Department of Radiology and Imaging Sciences Works

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Evaluation of 11C-Acetate and 18 F-FDG PET/CT in mouse multidrug resistance gene-2 deficient mouse model of hepatocellular carcinoma
(BioMed Central, 2015-05) Territo, Paul R.; Maluccio, Mary; Riley, Amanda A.; McCarthy, Brian P.; Fletcher, James; Tann, Mark; Saxena, Romil; Skill, Nicholas J.; Department of Radiology and Imaging, IU School of Medicine
Background Hepatocellular carcinoma (HCC) remains a global health problem with unique diagnostic and therapeutic challenges, including difficulties in identifying the highest risk patients. Previous work from our lab has established the murine multidrug resistance-2 mouse (MDR2) model of HCC as a reasonable preclinical model that parallels the changes seen in human inflammatory associated HCC. The purpose of this study is to evaluate modalities of PET/CT in MDR2−/− mice in order to facilitate therapeutic translational studies from bench to bedside. Methods 18F-FDG and 11C-acetate PET/CT was performed on 12 m MDR2−/− mice (n = 3/tracer) with HCC and 12 m MDR2−/+ control mice (n = 3/tracer) without HCC. To compare PET/CT to biological markers of HCC and cellular function, serum alpha-fetoprotein (AFP), lysophosphatidic acid (LPA), cAMP and hepatic tumor necrosis factor α (TNFα) were quantified in 3-12 m MDR2−/− (n = 10) mice using commercially available ELISA analysis. To translate results in mice to patients 11C-acetate PET/CT was also performed in 8 patents suspected of HCC recurrence following treatment and currently on the liver transplant wait list. Results Hepatic18F-FDG metabolism was not significantly increased in MDR2−/− mice. In contrast, hepatic 11C-acetate metabolism was significantly elevated in MDR2−/− mice when compared to MDR2−/+ controls. Serum AFP and LPA levels increased in MDR2−/− mice contemporaneous with the emergence of HCC. This was accompanied by a significant decrease in serum cAMP levels and an increase in hepatic TNFα. In patients suspected of HCC recurrence there were 5 true positives, 2 true negatives and 1 suspected false 11C-acetate negative. Conclusions Hepatic 11C-acetate PET/CT tracks well with HCC in MDR2−/− mice and patients with underlying liver disease. Consequently 11C-acetate PET/CT is well suited to study 1) HCC emergence/progression in patients and 2) reduce animal numbers required to study new chemotherapeutics in murine models of HCC.
Fat suppression techniques in breast magnetic resonance imaging: a critical comparison and state of the art
(Dovepress, 2015) Lin, Chen; Rogers, Clark David; Majidi, Shadie; Department of Radiology and Imaging, IU School of Medicine
Robust and accurate fat suppression is highly desirable in breast magnetic resonance imaging (MRI) because it can considerably improve the image quality and lesion conspicuity. However, fat suppression is also more challenging in the breast compared with other regions in the body. Technical advances have been made over time to make fat suppression more efficient and reliable. Combined with other innovations, breast MRI continues to be the most sensitive and comprehensive diagnostic modality in the detection and evaluation of breast lesions. This review offers a critical comparison of various fat suppression techniques in breast MRI including spectral-selective excitation and saturation techniques based on the chemical shift difference between fat and water, the inversion recovery techniques based on the T1 relaxation time difference, the hybrid spectral-selective inversion recovery techniques, and the new Dixon fat and water separation techniques based on the phase difference between fat and water signal at different echo times. This review will also cover less frequently used techniques such as slice-selective gradient reversal. For each fat suppression technique in breast MRI, a detailed explanation of the technical principle, the advantages and disadvantages, the approaches for optimization as well as the clinical examples are included. The additional challenges of fat suppression in breast MRI at higher field strength and in the presence of metallic and silicone implants are also discussed.
PARP1 gene variation and microglial activity on [11C]PBR28 PET in older adults at risk for Alzheimer's disease
(Springer, 2013) Kim, Sungeun; Nho, Kwangsik; Risacher, Shannon L.; Inlow, Mark; Swaminathan, Shanker; Yoder, Karmen K.; Shen, Li; West, John D.; McDonald, Brenna C.; Tallman, Eileen F.; Hutchins, Gary D.; Fletcher, James W.; Farlow, Martin R.; Ghetti, Bernardino; Saykin, Andrew J.; Radiology and Imaging Sciences, School of Medicine
Increasing evidence suggests that inflammation is one pathophysio-logical mechanism in Alzheimer's disease (AD). Recent studies have identified an association between the poly (ADP-ribose) polymerase 1 (PARP1) gene and AD. This gene encodes a protein that is involved in many biological functions, including DNA repair and chromatin remodeling, and is a mediator of inflammation. Therefore, we performed a targeted genetic association analysis to investigate the relationship between the PARP1 polymorphisms and brain micro-glial activity as indexed by [11C]PBR28 positron emission tomography (PET). Participants were 26 non-Hispanic Caucasians in the Indiana Memory and Aging Study (IMAS). PET data were intensity-normalized by injected dose/total body weight. Average PBR standardized uptake values (SUV) from 6 bilateral regions of interest (thalamus, frontal, parietal, temporal, and cingulate cortices, and whole brain gray matter) were used as endophenotypes. Single nucleotide polymorphisms (SNPs) with 20% minor allele frequency that were within +/− 20 kb of the PARP1 gene were included in the analyses. Gene-level association analyses were performed using a dominant genetic model with translocator protein (18-kDa) (TSPO) genotype, age at PET scan, and gender as covariates. Analyses were performed with and without APOE ε4 status as a covariate. Associations with PBR SUVs from thalamus and cingulate were significant at corrected p<0.014 and <0.065, respectively. Subsequent multi-marker analysis with cingulate PBR SUV showed that individuals with the “C” allele at rs6677172 and “A” allele at rs61835377 had higher PBR SUV than individuals without these alleles (corrected P<0.03), and individuals with the “G” allele at rs6677172 and “G” allele at rs61835377 displayed the opposite trend (corrected P<0.065). A previous study with the same cohort showed an inverse relationship between PBR SUV and brain atrophy at a follow-up visit, suggesting possible protective effect of microglial activity against cortical atrophy. Interestingly, all 6 AD and 2 of 3 LMCI participants in the current analysis had one or more copies of the “GG” allele combination, associated with lower cingulate PBR SUV, suggesting that this gene variant warrants further investigation.
A graph-based integration of multimodal brain imaging data for the detection of early mild cognitive impairment (E-MCI)
(Springer, 2013) Kim, Dokyoon; Kim, Sungeun; Risacher, Shannon L.; Shen, Li; Ritchie, Marylyn D.; Weiner, Michael W.; Saykin, Andrew J.; Nho, Kwangsik; Radiology and Imaging Sciences, School of Medicine
Alzheimer's disease (AD) is the most common cause of dementia in older adults. By the time an individual has been diagnosed with AD, it may be too late for potential disease modifying therapy to strongly influence outcome. Therefore, it is critical to develop better diagnostic tools that can recognize AD at early symptomatic and especially pre-symptomatic stages. Mild cognitive impairment (MCI), introduced to describe a prodromal stage of AD, is presently classified into early and late stages (E-MCI, L-MCI) based on severity. Using a graph-based semi-supervised learning (SSL) method to integrate multimodal brain imaging data and select valid imaging-based predictors for optimizing prediction accuracy, we developed a model to differentiate E-MCI from healthy controls (HC) for early detection of AD. Multimodal brain imaging scans (MRI and PET) of 174 E-MCI and 98 HC participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were used in this analysis. Mean targeted region-of-interest (ROI) values extracted from structural MRI (voxel-based morphometry (VBM) and FreeSurfer V5) and PET (FDG and Florbetapir) scans were used as features. Our results show that the graph-based SSL classifiers outperformed support vector machines for this task and the best performance was obtained with 66.8% cross-validated AUC (area under the ROC curve) when FDG and FreeSurfer datasets were integrated. Valid imaging-based phenotypes selected from our approach included ROI values extracted from temporal lobe, hippocampus, and amygdala. Employing a graph-based SSL approach with multimodal brain imaging data appears to have substantial potential for detecting E-MCI for early detection of prodromal AD warranting further investigation.
Neuroimaging biomarkers of neurodegenerative diseases and dementia
(Thieme, 2013-09) Risacher, Shannon L.; Saykin, Andrew J.; Radiology and Imaging Sciences, School of Medicine
Neurodegenerative disorders leading to dementia are common diseases that affect many older and some young adults. Neuroimaging methods are important tools for assessing and monitoring pathological brain changes associated with progressive neurodegenerative conditions. In this review, the authors describe key findings from neuroimaging studies (magnetic resonance imaging and radionucleotide imaging) in neurodegenerative disorders, including Alzheimer's disease (AD) and prodromal stages, familial and atypical AD syndromes, frontotemporal dementia, amyotrophic lateral sclerosis with and without dementia, Parkinson's disease with and without dementia, dementia with Lewy bodies, Huntington's disease, multiple sclerosis, HIV-associated neurocognitive disorder, and prion protein associated diseases (i.e., Creutzfeldt-Jakob disease). The authors focus on neuroimaging findings of in vivo pathology in these disorders, as well as the potential for neuroimaging to provide useful information for differential diagnosis of neurodegenerative disorders.
ALARA, Image Gently and CT-induced cancer
(Springer, 2015-04) Cohen, Mervyn D.; Department of Radiology and Imaging, IU School of Medicine
Reply
(Wiley, 2015-09) Nho, Kwangsik; Saykin, Andrew J.; Department of Radiology and Imaging Sciences, School of Medicine
Marx on Radiology's Future
(Elsevier, 2015-05) Gunderman, Richard B.; Department of Radiology and Imaging Sciences, School of Medicine
Education in Professionalism: Leonard Berlin, MD
(Elsevier, 2015-06) Gunderman, Richard B.; Department of Radiology and Imaging Sciences, School of Medicine
The Radiologist as an Anatomy Student
(Elsevier, 2015-07) Gunderman, Richard B.; Bedi, Harprit; Department of Radiology and Imaging Sciences, School of Medicine

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