Characterizing spatiotemporal progression and prediction of infarct lesion volumes in experimental acute ischemia using quantitative perfusion and diffusion imaging

Pixel-by-pixel spatiotemporal progression of focal ischemia (permanent occlusion) in rats was investigated using quantitative perfusion and diffusion magnetic resonance imaging every 30 minutes for 3 hours. The normal left-hemisphere apparent diffusion coefficient (ADC) was 0.76 ± 0.03 × 10−3 mm2/s and CBF was 0.7 ± 0.3 mL · g−1 · min−1 (mean ± SD, n = 5). The ADC and CBF viability thresholds yielding the lesion volumes (LV) at 3 hours that best approximated the 2,3,5-triphenyltetrazolium chloride (TTC) infarct volumes (200 ± 30 mm2) at 24 hours were 0.53 ± 0.02 × 10−3 mm2/s (30% ± 2% reduction) and 0.30 ± 0.09 mL · g−1 · min−1 (57% ±11% reduction), respectively. Temporal evolution of the ADC- and CBF-defined LV showed a significant “perfusion-diffusion mismatch” up to 2 hours ( P < 0.05, n = 11), a potential therapeutic window. Based on the viability thresholds, three pixel clusters were identified on the CBF-ADC scatterplots: (1) a “normal” cluster with normal CBF and ADC, (2) an “ischemic core” cluster with markedly reduced CBF and ADC, and (3) a “mismatch” cluster with reduced CBF but slightly reduced ADC. These clusters were color-coded and mapped onto the image and CBF-ADC spaces. Lesions grew peripheral and medial to the initial ADC abnormality. In contrast to the CBF distribution, the ADC distribution in the ischemic hemisphere was bimodal; the relatively time-invariant bimodal-ADC minima were 0.57 ± 0.02 × 10−3 mm2/s (corresponding CBF 0.35 ± 0.04 mL · g−1 · min−1), surprisingly similar to the TTC-derived thresholds. Together, these results illustrate an analysis approach to systemically track the pixel-by-pixel spatiotemporal progression of acute ischemic brain injury.

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Partial-volume effect on ischemic tissue-fate delineation using quantitative perfusion and diffusion imaging on a rat stroke model

Magnetic Resonance in Medicine ◽

10.1002/mrm.20299 ◽

2004 ◽

Vol 52 (6) ◽

pp. 1328-1335 ◽

Cited By ~ 6

Author(s):

Hongxia Ren ◽

Qiang Shen ◽

Juergen Bardutzky ◽

Marc Fisher ◽

Timothy Q. Duong

Keyword(s):

Diffusion Imaging ◽

Volume Effect ◽

Partial Volume Effect ◽

Stroke Model ◽

Partial Volume ◽

Quantitative Perfusion ◽

Ischemic Tissue ◽

And Diffusion

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MESMERISED: Super-accelerating T1 relaxometry and diffusion MRI with STEAM at 7 T for quantitative multi-contrast and diffusion imaging

NeuroImage ◽

10.1016/j.neuroimage.2021.118285 ◽

2021 ◽

pp. 118285

Author(s):

F.J. Fritz ◽

B.A. Poser ◽

A. Roebroeck

Keyword(s):

Diffusion Mri ◽

Diffusion Imaging ◽

And Diffusion ◽

T1 Relaxometry

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Classification of first-episode psychosis: a multi-modal multi-feature approach integrating structural and diffusion imaging

Journal of Neural Transmission ◽

10.1007/s00702-014-1324-x ◽

2014 ◽

Vol 122 (6) ◽

pp. 897-905 ◽

Cited By ~ 15

Author(s):

Denis Peruzzo ◽

◽

Umberto Castellani ◽

Cinzia Perlini ◽

Marcella Bellani ◽

...

Keyword(s):

Diffusion Imaging ◽

First Episode Psychosis ◽

First Episode ◽

Episode Psychosis ◽

And Diffusion

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MR perfusion and diffusion imaging in the follow-up of recurrent glioblastoma treated with dendritic cell immunotherapy: a pilot study

Neuroradiology ◽

10.1007/s00234-010-0802-6 ◽

2010 ◽

Vol 53 (10) ◽

pp. 721-731 ◽

Cited By ~ 45

Author(s):

Matej Vrabec ◽

Sofie Van Cauter ◽

Uwe Himmelreich ◽

Stefaan W. Van Gool ◽

Stefan Sunaert ◽

...

Keyword(s):

Dendritic Cell ◽

Pilot Study ◽

Diffusion Imaging ◽

Recurrent Glioblastoma ◽

Mr Perfusion ◽

Cell Immunotherapy ◽

And Diffusion

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Utilising functional imaging to predict survival in paediatric brain tumours.

Neuro-Oncology ◽

10.1093/neuonc/noz167.018 ◽

2019 ◽

Vol 21 (Supplement_4) ◽

pp. iv5-iv5

Author(s):

James Grist ◽

Stephanie Withey ◽

Lesley MacPherson ◽

Adam Oates ◽

Mr Stephen Powell ◽

...

Keyword(s):

Functional Imaging ◽

Brain Tumours ◽

Diffusion Imaging ◽

Supervised Machine Learning ◽

Imaging Features ◽

Tumour Grade ◽

Survival Status ◽

Significant Difference ◽

Paediatric Brain Tumours ◽

And Diffusion

Abstract Introduction Brain tumours are a common cause of death in the paediatric population. We have previously shown that MR imaging and spectroscopy can be used to non-invasively differentiate between tumour types. Here, we demonstrate that functional imaging can be highly predictive of survival and grade in a paediatric cohort. Methods Perfusion (PWI) and diffusion weighted imaging (DWI) were performed in a multi-site (Birmingham Children’s Hospital, Royal Victoria Infirmary, Alder Hey, Nottingham) cohort ([grade, 5-year survival alive:dead number] = [I,15:1],[II, 5:1],[III,2:3],[IV,8:11]). ROIs were drawn on T2 imaging and functional imaging features (mean, standard deviation, skewness, and kurtosis) were derived. Supervised machine learning was used to predict 5-year survival and tumour grade from features. ANOVA and post-hoc tests were used to assess differences in features between grade and 5-year survival status. Results 5-year survival was predicted with 89%, 85%, and 87% accuracy with all imaging, perfusion, or diffusion features, respectively. A significant difference in perfusion was found between surviving and diseased participants (1.71 ± 0.82 vs 2.62 ± 1 mL/100g/min, respectively, p < 0.05). A significant difference in ADC (mm2 s-1) between tumour grades was found (1 vs 4 (1533 ± 458 vs 857 ± 239), 4 vs 3 (857 ± 239 vs 1197 ± 137), 4 vs 2 (857 ± 239 vs 1440 ± 557), corrected p < 0.05). Conclusion We have shown that perfusion and diffusion imaging features can be used to non-invasively assess tumour grade and estimate 5-year survival status in a cohort of paediatric brain tumours.

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