Technical note: development of a 3D printed subresolution sandwich phantom for validation of brain SPECT analysis

Purpose: To make an adaptable, head shaped radionuclide phantom to simulate molecular imaging of the brain using clinical acquisition and reconstruction protocols. This will allow the characterization and correction of scanner characteristics, and improve the accuracy of clinical image analysis, including the application of databases of normal subjects. Methods: A fused deposition modeling 3D printer was used to create a head shaped phantom made up of transaxial slabs, derived from a simulated MRI dataset. The attenuation of the printed polylactide (PLA), measured by means of the Hounsfield unit on CT scanning, was set to match that of the brain by adjusting the proportion of plastic filament and air (fill ratio). Transmission measurements were made to verify the attenuation of the printed slabs. The radionuclide distribution within the phantom was created by adding 99mTc pertechnetate to the ink cartridge of a paper printer and printing images of gray and white matter anatomy, segmented from the same MRI data. The complete subresolution sandwich phantom was assembled from alternate 3D printed slabs and radioactive paper sheets, and then imaged on a dual headed gamma camera to simulate an HMPAO SPECT scan. Results: Reconstructions of phantom scans successfully used automated ellipse fitting to apply attenuation correction. This removed the variability inherent in manual application of attenuation correction and registration inherent in existing cylindrical phantom designs. The resulting images were assessed visually and by count profiles and found to be similar to those from an existing elliptical PMMA phantom. Conclusions: The authors have demonstrated the ability to create physically realistic HMPAO SPECT simulations using a novel head-shaped 3D printed subresolution sandwich method phantom. The phantom can be used to validate all neurological SPECT imaging applications. A simple modification of the phantom design to use thinner slabs would make it suitable for use in PET

A virtual imaging platform for multi-modality medical image simulation.

International audienceThis paper presents the Virtual Imaging Platform (VIP), a platform accessible at http://vip.creatis.insa-lyon.fr to facilitate the sharing of object models and medical image simulators, and to provide access to distributed computing and storage resources. A complete overview is presented, describing the ontologies designed to share models in a common repository, the workflow template used to integrate simulators, and the tools and strategies used to exploit computing and storage resources. Simulation results obtained in four image modalities and with different models show that VIP is versatile and robust enough to support large simulations. The platform currently has 200 registered users who consumed 33 years of CPU time in 2011

Simulation of Clinical PET Studies for the Assessment of Quantification Methods

On this PhD thesis we developed a methodology for evaluating the robustness of SUV measurements based on MC simulations and the generation of novel databases of simulated studies based on digital anthropomorphic phantoms. This methodology has been applied to different problems related to quantification that were not previously addressed. Two methods for estimating the extravasated dose were proposed andvalidated in different scenarios using MC simulations. We studied the impact of noise and low counting in the accuracy and repeatability of three commonly used SUV metrics (SUVmax, SUVmean and SUV50). The same model was used to study the effect of physiological muscular uptake variations on the quantification of FDG-PET studies. Finally, our MC models were applied to simulate 18F-fluorocholine (FCH) studies. The aim was to study the effect of spill-in counts from neighbouring regions on the quantification of small regions close to high activity extended sources

Phenomenological model of diffuse global and regional atrophy using finite-element methods

The main goal of this work is the generation of ground-truth data for the validation of atrophy measurement techniques, commonly used in the study of neurodegenerative diseases such as dementia. Several techniques have been used to measure atrophy in cross-sectional and longitudinal studies, but it is extremely difficult to compare their performance since they have been applied to different patient populations. Furthermore, assessment of performance based on phantom measurements or simple scaled images overestimates these techniques' ability to capture the complexity of neurodegeneration of the human brain. We propose a method for atrophy simulation in structural magnetic resonance (MR) images based on finite-element methods. The method produces cohorts of brain images with known change that is physically and clinically plausible, providing data for objective evaluation of atrophy measurement techniques. Atrophy is simulated in different tissue compartments or in different neuroanatomical structures with a phenomenological model. This model of diffuse global and regional atrophy is based on volumetric measurements such as the brain or the hippocampus, from patients with known disease and guided by clinical knowledge of the relative pathological involvement of regions and tissues. The consequent biomechanical readjustment of structures is modelled using conventional physics-based techniques based on biomechanical tissue properties and simulating plausible tissue deformations with finite-element methods. A thermoelastic model of tissue deformation is employed, controlling the rate of progression of atrophy by means of a set of thermal coefficients, each one corresponding to a different type of tissue. Tissue characterization is performed by means of the meshing of a labelled brain atlas, creating a reference volumetric mesh that will be introduced to a finite-element solver to create the simulated deformations. Preliminary work on the simulation of acquisition artefa- - cts is also presented. Cross-sectional and

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities

Development of a simulation platform for the evaluation of PET neuroimaging protocols in epilepsy

Monte Carlo simulation of PET studies is a reference tool for the evaluation and standardization of PET protocols. However, current Monte Carlo software codes require a high degree of knowledge in physics, mathematics and programming languages, in addition to a high cost of time and computational resources. These drawbacks make their use difficult for a large part of the scientific community. In order to overcome these limitations, a free and an efficient web-based platform was designed, implemented and validated for the simulation of realistic brain PET studies, and specifically employed for the generation of a wellvalidated large database of brain FDG-PET studies of patients with refractory epilepsy

Quantification of dopaminergic neurotransmission SPECT studies with 123 l-labelled radioligands

Dopaminergic neurotransmission SPECT studies with 123I-labelled radioligands can help in the diagnosis of neurological and psychiatric disorders such as Parkinson¿s disease and schizophrenia. Nowadays, interpretation of SPECT images is based mainly on visual assessment by experienced observers. However, a quantitative evaluation of the images is recommended in current clinical guidelines. Quantitative information can help diagnose the disease at the early pre-clinical stages, follow its progression and assess the effects of treatment strategies. SPECT images are affected by a number of effects that are inherent in the image formation: attenuation and scattering of photons, system response and partial volume effect. These effects degrade the contrast and resolution of the images and, as a consequence, the real activity distribution of the radiotracer is distorted. Whilst the photon emission of 123I is dominated by a low-energy line of 159 keV, it also emits several high-energy lines. When 123I-labelled radioligands are used, a non-negligible fraction of high-energy photons undergoes backscattering in the detector and the gantry and reaches the detector within the energy window. In this work, a complete methodology for the compensation of all the degrading effects involved in dopaminergic neurotransmission SPECT imaging with 123I is presented. The proposed method uses Monte Carlo simulation to estimate the scattered photons detected in the projections. For this purpose, the SimSET Monte Carlo code was modified so as to adapt it to the more complex simulation of high-energy photons emitted by 123I. Once validated, the modified SimSET code was used to simulate 123I SPECT studies of an anthropomorphic striatal phantom using different imaging systems. The projections obtained showed that scatter is strongly dependent on the imaging system and comprises at least 40% of the detected photons. Applying the new methodology demonstrated that absolute quantification can be achieved when the method includes accurate compensations for all the degrading effects. When the method did not include correction for all degradations, calculated values depended on the imaging system, although a linear relationship was found between calculated and true values. It was also found that partial volume effect and scatter corrections play a major role in the recovery of nominal values. Despite the advantages of absolute quantification, the computational and methodological requirements needed severely limit the possibility of application in clinical routine. Thus, for the time being, absolute quantification is limited to academic studies and research trials. In a clinical context, reliable, simple and rapid methods are needed, thus, semi-quantitative methods are used. Diagnosis also requires the establishment of robust reference values for healthy controls. These values are usually derived from a large data pool obtained in multicentre clinical trials. The comparison between the semi-quantitative values obtained from a patient and the reference is only feasible if the quantitative values have been previously standardised, i.e. they are independent of the gamma camera, acquisition protocol, reconstruction parameters and quantification procedure applied. Thus, standardisation requires that the calculated values are compensated somehow for all the image-degrading phenomena. In this thesis dissertation, a methodology for the standardisation of the quantitative values extracted from dopaminergic neurotransmission SPECT studies with 123I is evaluated using Monte Carlo simulation. This methodology is based on the linear relationship found between calculated and true values for a group of studies corresponding to different subjects with non-negligible anatomical and tracer uptake differences. Reconstruction and quantification methods were found to have a high impact on the linearity of the relationship and on the accuracy of the standardised results

Neuroimaging techniques in epilepsy

Objective: To review state-of-the-art neuroimaging modalities in epilepsy and their clinical applications. Data sources and study selection: PubMed literature searches to March 2010, using the following key words: 'epilepsy', 'positron emission tomography (PET)', 'single photon emission computed tomography (SPECT)', 'MR volumetry', 'diffusion tensor imaging', and 'functional MR imaging'. Data extraction All articles including neuroimaging techniques in epilepsy were included in the review. Data synthesis High-field magnetic resonance imaging is fundamental for high-resolution structural imaging. Functional radionuclide imaging (positron emission tomography/single-photon emission computed tomography) can provide additional information to improve overall accuracy, and show good results with high concordance rates in temporal lobe epilepsy. Magnetic resonance spectroscopy is a useful adjunct consistently demonstrating changing metabolites in the epileptogenic region. Magnetic resonance volumetric imaging shows excellent sensitivity and specificity for temporal lobe epilepsy but thus far it has been inconsistent for extratemporal epilepsy. Diffusion tensor imaging with tractography allows visualisation of specific tracts such as connections with the language and visual cortex to enhance preoperative evaluation. Functional magnetic resonance imaging using blood oxygen level-dependent activation techniques is mainly used in presurgical planning for the high-sensitivity mapping of the eloquent cortex. Both contrast-bolus and arterial spin labelling magnetic resonance perfusion imaging show good correlation with clinical lateralisation of seizure disorder. Conclusion Structural imaging is essential in localisation and lateralization of the seizure focus. Functional radionuclide imaging or advanced magnetic resonance imaging techniques can provide complementary information when an epileptogenic substrate is not identified or in the presence of non-concordant clinical and structural findings.link_to_subscribed_fulltex

State of the Art of Level Set Methods in Segmentation and Registration of Medical Imaging Modalities

Segmentation of medical images is an important step in various applications such as visualization, quantitative analysis and image-guided surgery. Numerous segmentation methods have been developed in the past two decades for extraction of organ contours on medical images. Low-level segmentation methods, such as pixel-based clustering, region growing, and filter-based edge detection, require additional pre-processing and post-processing as well as considerable amounts of expert intervention or information of the objects of interest. Furthermore the subsequent analysis of segmented objects is hampered by the primitive, pixel or voxel level representations from those region-based segmentation. Deformable models, on the other hand, provide an explicit representation of the boundary and the shape of the object. They combine several desirable features such as inherent connectivity and smoothness, which counteract noise and boundary irregularities, as well as the ability to incorporate knowledge about the object of interest. However, parametric deformable models have two main limitations. First, in situations where the initial model and desired object boundary differ greatly in size and shape, the model must be re-parameterized dynamically to faithfully recover the object boundary. The second limitation is that it has difficulty dealing with topological adaptation such as splitting or merging model parts, a useful property for recovering either multiple objects or objects with unknown topology. This difficulty is caused by the fact that a new parameterization must be constructed whenever topology change occurs, which requires sophisticated schemes. Level set deformable models, also referred to as geometric deformable models, provide an elegant solution to address the primary limitations of parametric deformable models. These methods have drawn a great deal of attention since their introduction in 1988. Advantages of the contour implicit formulation of the deformable model over parametric formulation include: (1) no parameterization of the contour, (2) topological flexibility, (3) good numerical stability, (4) straightforward extension of the 2D formulation to n-D. Recent reviews on the subject include papers from Suri. In this chapter we give a general overview of the level set segmentation methods with emphasize on new frameworks recently introduced in the context of medical imaging problems. We then introduce novel approaches that aim at combining segmentation and registration in a level set formulation. Finally we review a selective set of clinical works with detailed validation of the level set methods for several clinical applications