Combining bone structure and density measurement in 3D is required to assess site-specific fracture risk. Spectral
molecular imaging can measure bone structure in relation to bone density by measuring macro and microstructure of bone
in 3D. This study aimed to optimize spectral CT methodology to measure bone structure in excised bone samples. MARS
CT with CdTe Medipix3RX detector was used in multiple energy bins to calibrate bone structure measurements. To
calibrate thickness measurement, eight different thicknesses of Aluminium (Al) sheets were scanned one in air and the
other around a falcon tube and then analysed. To test if trabecular thickness measurements differed depending on scan
plane, a bone sample from sheep proximal tibia was scanned in two orthogonal directions. To assess the effect of air on
thickness measurement, two parts of the same human femoral head were scanned in two conditions (in the air and in PBS).
The results showed that the MARS scanner (with 90μm voxel size) is able to accurately measure the Al (in air) thicknesses
over 200μm but it underestimates the thicknesses below 200μm because of partial volume effect in Al-air interface. The
Al thickness measured in the highest energy bin is overestimated at Al-falcon tube interface. Bone scanning in two
orthogonal directions gives the same trabecular thickness and air in the bone structure reduced measurement accuracy. We
have established a bone structure assessment protocol on MARS scanner. The next step is to combine this with bone
densitometry to assess bone strength.
The Medipix All Resolution Scanner (MARS) spectral CT is intended for small animal, pre-clinical imaging and uses an x-ray detector (Medipix) operating in single photon counting mode. The MARS system provides spectrometric information to facilitate differentiation of tissue types and bio-markers. For longitudinal studies of disease models, it is desirable to characterise the system’s dosimetry. This dosimetry study is performed using three phantoms each consisting of a 30 mm diameter homogeneous PMMA cylinder simulating a mouse. The imaging parameters used for this study are derived from those used for gold nanoparticle identification in mouse kidneys. Dosimetry measurement are obtained with thermo-luminescent Lithium Fluoride (LiF:CuMgP) detectors, calibrated in terms of air kerma and placed at different depths and orientations in the phantoms. Central axis TLD air kerma rates of 17.2 (± 0.71) mGy/min and 18.2 (± 0.75) mGy/min were obtained for different phantoms and TLD orientations. Validation measurements were acquired with a pencil ionization chamber, giving an air-kerma rate of 20.3 (±1) mGy/min and an estimated total air kerma of 81.2 (± 4) mGy for a 720 projection acquisition. It is anticipated that scanner design improvements will significantly decrease future dose requirements. The procedures developed in this work will be used for further dosimetry calculations when optimizing image acquisition for the MARS system as it undergoes development towards human clinical applications.
X-ray scatter can cause significant distortion in CT imaging, especially with the move to cone-beam geometries. Incoherent scatter (Compton scatter) is known to reduce the energy of scattered photons according to the angle of the scattering. The emergence of energy-resolved x-ray detectors offers an opportunity to produce and apply more accurate scatter estimates, leading to improved image quality. We have developed a scatter estimation algorithm that accounts for the variation in scatter with incident radiation energy. Where existing methods generate estimates of scatter for the complete detected energy band, our new method produces separate estimates for each of the energy bands that are measured, allowing a more focused correction of scatter. Our method is intended to be used in an iterative compensation framework like that of Rührnschopf and Klingenbeck (2011); it calculates the scatter contribution to each energy bin used in a scan based on the current volume estimate. Comparisons with Monte Carlo simulations indicate that this algorithm is effective at estimating the scatter level in separate energy bins. We found that the amount of scatter that loses enough energy to hop between energy bands is small enough to neglect, but that scatter intensity is dependent on the incident energy, so application of a spectrally-aware compensation technique is valuable.
Photon counting detectors are of growing importance in medical imaging because they enable routine measurement
of photon energy. Detectors such as Medipix2 and Medipix3 record the energy of incident photons with
minimal loss of spatial resolution. Their use is being investigated for both pre-clinical and clinical applications
of X-ray CT. The Medipix3 detector has 256 x 256 55 μm pixels and a silicon or cadmium telluride detector
layer, giving a spatial resolution comparable to mammographic film. Each Medipix pixel can be seen as an individual
spectral detector. The logic circuits for each pixel (some 1300 transistors) can analyze incoming events at
megahertz rates, comparing the charge of the electron-hole cloud with preset levels, giving a resolution of about
2 keV across the range of 8 - 140 keV.
A prototype CT scanner has been developed for laboratory animals and excised specimens. Applications under
investigation include: K-edge imaging: Using spectral information to measure heavy elements (e.g., preparations
of iodine, barium, and gadolinium) and Soft tissue contrast: Dual energy systems have shown that image contrast
for soft tissue can be improved, e.g., distinguishing between iron and calcium within vascular plaques.
Quantitation of coherent x-ray scatter traditionally involves measuring the intensity of the scattered x-ray over a range of
angles (θ) from the illuminating monochromatic x-ray beam. Spectral x-ray imaging produces the same information at a
single θ when bremsstrahlung x-ray exposure is used. We used a 200μm thick sheet-illumination of a phantom (lucite
cylinder containing holes with water, polyethylene, collagen, polycarbonate, and nylon) and a polycapillary x-ray optic
collimator to provide measurements at a fixed θ. A Medipix2 x-ray detection array (2562 (55μm)2 pixels) provided the spectral (E, 10 - 22 keV in 3keV energy bins) spread needed to generate the momentum transfer (q) profile information
at one angle. The tungsten x-ray source anode (aluminum filter) was operated at 35kVp at 20mA. The detected scatter
intensity was corrected for attenuation of the incident and the scattered x-ray by use of the regular CT image of the
phantom generated at the same energy bins. The phantom was translated normal to the plane of the fan beam in 65,
0.2mm, steps to generate the 3D image data. The momentum transfer profiles generated with this approach were
compared to published momentum transfer profiles obtained by other methods.
Frontal chest radiographs ("chest X-rays") are routinely used by medical personnel to assess patients for a wide
range of suspected disorders. Often large numbers of images need to be analyzed. Furthermore, at times the
images need to analyzed ("reported") when no radiological expert is available. A system which enhances the
images in such a way that abnormalities are more obvious is likely to reduce the chance that an abnormality
goes unnoticed. The authors previously reported the use of principal components analysis to derive a basis set
of eigenimages from a training set made up of images from normal subjects. The work is here extended to
investigate how best to emphasize the abnormalities in chest radiographs. Results are also reported for various
forms of image normalizing transformations used in performing the eigenimage processing.
A method first employed for face recognition has been employed to analyse a set of chest x-ray images. After marking certain common features on the images, they are registered by means of an affine transformation. The differences between each registered image and the mean of all images in the set are computed and the first K principal components are found, where K is less than or equal to the number of images in the set. These form eigenimages (we have coined the term 'eigenchests') from which an approximation to any one of the original images can be reconstructed. Since the method effectively treats each pixel as a dimension in a hyperspace, the matrices concerned are huge; we employ the method developed by Turk and Pentland for face recognition to make the computations tractable. The K coefficients for the eigenimages encode the variation between images
and form the basis for discriminating normal from abnormal. Preliminary results have been obtained for a set of eigenimages formed from a set of normal chests and tested on separate sets of normals and patients with pneumonia. The distributions of coefficients have been observed to be different for the two test sets and work is continuing to determine the most sensitive method for detecting the differences.