PRECLINICAL AND CLINICAL IMAGING - Rapid Communication Diffusion Kurtosis Imaging to Detect Amyloidosis in an APP/PS1 Mouse Model for Alzheimer’s Disease Greetje Vanhoutte, 1 Sandra Pereson, 2,3 Rafael Delgado y Palacios, 1 Pieter-Jan Guns, 1,4 Bob Asselbergh, 2 Jelle Veraart, 5 Jan Sijbers, 5 Marleen Verhoye, 1 Christine Van Broeckhoven, 2,3 and Annemie Van der Linden 1 * Purpose: Amyloid deposition in the brain is considered an ini- tial event in the progression of Alzheimer’s disease. We hypothesized that the presence of amyloid plaques in the brain of APP/presenilin 1 mice leads to higher diffusion kurtosis measures due to increased microstructural complexity. As such, our purpose was to provide an in vivo proof of principle for detection of amyloidosis by diffusion kurtosis imaging (DKI). Methods: APP KM670/671NL /presenilin 1 L166P mice (n ¼ 5) and wild-type littermates (n ¼ 5) underwent DKI at the age of 16 months. Averaged diffusion and diffusion kurtosis parameters were obtained for multiple regions (hippocampus–cortex–thala- mus–cerebellum). After DKI, mice were sacrificed for amyloid staining. Results: Histograms of the frequency distribution of the DKI parameters tended to shift to higher values. After normaliza- tion of absolute values to the cerebellum, a nearly plaque-free region, mean, radial, and axial diffusion kurtosis were signifi- cantly higher in APP/presenilin 1 mice as compared to wild- type in the cortex and thalamus, regions demonstrating sub- stantial amyloid staining. Conclusion: The current study, although small-scale, suggests increased DKI metrics, in the absence of alterations in diffu- sion tensor imaging metrics in the cortex and thalamus of APP/presenilin 1 mice with established amyloidosis. These results warrant further investigations on the potential of DKI as a sensitive marker for Alzheimer’s disease. Magn Reson Med 69:1115–1121, 2013. V C 2013 Wiley Periodicals, Inc. Key words: diffusion kurtosis imaging; mouse model; Alzhei- mer’s disease The complex progression of Alzheimer’s disease (AD) involves interaction of different pathological cascades, including accumulation of amyloid-b (Ab) and hyper- phosphorylation of tau protein leading to the formation of Ab-plaques and intracellular neurofibrillary tangles respectively. Together with associated processes such as inflammation and oxidative stress, these cascades con- tribute to loss of synaptic integrity and progressive neu- rodegeneration, eventually resulting in cognitive deficits in patients. In 2010, worldwide 36 million people suffered from dementia and the majority have AD, a striking figure that is predicted to almost quadruple by 2050 (1–3). Despite huge investments of resources to develop and validate clinical biomarkers of AD, potential biomarkers all have their shortcomings and none is adequate enough for effective diagnosis of AD. Moreover, postmortem histopathological analysis demonstrating Ab-plaques in the brain remains the gold standard for accurate and final diagnosis of AD. As Ab accumulation and the subsequent formation of Ab-plaques starts at the early phase of the disease (4) many of the biomarker approaches are based on the early detection of Ab-peptides and/or Ab-plaques. Detection of Ab 40 -, Ab 42 -peptides and their respective ratio in the cerebral spinal fluid has been proposed for clinical diagnosis of AD. Indeed, it has been shown that the cerebral spinal fluid concentration of Ab 42 —the main constituent of Ab-plaques—decreases upon devel- opment of brain plaques. The down side of this approach is the invasive nature of the cerebral spinal fluid collec- tion procedure (5). In addition, various neuro-imaging tools have been proposed for noninvasive monitoring of Ab-load. Vari- ous radio-tracers have been developed and claim to selectively bind to Ab-plaques ( 11 C-PiB-PET (6,7), 18 F- florbetapir (Amyvid V R )-PET (8,9), 18 F-florbetaben (BAY 949172-PET) (10)). Despite these recent advances in PET-imaging of AD, most radioligands are not ideal for quantification due to low signal-to-noise ratio, nonspe- cific binding or unfavorable kinetics, and radiation burden. 1 Department of Biomedical Sciences, Bio-Imaging Lab, University of Ant- werp, Antwerp, Belgium. 2 Department of Molecular Genetics, VIB, Antwerp, Belgium. 3 Laboratory of Neurogenetics, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium. 4 Expert Group Antwerp Molecular Imaging (EGAMI), University of Antwerp, Antwerp, Belgium. 5 Department of Physics, IMinds-Vision Lab, University of Antwerp, Antwerp, Belgium. Grant sponsor: Interuniversity Attraction Poles program of the Belgian Science Policy Office, Methusalem Excellence program of the Flemish Government, Research Foundation Flanders (FWO), and Agency for Innova- tion by Science and Technology (IWT), Belgium; Grant sponsor: EC-FP7 project NAD; Grant number: CP-IP 212043-2; Grant sponsor: European Unionhx0027;s Seventh Framework Programme (FP7/2007-2013); Grant number: HEALTH-F2-2011-278850 (INMiND); Grant sponsor: Interuniversity Attraction Poles Programme (IUAP 7/11) initiated by the Belgian Science Policy Office. *Corresponding to: Vanhoutte Greetje, Universiteit Antwerpen, Bio-Imaging Lab, Universiteitsplein,1, Campus Drie Eiken, Building UC, 2610 Antwerpen, Belgium. E-mail: Greetje.Vanhoutte@ua.ac.be Received 18 October 2012; revised 21 December 2012; accepted 14 January 2013 DOI 10.1002/mrm.24680 Published online 11 March 2013 in Wiley Online Library (wileyonlinelibrary. com). Magnetic Resonance in Medicine 69:1115–1121 (2013) V C 2013 Wiley Periodicals, Inc. 1115