Geometry calibration method for a cone-beam CT system

An X-Ray C-Arm Guided Automatic Targeting System for Histotripsy

OBJECTIVE

Histotripsy is an emerging noninvasive, nonionizing and nonthermal focal cancer therapy that is highly precise and can create a treatment zone of virtually any size and shape. Current histotripsy systems rely on ultrasound imaging to target lesions. However, deep or isoechoic targets obstructed by bowel gas or bone can often not be treated safely using ultrasound imaging alone. This work presents an alternative x-ray C-arm based targeting approach and a fully automated robotic targeting system.

METHODS

The approach uses conventional cone beam CT (CBCT) images to localize the target lesion and 2D fluoroscopy to determine the 3D position and orientation of the histotripsy transducer relative to the C-arm. The proposed pose estimation uses a digital model and deep learning-based feature segmentation to estimate the transducer focal point relative to the CBCT coordinate system. Additionally, the integrated robotic arm was calibrated to the C-arm by estimating the transducer pose for four preprogrammed transducer orientations and positions. The calibrated system can then automatically position the transducer such that the focal point aligns with any target selected in a CBCT image.

RESULTS

The accuracy of the proposed targeting approach was evaluated in phantom studies, where the selected target location was compared to the center of the spherical ablation zones in post-treatment CBCTs. The mean and standard deviation of the Euclidean distance was 1.4 ±0.5 mm. The mean absolute error of the predicted treatment radius was 0.5 ±0.5 mm.

CONCLUSION

CBCT-based histotripsy targeting enables accurate and fully automated treatment without ultrasound guidance.

SIGNIFICANCE

The proposed approach could considerably decrease operator dependency and enable treatment of tumors not visible under ultrasound.

Online geometry calibration for retrofit computed tomography from a mouse rotation system and a small-animal imager

BACKGROUND

Computed tomography (CT) generates a three-dimensional rendering that can be used to interrogate a given region or desired structure from any orientation. However, in preclinical research, its deployment remains limited due to relatively high upfront costs. Existing integrated imaging systems that provide merged planar X-ray also dwarfs CT popularity in small laboratories due to their added versatility.

PURPOSE

In this paper, we sought to generate CT-like data using an existing small-animal X-ray imager with a specialized specimen rotation system, or MiSpinner. This setup conforms to the cone-beam CT (CBCT) geometry, which demands high spatial calibration accuracy. Therefore, a simple but robust geometry calibration algorithm is necessary to ensure that the entire imaging system works properly and accurately.

METHODS

Because the rotation system is not permanently affixed, we propose a structure tensor-based two-step online (ST-TSO) geometry calibration algorithm. Specifically, two datasets are needed, namely, calibration and actual measurements. A calibration measurement detects the background of the system forward X-ray projections. A study on the background image reveals the characteristics of the X-ray photon distribution, and thus, provides a reliable estimate of the imaging geometry origin. Actual measurements consisted of an X-ray of the intended object, including possible geometry errors. A comprehensive image processing technique helps to detect spatial misalignment information. Accordingly, the first processing step employs a modified projection matrix-based calibration algorithm to estimate the relevant geometric parameters. Predicted parameters are then fine-tuned in a second processing step by an iterative strategy based on the symmetry property of the sum of projections. Virtual projections calculated from the parameters after two-step processing compensate for the scanning errors and are used for CT reconstruction. Experiments on phantom and mouse imaging data were performed to validate the calibration algorithm.

RESULTS

Once system correction was conducted, CBCT of a CT bar phantom and a cohort of euthanized mice were analyzed. No obvious structure error or spatial artifacts were observed, validating the accuracy of the proposed geometry calibration method. Digital phantom simulation indicated that compared with the preset spatial values, errors in the final estimated parameters could be reduced to 0.05° difference in dominant angle and 0.5-pixel difference in dominant axis bias. The in-plane resolution view of the CT-bar phantom revealed that the resolution approaches 150 μ $\umu$ m.

CONCLUSIONS

A constrained two-step online geometry calibration algorithm has been developed to calibrate an integrated X-ray imaging system, defined by a first-step analytical estimation and a second-step iterative fine-tuning. Test results have validated its accuracy in system correction, thus demonstrating the potential of the described system to be modified and adapted for preclinical research.

An empirical method for geometric calibration of a photon counting detector-based cone beam CT system

BACKGROUND

Geometric calibration is essential in developing a reliable computed tomography (CT) system. It involves estimating the geometry under which the angular projections are acquired. Geometric calibration of cone beam CTs employing small area detectors, such as currently available photon counting detectors (PCDs), is challenging when using traditional-based methods due to detectors' limited areas.

OBJECTIVE

This study presented an empirical method for the geometric calibration of small area PCD-based cone beam CT systems.

METHODS

Unlike the traditional methods, we developed an iterative optimization procedure to determine geometric parameters using the reconstructed images of small metal ball bearings (BBs) embedded in a custom-built phantom. An objective function incorporating the sphericities and symmetries of the embedded BBs was defined to assess performance of the reconstruction algorithm with the given initial estimated set of geometric parameters. The optimal parameter values were those which minimized the objective function. The TIGRE toolbox was employed for fast tomographic reconstruction. To evaluate the proposed method, computer simulations were carried out using various numbers of spheres placed in various locations. Furthermore, efficacy of the method was experimentally assessed using a custom-made benchtop PCD-based cone beam CT.

RESULTS

Computer simulations validated the accuracy and reproducibility of the proposed method. The precise estimation of the geometric parameters of the benchtop revealed high-quality imaging in CT reconstruction of a breast phantom. Within the phantom, the cylindrical holes, fibers, and speck groups were imaged in high fidelity. The CNR analysis further revealed the quantitative improvements of the reconstruction performed with the estimated parameters using the proposed method.

CONCLUSION

Apart from the computational cost, we concluded that the method was easy to implement and robust.

Estimating the Center of Rotation of Tomographic Imaging Systems with a Limited Number of Projections

For a tomographic imaging system, image reconstruction quality is dependent on the accurate determination of coordinates for the true center of rotation (COR). A significant COR offset error may introduce ringing, streaking, or other artifacts, while smaller error in determining COR may blur the reconstructed image. Well known COR correction techniques including image registration, center of mass calculation, or reconstruction evaluation work well under certain conditions. However, many of these methods do not consider various real-world cases such as a tilted sensor or non-parallel projections. Furthermore, a limited number of projections introduces stripe artifacts into the image reconstruction that interfere with many of these classic COR correction techniques. In this paper, we propose a revised variance-based algorithm to find the correct COR position automatically prior to tomographic reconstruction. The algorithm was tested on both simulated phantoms and acquired datasets, and our results show improved reconstruction accuracy.

C1 Posterior Arch Flare Point: A Useful Landmark for Fluoroscopically Guided C1-2 Puncture

BACKGROUND AND PURPOSE

The C1-2 intrathecal puncture is routinely performed when lumbar puncture is not feasible. Usage has steadily decreased in part because of the perceived high risk of injury to the cervical cord. Up to this point, vague fluoroscopic guidelines have been used, creating uncertainty about the actual needle location relative to the spinal cord. We present a novel osseous landmark to aid in C1-2 intrathecal puncture, corresponding to the posterior spinal cord margin on lateral fluoroscopic views. This landmark, which we have termed the "flare point," represents the triangular "flaring" of the posterior C1 arch at its junction with the anterior arch.

MATERIALS AND METHODS

Cervical spine CT myelograms were reviewed. High-resolution axial images were reformatted into the sagittal plane, and maximum-intensity-projection images were created to simulate a lateral fluoroscopic view. Tangential lines were drawn along the superior cortices of the anterior and posterior C1 arches, with the point of intersection used to approximate the flare point. Chart review was performed for all C1-2 punctures using the flare point technique in the past 3 years.

RESULTS

Forty-two cervical myelograms were reviewed. The average flare point was 0.2 ± 0.5 mm posterior to the dorsal spinal cord margin. In 37/42 subjects, the flare point was localized posterior to the spinal cord. Targeting by means of the flare point was used in 16 C1-2 punctures without complications.

CONCLUSIONS

The C1 posterior arch flare point accurately approximates the dorsal spinal cord margin on myelography. Targeting between the flare point and the spinolaminar line, at the mid-C1-2 interspace, allows safe and optimal needle positioning.

Upper bound dose values for meson radiation in heavy-ion therapy

Radiation treatment of cancer has evolved to include massive particle beams, instead of traditional irradiation procedures. Thus, patient doses and worker radiological protection have become issues of constant concern in the use of these new technologies, especially for proton- and heavy-ion-therapy. In the beam energies of interest of heavy-ion-therapy, secondary particle radiation comes from proton, neutron, and neutral and charged pions produced in the nuclear collisions of the beam with human tissue atoms. This work, for the first time, offers the upper bound of meson radiation dose in organic tissues due to secondary meson radiation in heavy-ion therapy. A model based on intranuclear collision has been used to follow in time the nuclear reaction and to determine the secondary radiation due to the meson yield produced in the beam interaction with nuclei in the tissue-equivalent media and water. The multiplicity, energy spectrum, and angular distribution of these pions, as well as their decay products, have been calculated in different scenarios for the nuclear reaction mechanism. The results of the produced secondary meson particles has been used to estimate the energy deposited in tissue using a cylindrical phantom by a transport Monte Carlo simulation and we have concluded that these mesons contribute at most 0.1% of the total prescribed dose.