Imaging phantom

About: Imaging phantom is a research topic. Over the lifetime, 28170 publications have been published within this topic receiving 510003 citations. The topic is also known as: phantom.

The need for application-based adaptation of deformable image registration.

Abstract: Purpose: To utilize a deformable phantom to objectively evaluate the accuracy of 11 different deformable image registration (DIR) algorithms. Methods: The phantom represents an axial plane of the pelvic anatomy. Urethane plastic serves as the bony anatomy and urethane rubber with three levels of Hounsfield units (HU) is used to represent fat and organs, including the prostate. A plastic insert is placed into the phantom to simulate bladder filling. Nonradiopaque markers reside on the phantom surface. Optical camera images of these markers are used to measure the positions and determine the deformation from the bladder insert. Eleven different DIR algorithms are applied to the full and empty-bladder computed tomography images of the phantom (fixed and moving volumes, respectively) to calculate the deformation. The algorithms include those fromMIM Software (MIM) and Velocity Medical Solutions (VEL) and nine different implementations from the deformable image registration and adaptive radiotherapy toolbox for Matlab. These algorithms warp one image to make it similar to another, but must utilize a method for regularization to avoid physically unrealistic deformation scenarios. The mean absolute difference (MAD) between the HUs at the marker locations on one image and the calculated location on the other serves as a metric to evaluate the balance between image similarity and regularization. To demonstrate the effect of regularization on registration accuracy, an additional beta version of MIM was created with a variable smoothness factor that controls the emphasis of the algorithm on regularization. The distance to agreement between the measured and calculated marker deformations is used to compare the overall spatial accuracy of the DIR algorithms. This overall spatial accuracy is also utilized to evaluate the phantom geometry and the ability of the phantom soft-tissue heterogeneity to represent patient data. To evaluate the ability of the DIR algorithms to accurately transfer anatomical contours, the rectum is delineated on both the fixed and moving images. A Dice similarity coefficient is then calculated between the contour on the fixed image and that transferred, via the calculated deformation, from the moving to the fixed image. Results: The phantom possesses sufficient soft-tissue heterogeneity to act as a proxy for patient data. Large discrepancies appear between the algorithms and the measured ground-truth deformation. VEL yields the smallest mean spatial error and a Dice coefficient of 0.90. MIM produces the lowest MAD value and the highest Dice coefficient of 0.96, but creates the largest spatial errors. Increasing theMIM smoothness factor above the default value improves the overall spatial accuracy, but the factor associated with the lowest mean error decreases the Dice coefficient to 0.85. Conclusions: Different applications of DIR require disparate balances between image similarity and regularization. A DIR algorithm that is optimized only for its ability to transfer anatomical contours will yield large deformation errors in homogeneous regions, which is problematic for dose mapping. For this reason, these algorithms must be tested for their overall spatial accuracy. The developed phantom is an objective tool for this purpose.

Projection X-Space Magnetic Particle Imaging

Abstract: Projection magnetic particle imaging (MPI) can improve imaging speed by over 100-fold over traditional 3-D MPI. In this work, we derive the 2-D x-space signal equation, 2-D image equation, and introduce the concept of signal fading and resolution loss for a projection MPI imager. We then describe the design and construction of an x-space projection MPI scanner with a field gradient of 2.35 T/m across a 10 cm magnet free bore. The system has an expected resolution of 3.5 × 8.0 mm using Resovist tracer, and an experimental resolution of 3.8 × 8.4 mm resolution. The system images 2.5 cm × 5.0 cm partial field-of views (FOVs) at 10 frames/s, and acquires a full field-of-view of 10 cm × 5.0 cm in 4 s. We conclude by imaging a resolution phantom, a complex “Cal” phantom, mice injected with Resovist tracer, and experimentally confirm the theoretically predicted x-space spatial resolution.

Evaluation of inverse methods and head models for EEG source localization using a human skull phantom.

Abstract: We used a real-skull phantom head to investigate the performances of representative methods for EEG source localization when considering various head models. We describe several experiments using a montage with current sources located at multiple positions and orientations inside a human skull filled with a conductive medium. The robustness of selected methods based on distributed source models is evaluated as various solutions to the forward problem (from the sphere to the finite element method) are considered. Experimental results indicate that inverse methods using appropriate cortex-based source models are almost always able to locate the active source with excellent precision, with little or no spurious activity in close or distant regions, even when two sources are simultaneously active. Superior regularization schemes for solving the inverse problem can dramatically help the estimation of sparse and focal active zones, despite significant approximation of the head geometry and the conductivity properties of the head tissues. Realistic head models are necessary, though, to fit the data with a reasonable level of residual variance.

Use of the Magnetic Field Generated by the Internal Distribution of Injected Currents for Electrical Impedance Tomography (MR-EIT)

Abstract: In two dimensional conventional Electrical Impedance Tomography (EIT), volume conductor is probed by means of injected currents, and peripheral voltage measurements are used as input to the reconstruction algorithm. The current that flows in the 2D object creates magnetic fields that are perpendicular to the plane of imaging. Such magnetic fields can be measured using magnetic resonance tomography. In this study, use of this magnetic field generated by the injected currents, for the purpose of reconstructing the conductivity distribution, is studied. Sensitivity matrix relating the magnetic field to the element conductivities is calculated using the Finite Element Method and Biot-Savart law. Linearization is made during sensitivity matrix formation. This matrix is inverted using singular value decompostion. Simulations for objects placed in different parts of the imaging region are made to understand the spatial dependency of the proposed method and it is seen that the method has uniform sensitivity throughout the imaging region. Finally, images reconstructed using data taken from an experimental phantom are presented.

Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology.

Abstract: Purpose: Different computed tomography (CT) reconstruction techniques offer different image quality attributes of resolution and noise, challenging the ability to compare their dose reduction potential against each other. The purpose of this study was to evaluate and compare the task-based imaging performance of CT systems to enable the assessment of the dose performance of a model-based iterative reconstruction (MBIR) to that of an adaptive statistical iterative reconstruction (ASIR) and a filtered back projection (FBP) technique. Methods: The ACR CT phantom (model 464) was imaged across a wide range of mA setting on a 64-slice CT scanner (GE Discovery CT750 HD, Waukesha, WI). Based on previous work, the resolution was evaluated in terms of a task-based modulation transfer function (MTF) using a circular-edge technique and images from the contrast inserts located in the ACR phantom. Noise performance was assessed in terms of the noise-power spectrum (NPS) measured from the uniform section of the phantom. The task-based MTF and NPS were combined with a task function to yield a task-based estimate of imaging performance, the detectability index (d′). The detectability index was computed as a function of dose for two imaging tasks corresponding to the detection of a relatively small and a relatively large feature (1.5 and 25 mm, respectively). The performance of MBIR in terms of the d′ was compared with that of ASIR and FBP to assess its dose reduction potential. Results: Results indicated that MBIR exhibits a variability spatial resolution with respect to object contrast and noise while significantly reducing image noise. The NPS measurements for MBIR indicated a noise texture with a low-pass quality compared to the typical midpass noise found in FBP-based CT images. At comparable dose, the d′ for MBIR was higher than those of FBP and ASIR by at least 61% and 19% for the small feature and the large feature tasks, respectively. Compared to FBP and ASIR, MBIR indicated a 46%–84% dose reduction potential, depending on task, without compromising the modeled detection performance. Conclusions: The presented methodology based on ACR phantom measurements extends current possibilities for the assessment of CT image quality under the complex resolution and noise characteristics exhibited with statistical and iterative reconstruction algorithms. The findings further suggest that MBIR can potentially make better use of the projections data to reduce CT dose by approximately a factor of 2. Alternatively, if the dose held unchanged, it can improve image quality by different levels for different tasks.