Statistical iterative methods have been extensively demonstrated to outperform analytical methods in terms
of image quality in nuclear imaging. In the method, the mathematically unknown biodistribution is usually
represented by cubic basis functions in 3D. Alternatively, spherical basis functions have demonstrated lower
noise produced in the resulting reconstructed images. Additionally, the system response matrix (SRM), a key
element required by iterative methods, is usually too large to be stored in random access memory of a regular
computer. The SRM can be calculated prior to reconstruction and stored on-disk, and thus be directly accessed
during the reconstruction process. But this approach usually makes the process too time consuming. To reduce
the number of elements to be computed and stored, a common approach uses the scanner symmetries. In this
work we use polar voxels, reducing the number of non-zero elements to be computed by a factor 72, allowing
us to speed up the Monte-Carlo simulations in which the computation of the SRM is based. In this work we
combine the use blobs, a type of spherical basis function, as basis functions and a polar representation in the
SRM. The latter is especially important for blobs, given that due to the overlapping of blobs, the size of the SRM
significantly increases. This work will show a quantitative comparison of reconstructed images using Cartesian
voxels, polar voxels and blobs. We will show that blobs reduce image noise compared to voxels, producing a
lower spatial resolution degradation, compared to polar voxels after Gaussian filtering.
Segmentation in medical imaging plays a critical role easing the delineation of key anatomical functional
structures in all the imaging modalities. However, many segmentation approaches are optimized with the
assumption of high contrast, and then fail when segmenting poor contrast to noise objects. The number of
approaches published in the literature falls dramatically when functional imaging is the aim. In this paper a
feature extraction based approach, based on region growing, is presented as a segmentation technique suitable
for poor quality (low Contrast to Noise Ratio CNR) images, as often found in functional images derived from
Autoradiography. The region growing combines some modifications from the typical region growing method,
to make the algorithm more robust and more reliable. Finally the algorithm is validated using synthetic
images and biological imagery.
Autoradiography is a well established imaging modality in Biology and Medicine. This aims to measure the location and concentration of labelled molecules within thin tissue sections. The brain is the most anatomically complex organ and identification of neuroanatomical structures is still a challenge particularly when small
animals are used for pre-clinical trials. High spatial resolution and high sensitivity are therefore necessary.
This work shows the performance and ability of a prototype commercial system, based on a Charged-Couple Device (CCD), to accurately obtain detailed functional information in brain Autoradiography. The sample is placed in contact with the detector enabling direct detection of β- particles in silicon, and the system is run in a range of quasi-room temperatures (17-22 °C) under stable conditions by using a precision temperature controller. Direct detection of
β- particles with low energy down to ~5 keV from <sup>3</sup>[H] is possible using this room temperature approach. The CCD used in this work is an E2V CCD47-20 frame-transfer device which removes the image smear arising in conventional full-frame imaging devices. The temporal stability of the system has been analyzed by exposing a set of <sup>14</sup>[C] calibrated microscales for different periods of time, and measuring the stability of the resultant sensitivity and background noise. The thermal performance of the system has also been analyzed in order to demonstrate its capability of working in other life science applications, where higher working temperatures are required. Once the performance of the system was studied, a set of experiments with biological samples, labelled with typical β- radioisotopes, such as <sup>3</sup>[H], has been carried out to demonstrate its application in life sciences.