Deformable 3D/3D CT-to-digital-tomosynthesis image registration in image-guided bronchoscopy interventions.

Traditional navigational bronchoscopy procedures rely on preprocedural computed tomography (CT) and intraoperative chest radiography and cone-beam CT (CBCT) to biopsy peripheral lung lesions. This navigational approach is challenging due to the projective nature of radiography, and the high radiation dose, long imaging time, and large footprints of CBCT. Digital tomosynthesis (DTS) is considered an attractive alternative combining the advantages of radiography and CBCT. Only the depth resolution cannot match a full CBCT image due to the limited angle acquisition. To address this issue, preoperative CT is a good auxiliary in guiding bronchoscopy interventions. Nevertheless, CT-to-body divergence caused by anatomic changes and respiratory motion, hinders the effective use of CT imaging. To mitigate CT-to-body divergence, we propose a novel deformable 3D/3D CT-to-DTS registration algorithm employing a multistage, multiresolution approach and using affine and elastic B-spline transformation models with bone and lung mask images. A multiresolution strategy with a Gaussian image pyramid and a multigrid strategy within the B-spline model are applied. The normalized correlation coefficient is included in the cost function for the affine model and a multimetric weighted cost function is used for the B-spline model, with weights determined heuristically. Tested on simulated and real patient bronchoscopy data, the algorithm yields promising results. Assessed qualitatively by visual inspection and quantitatively by computing the Dice coefficient (DC) and the average symmetric surface distance (ASSD), the algorithm achieves mean DC of 0.82±0.05 and 0.74±0.05, and mean ASSD of 0.65±0.29mm and 0.93±0.43mm for simulated and real data, respectively. This algorithm lays the groundwork for CT-aided intraoperative DTS imaging in image-guided bronchoscopy interventions with future studies focusing on automated metric weight setting.

AAPM Task Group Report 238: 3D C-arms with volumetric imaging capability.

This report reviews the image acquisition and reconstruction characteristics of C-arm Cone Beam Computed Tomography (C-arm CBCT) systems and provides guidance on quality control of C-arm systems with this volumetric imaging capability. The concepts of 3D image reconstruction, geometric calibration, image quality, and dosimetry covered in this report are also pertinent to CBCT for Image-Guided Radiation Therapy (IGRT). However, IGRT systems introduce a number of additional considerations, such as geometric alignment of the imaging at treatment isocenter, which are beyond the scope of the charge to the task group and the report. Section 1 provides an introduction to C-arm CBCT systems and reviews a variety of clinical applications. Section 2 briefly presents nomenclature specific or unique to these systems. A short review of C-arm fluoroscopy quality control (QC) in relation to 3D C-arm imaging is given in Section 3. Section 4 discusses system calibration, including geometric calibration and uniformity calibration. A review of the unique approaches and challenges to 3D reconstruction of data sets acquired by C-arm CBCT systems is give in Section 5. Sections 6 and 7 go in greater depth to address the performance assessment of C-arm CBCT units. First, Section 6 describes testing approaches and phantoms that may be used to evaluate image quality (spatial resolution and image noise and artifacts) and identifies several factors that affect image quality. Section 7 describes both free-in-air and in-phantom approaches to evaluating radiation dose indices. The methodologies described for assessing image quality and radiation dose may be used for annual constancy assessment and comparisons among different systems to help medical physicists determine when a system is not operating as expected. Baseline measurements taken either at installation or after a full preventative maintenance service call can also provide valuable data to help determine whether the performance of the system is acceptable. Collecting image quality and radiation dose data on existing phantoms used for CT image quality and radiation dose assessment, or on newly developed phantoms, will inform the development of performance criteria and standards. Phantom images are also useful for identifying and evaluating artifacts. In particular, comparing baseline data with those from current phantom images can reveal the need for system calibration before image artifacts are detected in clinical practice. Examples of artifacts are provided in Sections 4, 5, and 6.

Bayesian optimization to design a novel x-ray shaping device.

PURPOSE: In radiation therapy, x-ray dose must be precisely sculpted to the tumor, while simultaneously avoiding surrounding organs at risk. This requires modulation of x-ray intensity in space and/or time. Typically, this is achieved using a multi leaf collimator (MLC)-a complex mechatronic device comprising over one hundred individually powered tungsten 'leaves' that move in or out of the radiation field as required. Here, an all-electronic x-ray collimation concept with no moving parts is presented, termed "SPHINX": Scanning Pencil-beam High-speed Intensity-modulated X-ray source. SPHINX utilizes a spatially distributed bremsstrahlung target and collimator array in conjunction with magnetic scanning of a high energy electron beam to generate a plurality of small x-ray "beamlets." METHODS: A simulation framework was developed in Topas Monte Carlo incorporating a phase space electron source, transport through user defined magnetic fields, bremsstrahlung x-ray production, transport through a SPHINX collimator, and dose in water. This framework was completely parametric, meaning a simulation could be built and run for any supplied geometric parameters. This functionality was coupled with Bayesian optimization to find the best parameter set based on an objective function which included terms to maximize dose rate for a user defined beamlet width while constraining inter-channel cross talk and electron contamination. Designs for beamlet widths of 5, 7, and 10 mm2 were generated. Each optimization was run for 300 iterations and took approximately 40 h on a 24-core computer. For the optimized 7-mm model, a simulation of all beamlets in water was carried out including a linear scanning magnet calibration simulation. Finally, a back-of-envelope dose rate formalism was developed and used to estimate dose rate under various conditions. RESULTS: The optimized 5-, 7-, and 10-mm models had beamlet widths of 5.1 , 7.2 , and 10.1 mm2 and dose rates of 3574, 6351, and 10 015 Gy/C, respectively. The reduction in dose rate for smaller beamlet widths is a result of both increased collimation and source occlusion. For the simulation of all beamlets in water, the scanning magnet calibration reduced the offset between the collimator channels and beam centroids from 2.9 ±1.9 mm to 0.01 ±0.03 mm. A slight reduction in dose rate of approximately 2% per degree of scanning angle was observed. Based on a back-of-envelope dose rate formalism, SPHINX in conjunction with next-generation linear accelerators has the potential to achieve substantially higher dose rates than conventional MLC-based delivery, with delivery of an intensity modulated 100 × 100 mm2 field achievable in 0.9 to 10.6 s depending on the beamlet widths used. CONCLUSIONS: Bayesian optimization was coupled with Monte Carlo modeling to generate SPHINX geometries for various beamlet widths. A complete Monte Carlo simulation for one of these designs was developed, including electron beam transport of all beamlets through scanning magnets, x-ray production and collimation, and dose in water. These results demonstrate that SPHINX is a promising candidate for sculpting radiation dose with no moving parts, and has the potential to vastly improve both the speed and robustness of radiotherapy delivery. A multi-beam SPHINX system may be a candidate for delivering magavoltage FLASH RT in humans.

Science and practice of imaging physics through 50 years of SPIE Medical Imaging conferences.

Purpose: For 50 years now, SPIE Medical Imaging (MI) conferences have been the premier forum for disseminating and sharing new ideas, technologies, and concepts on the physics of MI. Approach: Our overarching objective is to demonstrate and highlight the major trajectories of imaging physics and how they are informed by the community and science present and presented at SPIE MI conferences from its inception to now. Results: These contributions range from the development of image science, image quality metrology, and image reconstruction to digital x-ray detectors that have revolutionized MI modalities including radiography, mammography, fluoroscopy, tomosynthesis, and computed tomography (CT). Recent advances in detector technology such as photon-counting detectors continue to enable new capabilities in MI. Conclusion: As we celebrate the past 50 years, we are also excited about what the next 50 years of SPIE MI will bring to the physics of MI.

Robust learning-based x-ray image denoising-potential pitfalls, their analysis and solutions.

Purpose:Since guidance based on x-ray imaging is an integral part of interventional procedures, continuous efforts are taken towards reducing the exposure of patients and clinical staff to ionizing radiation. Even though a reduction in the x-ray dose may lower associated radiation risks, it is likely to impair the quality of the acquired images, potentially making it more difficult for physicians to carry out their procedures.Method:We present a robust learning-based denoising strategy involving model-based simulations of low-dose x-ray images during the training phase. The method also utilizes a data-driven normalization step-based on an x-ray imaging model-to stabilize the mixed signal-dependent noise associated with x-ray images. We thoroughly analyze the method's sensitivity to a mismatch in dose levels used for training and application. We also study the impact of differing noise models used when training for low and very low-dose x-ray images on the denoising results.Results:A quantitative and qualitative analysis based on acquired phantom and clinical data has shown that the proposed learning-based strategy is stable across different dose levels and yields excellent denoising results, if an accurate noise model is applied. We also found that there can be severe artifacts when the noise characteristics of the training images are significantly different from those in the actual images to be processed. This problem can be especially acute at very low dose levels. During a thorough analysis of our experimental results, we further discovered that viewing the results from the perspective of denoising via thresholding of sub-band coefficients can be very beneficial to get a better understanding of the proposed learning-based denoising strategy.Conclusion:The proposed learning-based denoising strategy provides scope for significant x-ray dose reduction without the loss of important image information if the characteristics of noise is accurately accounted for during the training phase.

Rigid and Non-Rigid Motion Compensation in Weight-Bearing CBCT of the Knee Using Simulated Inertial Measurements.

OBJECTIVE: Involuntary subject motion is the main source of artifacts in weight-bearing cone-beam CT of the knee. To achieve image quality for clinical diagnosis, the motion needs to be compensated. We propose to use inertial measurement units (IMUs) attached to the leg for motion estimation. METHODS: We perform a simulation study using real motion recorded with an optical tracking system. Three IMU-based correction approaches are evaluated, namely rigid motion correction, non-rigid 2D projection deformation and non-rigid 3D dynamic reconstruction. We present an initialization process based on the system geometry. With an IMU noise simulation, we investigate the applicability of the proposed methods in real applications. RESULTS: All proposed IMU-based approaches correct motion at least as good as a state-of-the-art marker-based approach. The structural similarity index and the root mean squared error between motion-free and motion corrected volumes are improved by 24-35% and 78-85%, respectively, compared with the uncorrected case. The noise analysis shows that the noise levels of commercially available IMUs need to be improved by a factor of 105 which is currently only achieved by specialized hardware not robust enough for the application. CONCLUSION: Our simulation study confirms the feasibility of this novel approach and defines improvements necessary for a real application. SIGNIFICANCE: The presented work lays the foundation for IMU-based motion compensation in cone-beam CT of the knee and creates valuable insights for future developments.

Flat-panel conebeam CT in the clinic: history and current state.

Research into conebeam CT concepts began as soon as the first clinical single-slice CT scanner was conceived. Early implementations of conebeam CT in the 1980s focused on high-contrast applications where concurrent high resolution ( < 200 µ m ), for visualization of small contrast-filled vessels, bones, or teeth, was an imaging requirement that could not be met by the contemporaneous CT scanners. However, the use of nonlinear imagers, e.g., x-ray image intensifiers, limited the clinical utility of the earliest diagnostic conebeam CT systems. The development of consumer-electronics large-area displays provided a technical foundation that was leveraged in the 1990s to first produce large-area digital x-ray detectors for use in radiography and then compact flat panels suitable for high-resolution and high-frame-rate conebeam CT. In this review, we show the concurrent evolution of digital flat panel (DFP) technology and clinical conebeam CT. We give a brief summary of conebeam CT reconstruction, followed by a brief review of the correction approaches for DFP-specific artifacts. The historical development and current status of flat-panel conebeam CT in four clinical areas-breast, fixed C-arm, image-guided radiation therapy, and extremity/head-is presented. Advances in DFP technology over the past two decades have led to improved visualization of high-contrast, high-resolution clinical tasks, and image quality now approaches the soft-tissue contrast resolution that is the standard in clinical CT. Future technical developments in DFPs will enable an even broader range of clinical applications; research in the arena of flat-panel CT shows no signs of slowing down.

Special Section Guest Editorial: Computed tomography (CT) at 50 years.

Guest editors Patrick La Riviere, Rebecca Fahrig, and Norbert Pelc introduce the JMI Special Section Celebrating X-Ray Computed Tomography at 50.

3D Non-Rigid Alignment of Low-Dose Scans Allows to Correct for Saturation in Lower Extremity Cone-Beam CT.

Detector saturation in cone-beam computed tomography occurs when an object of highly varying shape and material composition is imaged using an automatic exposure control (AEC) system. When imaging a subject's knees, high beam energy ensures the visibility of internal structures but leads to overexposure in less dense border regions. In this work, we propose to use an additional low-dose scan to correct the saturation artifacts of AEC scans. Overexposed pixels are identified in the projection images of the AEC scan using histogram-based thresholding. The saturation-free pixels from the AEC scan are combined with the skin border pixels of the low-dose scan prior to volumetric reconstruction. To compensate for patient motion between the two scans, a 3D non-rigid alignment of the projection images in a backward-forward-projection process based on fiducial marker positions is proposed. On numerical simulations, the projection combination improved the structural similarity index measure from 0.883 to 0.999. Further evaluations were performed on two in vivo subject knee acquisitions, one without and one with motion between the AEC and low-dose scans. Saturation-free reference images were acquired using a beam attenuator. The proposed method could qualitatively restore the information of peripheral tissue structures. Applying the 3D non-rigid alignment made it possible to use the projection images with inter-scan subject motion for projection image combination. The increase in radiation exposure due to the additional low-dose scan was found to be negligibly low. The presented methods allow simple but effective correction of saturation artifacts.

X-Ray Scatter Estimation Using Deep Splines.

X-ray scatter compensation is a very desirable technique in flat-panel X-ray imaging and cone-beam computed tomography. State-of-the-art U-net based scatter removal approaches yielded promising results. However, as there are no physics' constraints applied to the output of the U-Net, it cannot be ruled out that it yields spurious results. Unfortunately, in the context of medical imaging, those may be misleading and could lead to wrong conclusions. To overcome this problem, we propose to embed B-splines as a known operator into neural networks. This inherently constrains their predictions to well-behaved and smooth functions. In a study using synthetic head and thorax data as well as real thorax phantom data, we found that our approach performed on par with U-net when comparing both algorithms based on quantitative performance metrics. However, our approach not only reduces runtime and parameter complexity, but we also found it much more robust to unseen noise levels. While the U-net responded with visible artifacts, the proposed approach preserved the X-ray signal's frequency characteristics.

Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images.

In this study, we propose a novel point cloud based 3D registration and segmentation framework using reinforcement learning. An artificial agent, implemented as a distinct actor based on value networks, is trained to predict the optimal piece-wise linear transformation of a point cloud for the joint tasks of registration and segmentation. The actor network estimates a set of plausible actions and the value network aims to select the optimal action for the current observation. Point-wise features that comprise spatial positions (and surface normal vectors in the case of structured meshes), and their corresponding image features, are used to encode the observation and represent the underlying 3D volume. The actor and value networks are applied iteratively to estimate a sequence of transformations that enable accurate delineation of object boundaries. The proposed approach was extensively evaluated in both segmentation and registration tasks using a variety of challenging clinical datasets. Our method has fewer trainable parameters and lower computational complexity compared to the 3D U-Net, and it is independent of the volume resolution. We show that the proposed method is applicable to mono- and multi-modal segmentation tasks, achieving significant improvements over the state-of-the-art for the latter. The flexibility of the proposed framework is further demonstrated for a multi-modal registration application. As we learn to predict actions rather than a target, the proposed method is more robust compared to the 3D U-Net when dealing with previously unseen datasets, acquired using different protocols or modalities. As a result, the proposed method provides a promising multi-purpose segmentation and registration framework, particular in the context of image-guided interventions.

Magnetic modeling of actively shielded rotating MRI magnets in the presence of environmental steel.

Rotating MRI systems could enable novel integrated medical devices such as MRI-Linacs, MRI-xray-angiography systems, and MRI-proton therapy systems. This work aimed to investigate the feasibility of rotating actively shielded superconducting MRI magnets in the presence of environmental steel-in particular, construction steel in the floor of the installation site. Two magnets were investigated: a 1.0 T split bore magnet, and a 1.5 T closed bore magnet. Each magnet was scaled to emulate field strengths of 0.5, 1.0, and 1.5 T. Finite Element Modeling was used to simulate these magnets in the presence of a 3 × 4 m steel plate located 1250 mm or 1400 mm below the isocenter. There are two possible rotation directions: around the longitudinal (z) axis or around the transverse (x) axis. Each model was solved for rotation angles between 0 and 360° in 30° intervals around each of these axes. For each simulation, a 300 mm DSV was extracted and decomposed into spherical harmonics. For the closed-bore magnet, total induced perturbation for the zero degree rotation angle was 223, 432, and 562 µT peak-to-peak (pk-pk) for the 0.5, 1.0, and 1.5 T models respectively (steel at 1250 mm). For the split-bore magnet, the same numbers were 1477, 16747, and 1766 µT. The substantially higher perturbation for the split-bore magnet can be traced to its larger fringe field. For rotation around the z-axis, total perturbation does not change as a function of angle but is exchanged between different harmonics. For rotation around the x-axis, total perturbation is different at each rotation angle. For the closed bore magnet, maximum perturbations occurred for a 90° rotation around the transverse axis. For the split-bore magnet, the opposite was observed, with the same 90° rotation yielding total perturbation lower than the conventional position. In all cases, at least 95% of the total perturbation was composed of 1st and 2nd order harmonics. The presence of environmental steel poses a major challenge to the realization of an actively shielded rotating superconducting MRI system, requiring some novel form of shimming. Possible shimming strategies are discussed at length.

XDose: toward online cross-validation of experimental and computational X-ray dose estimation.

PURPOSE: As the spectrum of X-ray procedures has increased both for diagnostic and for interventional cases, more attention is paid to X-ray dose management. While the medical benefit to the patient outweighs the risk of radiation injuries in almost all cases, reproducible studies on organ dose values help to plan preventive measures helping both patient as well as staff. Dose studies are either carried out retrospectively, experimentally using anthropomorphic phantoms, or computationally. When performed experimentally, it is helpful to combine them with simulations validating the measurements. In this paper, we show how such a dose simulation method, carried out together with actual X-ray experiments, can be realized to obtain reliable organ dose values efficiently. METHODS: A Monte Carlo simulation technique was developed combining down-sampling and super-resolution techniques for accelerated processing accompanying X-ray dose measurements. The target volume is down-sampled using the statistical mode first. The estimated dose distribution is then up-sampled using guided filtering and the high-resolution target volume as guidance image. Second, we present a comparison of dose estimates calculated with our Monte Carlo code experimentally obtained values for an anthropomorphic phantom using metal oxide semiconductor field effect transistor dosimeters. RESULTS: We reconstructed high-resolution dose distributions from coarse ones (down-sampling factor 2 to 16) with error rates ranging from 1.62 % to 4.91 %. Using down-sampled target volumes further reduced the computation time by 30 % to 60 %. Comparison of measured results to simulated dose values demonstrated high agreement with an average percentage error of under [Formula: see text] for all measurement points. CONCLUSIONS: Our results indicate that Monte Carlo methods can be accelerated hardware-independently and still yield reliable results. This facilitates empirical dose studies that make use of online Monte Carlo simulations to easily cross-validate dose estimates on-site.

Data-driven estimation of noise variance stabilization parameters for low-dose x-ray images.

PURPOSE: Denoising x-ray images corrupted by signal-dependent mixed noise is usually approached either by considering noise statistics directly or by using noise variance stabilization (NVS) techniques. An advantage of the latter is that the noise variance can be stabilized to a known constant throughout the image, facilitating the application of denoising algorithms designed for the removal of additive Gaussian noise. A well-performing NVS is the generalized Anscombe transform (GAT). To calculate the GAT, the system gain as well as the variance of electronic noise are required. Unfortunately, these parameters are difficult to predict from the x-ray tube settings in clinical practice, because the system gain observed at the detector depends on the beam hardening caused by the patient. MATERIALS AND METHODS: We propose a data-driven method for estimating the parameters required to carry out an NVS using the GAT. It utilizes the energy compaction property of the discrete cosine transform to obtain the NVS parameters using a robust regression approach relying on a linear Poisson-Gaussian model. The method has been experimentally validated with respect to beam hardening as well as denoising performance for different dose and scatter levels. RESULTS: Across a range of low-dose x-ray settings, the proposed robust regression approach has estimated both system gain and electronic noise level with an average error of only 4.2%. When used to perform a GAT followed by the denoising of low-dose x-ray images, performance gains of 5% for peak-signal-to-noise ratio and 4% for structural similarity index can be obtained. CONCLUSION: The parameters needed to calculate the GAT can be estimated efficiently and robustly using a data-driven approach. The improved parameter estimation method facilitates a more accurate GAT-based NVS and, hence, better denoising of low-dose x-ray images when algorithms designed for additive Gaussian noise are applied.

Corrigendum to "Intraoperative Imaging Modalities and Compensation for Brain Shift in Tumor Resection Surgery".

[This corrects the article DOI: 10.1155/2017/6028645.].

Physics-driven learning of x-ray skin dose distribution in interventional procedures.

PURPOSE: Radiation doses accumulated during very complicated image-guided x-ray procedures have the potential to cause stochastic, but also deterministic effects, such as skin rashes or even hair loss. To monitor and reduce radiation-related risks to patients' skin, x-ray imaging devices are equipped with online air kerma monitoring components. Traditionally, such measurements have been used to estimate skin entrance dose by (a) estimating air kerma at the interventional reference point (IRP), (b) forward projecting the dose distribution, and (c) considering a backscatter factor among other correction factors. Unfortunately, the complicated interaction between incident x-ray photons, secondary electrons, and skin tissue cannot be properly accounted for by assuming a linear relationship between forward projected air kerma and a backscatter factor. Gold standard skin dose models are therefore determined using Monte Carlo (MC) techniques. However, MC simulations are computationally complex in general and possible acceleration mainly depends on the employed hardware and variance reduction techniques. To obtain reliable and fast dose estimates, we propose to combine MC-based simulations with learning-based methods. METHODS: The basic idea of our method is to approximate the radiation physics to calculate a first-order exposure estimate quickly. This initial estimate is then refined using prior knowledge derived from MC simulations. To this end, the primary photon propagation inside a voxelized patient model is estimated using a less accurate but fast photon ray casting (RC) simulation based on the Beer-Lambert law. The results of the RC simulation are then fed into a convolutional neural network (CNN), which maps the propagation of primary photons to the dose deposition inside the patient model. Additionally, the patient model itself including anatomy and material properties, such as mass density and mass energy-absorption coefficients, are fed into the CNN as well. The CNN is trained using smoothed results of MC simulations as output and RC simulations of identical imaging settings and patient models as input. RESULTS: In total, 163 MC and associated RC simulations are carried out for the head, thorax, abdomen, and pelvis in three different voxel phantoms. We used 10 8 or 10 9 primarily emitted photons sampled from a 125 kV peak voltage spectrum, respectively. Edge-preserving smoothing (EPS) is applied to reduce (a) general stochastic uncertainties and (b) stochastic uncertainty concerning MC simulations of less primary photons. The CNN is trained using seven imaging settings of the abdomen in a single phantom. Testing its performance on the remaining datasets, the CNN is capable of estimating skin dose with an error of below 10% for the majority of test cases. CONCLUSION: The combination of deep neural networks and MC simulation of particle physics has the potential to decrease the computational complexity of accurate skin dose estimation. The proposed approach can provide dose distributions in under one second when running on high-end hardware. On lower cost hardware, it took up to 2 min to arrive at the same result. This makes our approach applicable in high-end environments as well as in budget solutions. Furthermore, the number of primary photons only affects the training time, while the execution time is independent of the number of primary photons.

Pitfalls in interventional X-ray organ dose assessment-combined experimental and computational phantom study: application to prostatic artery embolization.

PURPOSE: With X-ray radiation protection and dose management constantly gaining interest in interventional radiology, novel procedures often undergo prospective dose studies using anthropomorphic phantoms to determine expected reference organ-equivalent dose values. Due to inherent uncertainties, such as impact of exact patient positioning, generalized geometry of the phantoms, limited dosimeter positioning options, and composition of tissue-equivalent materials, these dose values might not allow for patient-specific risk assessment. Therefore, first the aim of this study is to quantify the influence of these parameters on local X-ray dose to evaluate their relevance in the assessment of patient-specific organ doses. Second, this knowledge further enables validating a simulation approach, which allows employing physiological material models and patient-specific geometries. METHODS: Phantom dosimetry experiments using MOSFET dosimeters were conducted reproducing imaging scenarios in prostatic arterial embolization (PAE). Associated organ-equivalent dose of prostate, bladder, colon, and skin was determined. Dose deviation induced by possible small displacements of the patient was reproduced by moving the X-ray source. Dose deviation induced by geometric and material differences was investigated by analyzing two different commonly used phantoms. We reconstructed the experiments using Monte Carlo (MC) simulations, a reference male geometry, and different material properties to validate simulations and experiments against each other. RESULTS: Overall, MC-simulated organ dose values are in accordance with the measured ones for the majority of cases. Marginal displacements of X-ray source relative to the phantoms lead to deviations of 6-135% in organ dose values, while skin dose remains relatively constant. Regarding the impact of phantom material composition, underestimation of internal organ dose values by 12-20% is prevalent in all simulated phantoms. Skin dose, however, can be estimated with low deviation of 1-8% at least for two materials. CONCLUSIONS: Prospective reference dose studies might not extend to precise patient-specific dose assessment. Therefore, online organ dose assessment tools, based on advanced patient modeling and MC methods, are desirable.

Pulse pileup analysis for a double-sided silicon strip detector using variable pulse shapes.

Due to pulse pileup, photon counting detectors (PCDs) suffer from count loss and energy distortion when operating in high count rate environments. In this paper, we studied the pulse pileup of a double-sided silicon strip detector (DSSSD) to evaluate its potential application in a mammography system. We analyzed the pulse pileup using pulses of varied shapes, where the shape of the pulse depends on the location of photon interaction within the detector. To obtain the shaped pulses, first, transient currents for photons interacting at different locations were simulated using a Technology Computer-Aided Design (TCAD) software. Next, the currents were shaped by a CR-RC2 shaping circuit, calculated using Simulink. After obtaining these pulses, both the different orders of pileup and the energy spectrum were calculated by taking into account the following two factors: 1) spatial distribution of photon interactions within the detector, and 2) time interval distribution between successive photons under a given photon flux. We found that for a DSSSD with thickness of 300 µm, pitch of 25 µm and strip length of 1 cm, under a bias voltage of 50 V, the variable pulse shape model predicts the fraction free of pileup can be > 90 % under a photon flux of 3.75 Mcps/mm2. The double-sided silicon-strip detector is a promising candidate for digital mammography applications.

Registration of vascular structures using a hybrid mixture model.

PURPOSE: Morphological changes to anatomy resulting from invasive surgical procedures or pathology, typically alter the surrounding vasculature. This makes it useful as a descriptor for feature-driven image registration in various clinical applications. However, registration of vasculature remains challenging, as vessels often differ in size and shape, and may even miss branches, due to surgical interventions or pathological changes. Furthermore, existing vessel registration methods are typically designed for a specific application. To address this limitation, we propose a generic vessel registration approach useful for a variety of clinical applications, involving different anatomical regions. METHODS: A probabilistic registration framework based on a hybrid mixture model, with a refinement mechanism to identify missing branches (denoted as HdMM+) during vasculature matching, is introduced. Vascular structures are represented as 6-dimensional hybrid point sets comprising spatial positions and centerline orientations, using Student's t-distributions to model the former and Watson distributions for the latter. RESULTS: The proposed framework is evaluated for intraoperative brain shift compensation, and monitoring changes in pulmonary vasculature resulting from chronic lung disease. Registration accuracy is validated using both synthetic and patient data. Our results demonstrate, HdMM+ is able to reduce more than [Formula: see text] of the initial error for both applications, and outperforms the state-of-the-art point-based registration methods such as coherent point drift and Student's t-distribution mixture model, in terms of mean surface distance, modified Hausdorff distance, Dice and Jaccard scores. CONCLUSION: The proposed registration framework models complex vascular structures using a hybrid representation of vessel centerlines, and accommodates intricate variations in vascular morphology. Furthermore, it is generic and flexible in its design, enabling its use in a variety of clinical applications.

Preliminary results of DSA denoising based on a weighted low-rank approach using an advanced neurovascular replication system.

PURPOSE: 2D digital subtraction angiography (DSA) has become an important technique for interventional neuroradiology tasks, such as detection and subsequent treatment of aneurysms. In order to provide high-quality DSA images, usually undiluted contrast agent and a high X-ray dose are used. The iodinated contrast agent puts a burden on the patients' kidneys while the use of high-dose X-rays expose both patients and medical staff to a considerable amount of radiation. Unfortunately, reducing either the X-ray dose or the contrast agent concentration usually results in a sacrifice of image quality. MATERIALS AND METHODS: To denoise a frame, the proposed spatiotemporal denoising method utilizes the low-rank nature of a spatially aligned temporal sequence where variation is introduced by the flow of contrast agent through a vessel tree of interest. That is, a constrained weighted rank-1 approximation of the stack comprising the frame to be denoised and its temporal neighbors is computed where the weights are used to prevent the contribution of non-similar pixels toward the low-rank approximation. The method has been evaluated using a vascular flow phantom emulating cranial arteries into which contrast agent can be manually injected (Vascular Simulations Replicator, Vascular Simulations, Stony Brook NY, USA). For the evaluation, image sequences acquired at different dose levels as well as different contrast agent concentrations have been used. RESULTS: Qualitative and quantitative analyses have shown that with the proposed approach, the dose and the concentration of the contrast agent could both be reduced by about 75%, while maintaining the required image quality. Most importantly, it has been observed that the DSA images obtained using the proposed method have the closest resemblance to typical DSA images, i.e., they preserve the typical image characteristics best. CONCLUSION: Using the proposed denoising approach, it is possible to improve the image quality of low-dose DSA images. This improvement could enable both a reduction in contrast agent and radiation dose when acquiring DSA images, thereby benefiting patients as well as clinicians. Since the resulting images are free from artifacts and as the inherent characteristics of the images are also preserved, the proposed method seems to be well suited for clinical images as well.