Ben Jordan1, Tom Roberts1, Angela D'Esposito1, John Connell1, Andrada Ianus2, Eleftheria Panagiotaki2, Daniel Alexander2, Mark Lythgoe1, and Simon Walker-Samuel1
1Centre for Advanced Biomedical Imaging, University College London, London, United Kingdom, 2Centre for Medical Image Computing, University College London, London, United Kingdom
Synopsis
It has previously been shown that compartmental models
of tissue diffusion such as VERDICT can enable access to useful measures of
in-vivo tumour microstructure such as cell radius. However, comparing the
in-vivo values with those measured from histology showed that a discrepancy
exists between the two; histological values were consistently smaller. In this
study, we assess the ability of VERDICT MRI to detect this change in cell
radius by acquiring data (9.4T MRI) both in-vivo and post-fixation. A
significant decrease in cell radius was detected post-fixation, which was supported
by a decrease in the intra-cellular volume fraction.Purpose
The
purpose of this study was to evaluate the ability of the VERDICT MRI model of
compartmental diffusion to quantify tumour microstructure and use it to assess
the effects of tissue fixation. Previously it has been demonstrated that the
VERDICT model can be successfully used in-vivo
to access histologic features such as cell size, vascular volume fraction,
intra- and extracellular volume fractions and microvascular pseudo-diffusivity
1.
However, the gold standard cell diameter values attained through histological
analysis were on average 12.3% smaller than the VERDICT measurements. This
discrepancy was attributed to the shrinkage of cells during the fixation
process. In this study both in-vivo
and ex-vivo data acquired from the
same subjects are compared to ascertain whether the expected cell shrinkage can
be detected in the VERDICT estimate of cell size.
Methods
Animal Model: The LS174T human colon adenocarcinoma cell line was
injected subcutaneously at a concentration of 5x106 cells per 100ml of serum-free media (~106 cells per
animal) into 5 female MF1 nu/nu mice (age ~6wks)2. Daily checks for
tumour growth were carried out using callipers. After a period of 18-19 days
tumours were ~1cm3 and thereby suitable for imaging.
Data Acquisition: Animals were scanned on a 9.4T Varian 20cm horizontal
bore scanner (Varian Inc. Palo Alto, CA, USA) with a maximum gradient strength
of 400mT/m, and a 39mm birdcage RF coil (Rapid Biomedical, Rimpar, Germany).
Dental paste was used to help secure the tumour, and prevent excess respiratory
motion. In-Vivo VERDICT data was
acquired using a steady-state respiration-gated PGSE sequence with 46 b-values
in total. Gradient separation times Δ = 10, 20, 30, 40ms with
duration δ = 3ms for all Δs and δ = 10ms for Δ = 30, 40ms. Gradient strength G was stepped from 40 to 400mT/m in steps of 40mT/m for δ = 3ms and 40, 80, 120mT/m for δ = 10ms. The in-plane field-of-view was 25mm x
25mm, slice thickness 0.5mm, minimum TE, 2 averages, data matrix 64 x 64 and 3
slices per acquisition. The total scan time for in-vivo data was ~5hours. Ex-vivo data was acquired using the
same protocol, except with the number of averages increased to 6 to improve SNR
and TR was minimised. Total scan time was around 8.5 hours.
Tissue
Fixation: Perfusion fixation was
carried out through the left ventricle using heparinised saline followed by 4%
paraformaldehyde (PFA). This was followed by immersion fixation for 2 weeks in
4% PFA at 4°C. 1 week prior to
imaging the fixed animals were rinsed and washed and transferred to 0.9% saline
to rehydrate the tissue.
Image Analysis: Tumour ROIs were manually segmented from each data set. The signal was averaged over the whole ROI, and
modelling performed on the averaged signal. Model fitting was performed using
an iterative optimisation scheme in the Camino toolkit3. The
“BallSphereStick” model (using the taxonomy of Panagiotaki et al 2012)4
was fitted to in-vivo data, where the “Ball” and “Sphere” compartments
correspond to the free and restricted (intracellular) diffusion, respectively,
and the “Stick” compartment corresponds to the pseudo-diffusion within the
microvasculature. The intra-cellular diffusivity was fixed (diso=3e-9m2s-1)
For ex-vivo data the “BallSphere” model was fitted as there is no
pseudo-diffusion present.
Results
The VERDICT model provided an accurate fit to the
diffusion data for both in-vivo and ex-vivo samples. Figure 1 shows typical
fits produced for the data in this study, where the VERDICT model is capable of
delivering an accurate fit over the entire range of b-values. The cell-radius
parameters produced by the model-fitting are shown in figure 2. A significant
reduction (Wilcoxon rank-sum, p=0.0079) in cell radius was detected between the
in-vivo and ex-vivo datasets, in agreement with previous findings
1.
The measured decrease in cell size was supported by a significant (p=0.0079)
decrease in the intra-cellular volume fraction and an increase in the
extracellular volume fraction.
Discussion and Conclusions
This study aimed to evaluate whether a decrease in
tumour cell size caused by chemical fixation could be measured using VERDICT
MRI. We successfully measured a significant decrease in cell radius and
intracellular volume fraction in a subcutaneous colorectal tumour model. This
result demonstrates that there is a potential for VERDICT MRI to access measurements
of ex-vivo histological parameters from within tumours non-invasively. A direct
comparison between in-vivo, ex-vivo and histology with a variety of fixation
methods is required to validate this. One potential limitation of this study is
that different models were used to represent the in-vivo and ex-vivo
data, meaning direct comparison of the fitted parameters is more difficult.
Future in-silico work will aim to validate this.
Acknowledgements
This work was supported by a Wellcome Trust Senior
Research Fellowship (grant WT100247MA), King’s College London and UCL Comprehensive
Cancer Imaging Centre CR-UK & EPSRC, in association with the DoH (England).
This work was also supported by the EPSRC-funded UCL Centre for
Doctoral Training in Medical Imaging (EP/L016478/1) and the Department of
Health’s NIHR-funded Biomedical Research Centre at University College London
Hospitals.References
1. Panagiotaki E, Walker-Samuel S, et al. Non-invasive quantification of solid tumour microstructure using VERDICT MRI. Cancer Research. 2014;74(7):1902-12.
2. Folarin A, Konerding M, Timonen J, et al. Three-dimensional analysis of tumour vascular corrosion casts using stereoimaging and micro-computed tomography. Microvascular Research. 2010;80(1):89-98.
3. Cook PA, Bai Y, Alexander DC, et al. Camino: Open-Source Diffusion-MRI Reconstruction and Processing. Proc ISMRM. 2006:2759.
4. Panagiotaki E, Schneider T, Siow B, et al. Compartment models of the diffusion MR signal in brain white matter: a taxonomy and comparison. Neuroimage. 2012;59(3):2241-54.