetic susceptibility factors have been elucidated over the past decade using genome-wide association studies (GWAS) and more recently by follow up with whole genome sequencing (WGS) and whole exome sequencing (WES). We are now carrying out GWAS using approximately 50 million single nucleotide variants (SNV) from WGS and WES (whole genome sequencing association studies; WGSAS). As AD-linked/associated functional SNVs are identified in these studies, they are being tested in our 3D human stem cell-derived neural culture models of AD, in which we have shown beta-amyloid directly drives tangle formation. Many of the more recently identified AD genes are involved in innate immunity, e.g. CD33, which we first reported in our family-based GWAS in 2008 (along with ADAM10 and ATXN1). To study CD33 and other innate immune-related AD genes, we have incorporated microglia into our 3D neural cultures while also utilizing classic transgenic mouse models.

Accelerating Data Acquisition for Anatomical, Physiological, and Functional MRI


Magnetic resonance imaging (MRI) provides various methods for imaging anatomical, physiological, and functional information of our body noninvasively. In a conference organized by the Gordon Center, Dr. Sung-Hong Park, from the South Korean university of KAIST, discussed the latest imaging modalities for acceleration of data acquisition in terms of pulse sequences and image reconstructions. These modalities include (i) acquisition of time-of-flight MR angiogram and blood oxygenation level dependent (BOLD) MR venogram, (ii) application of compressed sensing to arterial spin labeling, a non-invasive perfusion MRI technqiue, (iii) simultaneous acquisiton of blood perfusion and magnetization transfer (MT) with 2D inter-slice blood flow and MT effects, and (iv) acceleration of functional MRI with compressed sensing.

Dr. Sung-Hong Park is Associate Professor at the Department of Bio & Brain Engineering at the Korea Advanced Institute of Science and Technology (KAIST). He was the guest speaker at a lecture organized by the MGH Gordon Center.

Congratulations Daniel Yokell, Robert E. Henkin Government Relations Fellowship Recipient


Daniel L. Yokell, PharmD, RPh, associate director, Radioactive Drug Regulatory Affairs in the Gordon Center for Medical Imaging and manager of PET Production Chemistry & PET Nuclear Pharmacy Services, has received the 2017 Robert E. Henkin Government Relations Fellowship from the Society of Nuclear Medicine and Molecular Imaging (SNMMI) and Education Research Foundation for Nuclear Medicine and Molecular Imaging. The fellows spend a week with SNMMI staff, visiting Congress, federal agencies and other medical societies where they learn first-hand how the federal legislative and regulatory process impacts nuclear medicine and molecular imaging. Dr. Yokell hopes to use the fellowship to enhance his knowledge of regulatory and government affairs issues to educate stakeholders on the essential role nuclear medicine and molecular imaging plays in patient care.

Daniel Yokell working at the PET production facility of the MGH Gordon Center