Non-linear registration improves statistical power to detect hippocampal atrophy in aging and dementia

被引:7
作者
Bartel, F. [1 ]
Visser, M. [1 ]
de Ruiter, M. [2 ]
Belderbos, J. [3 ]
Barkhof, F. [1 ,4 ,5 ]
Vrenken, H. [1 ]
de Munck, J. C. [1 ]
van Herke, M. [6 ]
机构
[1] Vrije Univ Amsterdam, Med Ctr, Dept Radiol & Nucl Med, Amsterdam, Netherlands
[2] Netherlands Canc Inst, Div Psychosocial Res & Epidemiol, Amsterdam, Netherlands
[3] Netherlands Canc Inst, Dept Radiotherapy, Amsterdam, Netherlands
[4] UCL Inst Neurol, London, England
[5] UCL Inst Healthcare Engn, London, England
[6] Univ Manchester, Manchester Acad Hlth Sci Ctr, Fac Biol Med & Hlth, Sch Med Sci,Div Canc Sci,Manchester Canc Res Ctr, Manchester, Lancs, England
基金
美国国家卫生研究院; 加拿大健康研究院;
关键词
Longitudinal MRI; MCI; AD; Hippocampal atrophy; Non-linear registration; Automatic segmentation; PROPHYLACTIC CRANIAL IRRADIATION; MILD COGNITIVE IMPAIRMENT; ADNI HARMONIZED PROTOCOL; ALZHEIMERS-DISEASE; AUTOMATED METHODS; SEGMENTATION; VOLUME; MRI; RADIATION; ROBUST;
D O I
10.1016/j.nicl.2019.101902
中图分类号
R445 [影像诊断学];
学科分类号
100207 ;
摘要
Objective: To compare the performance of different methods for determining hippocampal atrophy rates using longitudinal MRI scans in aging and Alzheimer's disease (AD). Background: Quantifying hippocampal atrophy caused by neurodegenerative diseases is important to follow the course of the disease. In dementia, the efficacy of new therapies can be partially assessed by measuring their effect on hippocampal atrophy. In radiotherapy, the quantification of radiation-induced hippocampal volume loss is of interest to quantify radiation damage. We evaluated plausibility, reproducibility and sensitivity of eight commonly used methods to determine hippocampal atrophy rates using test-retest scans. Materials and methods: Manual, FSL-FIRST, FreeSurfer, multi-atlas segmentation (MALF) and non-linear registration methods (Elastix, NiftyReg, ANTs and MIRTK) were used to determine hippocampal atrophy rates on longitudinal T1-weighted MRI from the ADNI database. Appropriate parameters for the non-linear registration methods were determined using a small training dataset (N = 16) in which two-year hippocampal atrophy was measured using WA-retest scans of 8 subjects with low and 8 subjects with high atrophy rates. On a larger dataset of 20 controls, 40 mild cognitive impairment (MCI) and 20 AD patients, one-year hippocampal atrophy rates were measured. A repeated measures ANOVA analysis was performed to determine differences between controls, MCI and AD patients. For each method we calculated effect sizes and the required sample sizes to detect one-year volume change between controls and MCI (N-CTRL_MCI) and between controls and AD (N-CTRL_MCI). Finally, reproducibility of hippocampal atrophy rates was assessed using within-session rescans and expressed as an average distance measure D-Ave, which expresses the difference in atrophy rate, averaged over all subjects. The same D-Ave was used to determine the agreement between different methods. Results: Except for MALF, all methods detected a significant group difference between CTRL and AD, but none could find a significant difference between the CTRL and MCI. FreeSurfer and MIRTK required the lowest sample sizes (FreeSurfer: N-CTRL_MCI = 115, N-CTRL_AD = 17 with D-A(ve) = 3.26%; MIRTK: N-CTRL_MCI = 97, N-CTRL_AD = 11 with D-A(ve) = 3.76%), while ANTs was most reproducible (N-CTRL_MCI = 162, N-CTRL_AD = 37 with D-Ave = 1.06%), followed by Elastix(N-CTRL_MCI = 226, N-CTRL_AD = 15 with D-A(ve) = 1.78%) and NiftyReg (N-CTRL_MCI = 193, N-CTRL_AD = 14 with D-A(ve) = 2.11%). Manually measured hippocampal atrophy rates required largest sample sizes to detect volume change and were poorly reproduced (N-CTRL_MCI = 452, N-CTRL_AD = 87 with D-A(ve) = 12.39%). Atrophy rates of non-linear registration methods also agreed best with each other. Discussion and conclusion: Non-linear registration methods were most consistent in determining hippocampal atrophy and because of their better reproducibility, methods, such as ANTs, Elastix and NiftyReg, are preferred for determining hippocampal atrophy rates on longitudinal MRI. Since performances of non-linear registration methods are well comparable, the preferred method would mostly depend on computational efficiency.
引用
收藏
页数:11
相关论文
共 67 条
  • [1] Artificial Acousto-Magnetic Soft Microswimmers
    Ahmed, Daniel
    Dillinger, Cornel
    Hong, Ayoung
    Nelson, Bradley J.
    [J]. ADVANCED MATERIALS TECHNOLOGIES, 2017, 2 (07):
  • [2] The diagnosis of mild cognitive impairment due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease
    Albert, Marilyn S.
    DeKosky, Steven T.
    Dickson, Dennis
    Dubois, Bruno
    Feldman, Howard H.
    Fox, Nick C.
    Gamst, Anthony
    Holtzman, David M.
    Jagust, William J.
    Petersen, Ronald C.
    Snyder, Peter J.
    Carrillo, Maria C.
    Thies, Bill
    Phelps, Creighton H.
    [J]. ALZHEIMERS & DEMENTIA, 2011, 7 (03) : 270 - 279
  • [3] [Anonymous], 1987, PROC INT C COMPUT GR, DOI [10.1145/37401.37422, DOI 10.1145/37402.37422, DOI 10.1145/37401.37422]
  • [4] [Anonymous], 2007, Bayesian Statistical Models of Shape and Appearance for Subcortical Brain Segmentation
  • [5] Mapping progressive brain structural changes in early Alzheimer's disease and mild cognitive impairment
    Apostolova, Liana G.
    Thompson, Paul M.
    [J]. NEUROPSYCHOLOGIA, 2008, 46 (06) : 1597 - 1612
  • [6] Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain
    Avants, B. B.
    Epstein, C. L.
    Grossman, M.
    Gee, J. C.
    [J]. MEDICAL IMAGE ANALYSIS, 2008, 12 (01) : 26 - 41
  • [7] The Insight ToolKit image registration framework
    Avants, Brian B.
    Tustison, Nicholas J.
    Stauffer, Michael
    Song, Gang
    Wu, Baohua
    Gee, James C.
    [J]. FRONTIERS IN NEUROINFORMATICS, 2014, 8
  • [8] Regional analysis of volumes and reproducibilities of automatic and manual hippocampal segmentations
    Bartel, Fabian
    Vrenken, Hugo
    Bijma, Fetsje
    Barkhof, Frederik
    van Herk, Marcel
    de Munck, Jan C.
    [J]. PLOS ONE, 2017, 12 (02):
  • [9] Boccardi M, 2011, ADV ALZH DIS, V2, P111, DOI 10.3233/978-1-60750-793-2-111
  • [10] Delphi definition of the EADC-ADNI Harmonized Protocol for hippocampal segmentation on magnetic resonance
    Boccardi, Marina
    Bocchetta, Martina
    Apostolova, Liana G.
    Barnes, Josephine
    Bartzokis, George
    Corbetta, Gabriele
    DeCarli, Charles
    deToledo-Morrell, Leyla
    Firbank, Michael
    Ganzola, Rossana
    Gerritsen, Lotte
    Henneman, Wouter
    Killiany, Ronald J.
    Malykhin, Nikolai
    Pasqualetti, Patrizio
    Pruessner, Jens C.
    Redolfi, Alberto
    Robitaille, Nicolas
    Soininen, Hilkka
    Tolomeo, Daniele
    Wang, Lei
    Watson, Craig
    Wolf, Henrike
    Duvernoy, Henri
    Duchesne, Simon
    Jack, Clifford R., Jr.
    Frisoni, Giovanni B.
    [J]. ALZHEIMERS & DEMENTIA, 2015, 11 (02) : 126 - 138