4 research outputs found
Comparison of peripheral quantitative computed tomography and magnetic resonance imaging for tissue characterisation in the gastrocnemius muscle
of the calf muscles. Magnetic resonance imaging (MRI) and ultrasound (US) are the
medical imaging modalities that are usually used to assess such injuries.
Texture analysis is a digital image processing technique that quantifies the
relationship between pixel intensities (grey levels) and pixel positions. Texture can
reveal valuable information that cannot be perceived by the naked eye. Dedicated
image processing software is required to extract texture parameters. Texture analysis
has been implemented for medical imaging modalities such as MRI, US and
computed tomography (CT) for the evaluation sports muscle injury.
Peripheral quantitative tomography (pQCT) is an adaptation of conventional CT. In
this project, texture analysis was implemented on MRI and pQCT images of the
gastrocnemius muscle (GM). MRI is an expensive technique that requires specialised
facilities. Conversely, pQCT utilises a small-bore, low-dose X-ray scanner, which is
portable and less costly than MRI. It has traditionally been used mainly for bone
analysis. The aim of this study was to assess the suitability of pQCT for GM tissue
characterisation using texture analysis compared with MRI. The study is novel in that
it is the first to apply texture analysis to GM images using pQCT
Texture analysis was done on image data acquired from MRI (GE, 1.5T) and pQCT
(Stratec XCT 2000) in a group of healthy human subjects and an injured subject. A
water phantom was also scanned with pQCT. An existing standard imaging protocol
was observed for MRI acquisition, while pQCT image acquisition parameters were
explored and optimised to yield a standard protocol.
The pQCT scanner was shown to be capable of acquiring calf muscle images and
distinguishing calf muscle boundaries. Texture parameters (grey level, variance,
skewness, kurtosis, co-occurrence matrix, run length matrix, gradient, autoregressive
(AR) model and wavelet transform) were extracted from the acquired images. The
repeatability of these quantities for pQCT in a healthy human subject and a water
phantom was assessed by calculating the coefficient of variation (%CV). The effect
of pQCT parameters (scan speed and pixel size) was tested using multiple variate
II
analysis of variance (MANOVA). The effect of region of interest (ROI) area and
anatomical position were evaluated using simple linear regression.
The t-test was used to compare the mean values of the texture features in the right
and left leg for both MRI and pQCT in a group of healthy human subjects. Neither
MRI nor pQCT showed significant differences between the two legs for any of the
texture features. In addition, there was no significant difference between the two
modalities for the AR model and wavelet transform texture parameters. Reference
ranges for the medial head of the GM were defined for both modalities. A study of a
single injured subject revealed that the values of the AR model texture parameter fell
outside the reference ranges for both MRI and pQCT, and so the AR model was
identified as the most sensitive texture parameter for distinguishing injured from
uninjured GM.
The principal conclusion from this work is that pQCT has the potential to be used for
imaging the gastrocnemius muscle and that GM images from both MRI and pQCT
scanners can be objectively characterised by texture analysis. In addition, the autoregressive
model texture parameter may be the most appropriate for muscle
characterisation