Practical aspects of a data-driven motion correction approach for brain SPECT

Patient motion can cause image artifacts in single photon emission computed tomography despite restraining measures. Data-driven detection and correction of motion can be achieved by comparison of acquired data with the forward projections. This enables the brain locations to be estimated and data to be correctly incorporated in a three-dimensional (3-D) reconstruction algorithm. Digital and physical phantom experiments were performed to explore practical aspects of this approach. Noisy simulation data modeling multiple 3-D patient head movements were constructed by projecting the digital Hoffman brain phantom at various orientations. Hoffman physical phantom data incorporating deliberate movements were also gathered. Motion correction was applied to these data using various regimes to determine the importance of attenuation and successive iterations. Studies were assessed visually for artifact reduction, and analyzed quantitatively via a mean registration error (MRE) and mean square difference measure (MSD). Artifacts and distortion in the motion corrupted data were reduced to a large extent by application of this algorithm. MRE values were mostly well within 1 pixel (4.4 mm) for the simulated data. Significant MSD improvements (>2) were common. Inclusion of attenuation was unnecessary to accurately estimate motion, doubling the efficiency and simplifying implementation. Moreover, most motion-related errors were removed using a single iteration. The improvement for the physical phantom data was smaller, though this may be due to object symmetry. In conclusion, these results provide the basis of an implementation protocol for clinical validation of the technique

A hybrid 3d reconstruction/registration algorithm for correction of head motion in emission tomography

Even with head restraint, small head movements can occur during data acquisition for emission tomography, sufficiently large to result in detectable artifacts in the final reconstruction. Direct measurement of motion can be cumbersome and difficult to implement, whereas previous attempts to correct for motion based on measured projections have been limited to simple translation orthogonal to the projection. A fully 3D algorithm is proposed that estimates the patient orientation at any time based on the projection of motion-corrupted data, with incorporation of the measured motion within subsequent OSEM sub-iterations. Preliminary studies have been performed using a digital version of the Hoffman brain phantom. Movement was simulated by constructing a mixed set of projections in two discrete positions of the phantom. The algorithm determined the phantom orientation that best aligned each constructed projection with its corresponding, measured projection. In the case of simulated movement of 24 of 64 projections, all mis-positioned projections were correctly identified. The algorithm resulted in a reduction of mean square difference (MSD) between motion corrected and motion-free reconstructions compared to the MSD between uncorrected and motion-free reconstructions by a factor of 2.7

PET/MRI attenuation estimation in the lung: A review of past, present, and potential techniques

Positron emission tomography/magnetic resonance imaging (PET/MRI) potentially offers several advantages over positron emission tomography/computed tomography (PET/CT), for example, no CT radiation dose and soft tissue images from MR acquired at the same time as the PET. However, obtaining accurate linear attenuation correction (LAC) factors for the lung remains difficult in PET/MRI. LACs depend on electron density and in the lung, these vary significantly both within an individual and from person to person. Current commercial practice is to use a single-valued population-based lung LAC, and better estimation is needed to improve quantification. Given the under-appreciation of lung attenuation estimation as an issue, the inaccuracy of PET quantification due to the use of single-valued lung LACs, the unique challenges of lung estimation, and the emerging status of PET/MRI scanners in lung disease, a review is timely. This paper highlights past and present methods, categorizing them into segmentation, atlas/mapping, and emission-based schemes. Potential strategies for future developments are also presented

A novel technique to incorporate structural prior information into multi-modal tomographic reconstruction

There has been a rapid expansion of multi-modal imaging techniques in tomography. In biomedical imaging, patients are now regularly imaged using both single photon emission computed tomography (SPECT) and x-ray computed tomography (CT), or using both positron emission tomography and magnetic resonance imaging (MRI). In non-destructive testing of materials both neutron CT (NCT) and x-ray CT are widely applied to investigate the inner structure of material or track the dynamics of physical processes. The potential benefits from combining modalities has led to increased interest in iterative reconstruction algorithms that can utilize the data from more than one imaging mode simultaneously. We present a new regularization term in iterative reconstruction that enables information from one imaging modality to be used as a structural prior to improve resolution of the second modality. The regularization term is based on a modified anisotropic tensor diffusion filter, that has shape-adapted smoothing properties. By considering the underlying orientations of normal and tangential vector fields for two co-registered images, the diffusion flux is rotated and scaled adaptively to image features. The images can have different greyscale values and different spatial resolutions. The proposed approach is particularly good at isolating oriented features in images which are important for medical and materials science applications. By enhancing the edges it enables both easy identification and volume fraction measurements aiding segmentation algorithms used for quantification. The approach is tested on a standard denoising and deblurring image recovery problem, and then applied to 2D and 3D reconstruction problems; thereby highlighting the capabilities of the algorithm. Using synthetic data from SPECT co-registered with MRI, and real NCT data co-registered with x-ray CT, we show how the method can be used across a range of imaging modalities

”High Resolution Gamma Cameras for Molecular Imaging”

We have developed, in the framework of the HICAM project supported by EC, two Anger cameras, with 5x5 cm2 and 10x10 cm2 FOV.They offer a high intrinsic spatial resolution (<1mm), an overall spatial resolution of ~ 2.5 mm @5cm and appropriate sensitivity. The cameras are based on the use of a matrix of Silicon Drift Detectors (SDDs) coupled to a CsI(Tl) crystal. The cameras are compact, very versatile and have a potential to be employed in several imaging applications, for clinical trials and for molecular imaging studies on small animals. Preliminary results from the experimentation in clinical tests, cellular imaging and SPECT are here presente

Reconstruction algorithm for single photon emission computed tomography and its numerical implementation

The modern imaging techniques of positron emission tomography and of single photon emission computed tomography are not only two of the most important tools for studying the functional characteristics of the brain, but they now also play a vital role in several areas of clinical medicine, including neurology, oncology and cardiology. The basic mathematical problems associated with these techniques are the construction of the inverse of the Radon transform and of the inverse of the so-called attenuated Radon transform, respectively. An exact formula for the inverse Radon transform is well known, whereas that for the inverse attenuated Radon transform was obtained only recently by R. Novikov. The latter formula was constructed by using a method introduced earlier by R. Novikov and the first author in connection with a novel derivation of the inverse Radon transform. Here, we first show that the appropriate use of that earlier result yields immediately an analytic formula for the inverse attenuated Radon transform. We then present an algorithm for the numerical implementation of this analytic formula, based on approximating the given data in terms of cubic splines. Several numerical tests are presented which suggest that our algorithm is capable of producing accurate reconstruction for realistic phantoms such as the well-known Shepp–Logan phantom

HICAM: development of a high-resolution AngerCamera for Nuclear Medicine

reserved16Pubblicazione su CDROM -M06-2009 INFNC. FIORINI;A. GOLA;R. PELOSO;A. GERACI;A. LONGONI;G. PADOVINI;P. LECHNER;L. STRÜDER;B. F. HUTTON;K. ERLANDSSON;S. MAHMOOD;P. VAN MULLEKOM;A. PEDRETTI;R. MORETTI;G. L. POLI;G. LUCIGNANIFiorini, CARLO ETTORE; Gola, Alberto; Peloso, Roberta; Geraci, Angelo; Longoni, ANTONIO FRANCESCO; Padovini, GIORGIO MICHELE; P., Lechner; L., Strüder; B. F., Hutton; K., Erlandsson; S., Mahmood; P., VAN MULLEKOM; A., Pedretti; R., Moretti; G. L., Poli; G., Lucignan