Single-shot quantitative x-ray imaging using a primary modulator and dual-layer detector

BACKGROUND

Conventional x-ray imaging and fluoroscopy have limitations in quantitation due to several challenges, including scatter, beam hardening, and overlapping tissues. Dual-energy (DE) imaging, with its capability to quantify area density of specific materials, is well-suited to address such limitations, but only if the dual-energy projections are acquired with perfect spatial and temporal alignment and corrected for scatter.

PURPOSE

In this work, we propose single-shot quantitative imaging (SSQI) by combining the use of a primary modulator (PM) and dual-layer (DL) detector, which enables motion-free DE imaging with scatter correction in a single exposure.

METHODS

The key components of our SSQI setup include a PM and DL detector, where the former enables scatter correction for the latter while the latter enables beam hardening correction for the former. The SSQI algorithm allows simultaneous recovery of two material-specific images and two scatter images using four sub-measurements from the PM encoding. The concept was first demonstrated using simulation of chest x-ray imaging for a COVID patient. For validation, we set up SSQI geometry on our tabletop system and imaged acrylic and copper slabs with known thicknesses (acrylic: 0-22.5 cm; copper: 0-0.9 mm), estimated scatter with our SSQI algorithm, and compared the material decomposition (MD) for different combinations of the two materials with ground truth. Second, we imaged an anthropomorphic chest phantom containing contrast in the coronary arteries and compared the MD with and without SSQI. Lastly, to evaluate SSQI in dynamic applications, we constructed a flow phantom that enabled dynamic imaging of iodine contrast.

RESULTS

Our simulation study demonstrated that SSQI led to accurate scatter correction and MD, particularly for smaller focal blur and finer PM pitch. In the validation study, we found that the root mean squared error (RMSE) of SSQI estimation was 0.13 cm for acrylic and 0.04 mm for copper. For the anthropomorphic phantom, direct MD resulted in incorrect interpretation of contrast and soft tissue, while SSQI successfully distinguished them quantitatively, reducing RMSE in material-specific images by 38%-92%. For the flow phantom, SSQI was able to perform accurate dynamic quantitative imaging, separating contrast from the background.

CONCLUSIONS

We demonstrated the potential of SSQI for robust quantitative x-ray imaging. The integration of SSQI is straightforward with the addition of a PM and upgrade to a DL detector, which may enable its widespread adoption, including in techniques such as radiography and dynamic imaging (i.e., real-time image guidance and cone-beam CT).

Multi-energy blended CBCT spectral imaging and scatter-decoupled material decomposition using a spectral modulator with flying focal spot (SMFFS)

BACKGROUND

Cone-beam CT (CBCT) has been extensively employed in industrial and medical applications, such as image-guided radiotherapy and diagnostic imaging, with a growing demand for quantitative imaging using CBCT. However, conventional CBCT can be easily compromised by scatter and beam hardening artifacts, and the entanglement of scatter and spectral effects introduces additional complexity.

PURPOSE

The intertwined scatter and spectral effects within CBCT pose significant challenges to the quantitative performance of spectral imaging. In this work, we present the first attempt to develop a stationary spectral modulator with flying focal spot (SMFFS) technology as a promising, low-cost approach to accurately solving the x-ray scattering problem and physically enabling spectral imaging in a unified framework, and with no significant misalignment in data sampling of spectral projections.

METHODS

To deal with the intertwined scatter-spectral challenge, we propose a novel scatter-decoupled material decomposition (SDMD) method for SMFFS, which consists of four steps in total, including (1) spatial resolution-preserved and noise-suppressed multi-energy "residual" projection generation free from scatter, based on a hypothesis of scatter similarity; (2) first-pass material decomposition from the generated multi-energy residual projections in non-penumbra regions, with a structure similarity constraint to overcome the increased noise and penumbra effect; (3) scatter estimation for complete data; and (4) second-pass material decomposition for complete data by using a multi-material spectral correction method. Monte Carlo simulations of a pure-water cylinder phantom with different focal spot deflections are conducted to validate the scatter similarity hypothesis. Both numerical simulations using a clinical abdominal CT dataset, and physics experiments on a tabletop CBCT system using a Gammex multi-energy CT phantom and an anthropomorphic chest phantom, are carried out to demonstrate the feasibility of CBCT spectral imaging with SMFFS and our proposed SDMD method.

RESULTS

Monte Carlo simulations show that focal spot deflections within a range of 2 mm share quite similar scatter distributions overall. Numerical simulations demonstrate that SMFFS with SDMD method can achieve better material decomposition and CT number accuracy with fewer artifacts. In physics experiments, for the Gammex phantom, the average error of the mean values (E RMSE ROI $E^{\text{ROI}}_{\text{RMSE}}$ ) in selected regions of interest (ROIs) of virtual monochromatic image (VMI) at 70 keV is 8 HU in SMFFS cone-beam (CB) scan, and 19 and 210 HU in sequential 80/120 kVp (dual kVp, DKV) CB scan with and without scatter correction, respectively. For the chest phantom, theE RMSE ROI $E^{\text{ROI}}_{\text{RMSE}}$ in selected ROIs of VMIs is 12 HU for SMFFS CB scan, and 15 and 438 HU for sequential 80/140 kVp CB scan with and without scatter correction, respectively. Also, the non-uniformity among selected regions of the chest phantom is 14 HU for SMFFS CB scan, and 59 and 184 HU for the DKV CB scan with and without a traditional scatter correction method, respectively.

CONCLUSIONS

We propose a SDMD method for CBCT with SMFFS. Our preliminary results show that SMFFS can enable spectral imaging with simultaneous scatter correction for CBCT and effectively improve its quantitative imaging performance.

Empirical optimization of energy bin weights for compressing measurements with realistic photon counting x-ray detectors

BACKGROUND

Photon counting detectors (PCDs) provide higher spatial resolution, improved contrast-to-noise ratio (CNR), and energy discriminating capabilities. However, the greatly increased amount of projection data in photon counting computed tomography (PCCT) systems becomes challenging to transmit through the slip ring, process, and store.

PURPOSE

This study proposes and evaluates an empirical optimization algorithm to obtain optimal energy weights for energy bin data compression. This algorithm is universally applicable to spectral imaging tasks including 2 and 3 material decomposition (MD) tasks and virtual monoenergetic images (VMIs). This method is simple to implement while preserving spectral information for the full range of object thicknesses and is applicable to different PCDs, for example, silicon detectors and CdTe detectors.

METHODS

We used realistic detector energy response models to simulate the spectral response of different PCDs and an empirical calibration method to fit a semi-empirical forward model for each PCD. We numerically optimized the optimal energy weights by minimizing the average relative Cramér-Rao lower bound (CRLB) due to the energy-weighted bin compression, for MD and VMI tasks over a range of material area densityρ A , m ${\rho }_{A,m}$ (0-40 g/cm2 water, 0-2.16 g/cm2 calcium). We used Monte Carlo simulation of a step wedge phantom and an anthropomorphic head phantom to evaluate the performance of this energy bin compression method in the projection domain and image domain, respectively.

RESULTS

The results show that for 2 MD, the energy bin compression method can reduce PCCT data size by 75% and 60%, with an average variance penalty of less than 17% and 3% for silicon and CdTe detectors, respectively. For 3 MD tasks with a K-edge material (iodine), this method can reduce the data size by 62.5% and 40% with an average variance penalty of less than 12% and 13% for silicon and CdTe detectors, respectively.

CONCLUSIONS

We proposed an energy bin compression method that is broadly applicable to different PCCT systems and object sizes, with high data compression ratio and little loss of spectral information.

[Analysis of etiology and complications in children with stage 5 chronic kidney disease]

Objective: To investigate the etiology, complications, and prognostic factors of stage 5 chronic kidney disease (CKD5) in children. Methods: A case series study was conducted to retrospectively analyze the general situation, clinical manifestations, laboratory tests, genetic testing, and follow-up data (until October 2022) of 174 children with CKD5 who were diagnosed and hospitalized at the Children's Hospital of Chongqing Medical University from April 2012 to April 2021. The characteristics of complications in the children were compared based on age, gender, and etiology. Based on the presence or absence of left ventricular hypertrophy (LVH), patients were divided into LVH group and non LVH group for analyzing the influencing factors of cardiovascular disease. Patients were also divided into death group and survival group, peritoneal dialysis group and hemodialysis group based on the follow-up data for analyzing the prognostic factors. The chi-square test, independent sample t-test, Fisher exact probability test, Mann-Whitney U test and Kruskal Wallis test were used to analyze data among different groups. Multivariate Logistic regression analysis was used to identify the prognostic factors. Results: A total of 174 children with CKD5 were enrolled in the study (96 boys and 78 girls), aged 11.2 (8.2, 13.0) years. Congenital kidney and urinary tract malformations (CAKUT) were the most common causes of the CKD5 (84 cases, 48.3%), followed by glomerular diseases (83 cases, 47.7%), and among which 28 cases (16.1%) were hereditary glomerular diseases. The common complications of CKD5 included anemia (98.2%, 165/168), mineral and bone disorder in chronic kidney disease (CKD-MBD) (97.7%, 170/174), lipid metabolism disorders (87.5%, 63/72), hypertension (81.4%, 127/156) and LVH (57.6%,57/99). The incidences of hypertension in primary glomerular disease were higher than that in CAKUT(93.8%(30/32) vs.73.7%(56/76),χ2=5.59,P<0.05). The incidences of hypertension in secondary glomerular disease were higher than that in CAKUT and that in hereditary kidney disease (100.0%(20/20) vs. 73.7%(56/76), 68.2%(15/22), both P<0.05). The incidence of hypocalcemia in CAKUT, primary glomerular disease, and hereditary kidney disease was higher than that in secondary glomerular disease (82.1%(69/84), 88.2%(30/34), 89.3%(25/28) vs. 47.6%(10/21), χ2=10.21, 10.75, 10.80, all P=0.001); the incidence of secondary hyperparathyroidism in women was higher than that in men (80.0%(64/80) vs. 95.0%(57/60), χ2=6.58, P=0.010). The incidence of LVH in children aged 6-<12 was higher than that in children aged 12-18 (73.5%(25/34) vs. 43.1%(22/51), χ2=7.62, P=0.006). Among 113 follow-up children, the mortality rate was 39.8% (45/113). Compared to the survival group, the children in the death group had lower hemoglobin, higher blood pressure, lower albumin, lower alkaline phosphatase and higher left ventricular mass index ((67±19) vs. (75±20) g/L, 142 (126, 154) vs. 128(113, 145) mmHg(1 mmHg=0.133 kPa), (91±21) vs. (82±22) mmHg, 32 (26, 41) vs. 40 (31, 43) g/L, 151 (82, 214) vs. 215 (129, 37) U/L, 48 (38, 66) vs. 38(32, 50) g/m2.7,t=2.03, Z=2.89, t=2.70, Z=2.49, 2.79, 2.29,all P<0.05), but no independent risk factors were identified (all P>0.05). The peritoneal dialysis group had better alleviation for anemia, low calcium, and high phosphorus than the hemodialysis group ((87±22) vs. (72±16) g/L, (1.9±0.5) vs. (1.7±0.4) mmol/L, (2.2±0.7) vs. (2.8±0.9) mmol/L, t=2.92, 2.29, 2.82, all P<0.05), and the survival rate of the peritoneal dialysis group was significantly higher than that of the hemodialysis group (77.8% (28/36) vs. 48.4% (30/62), χ2=8.14, P=0.004). Conclusions: CAKUT is the most common etiology in children with CKD 5, and anemia is the most common complication. The incidence of complications in children with CKD 5 varies with age, gender and etiology. Anemia, hypertension, hypoalbuminemia, reduced alkaline phosphatase and elevated LVMI may be the prognostic factors in children with CKD5. Peritoneal dialysis may be more beneficial for improving the long-term survival rate.

Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects

Filtered back projection (FBP) has been the standard CT image reconstruction method for 4 decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in several clinical applications. However, with faster and more advanced CT scanners, FBP has become increasingly obsolete. Higher image noise and more artifacts are especially noticeable in lower-dose CT imaging using FBP. This performance gap was partly addressed by model-based iterative reconstruction (MBIR). Yet, its "plastic" image appearance and long reconstruction times have limited widespread application. Hybrid iterative reconstruction partially addressed these limitations by blending FBP with MBIR and is currently the state-of-the-art reconstruction technique. In the past 5 years, deep learning reconstruction (DLR) techniques have become increasingly popular. DLR uses artificial intelligence to reconstruct high-quality images from lower-dose CT faster than MBIR. However, the performance of DLR algorithms relies on the quality of data used for model training. Higher-quality training data will become available with photon-counting CT scanners. At the same time, spectral data would greatly benefit from the computational abilities of DLR. This review presents an overview of the principles, technical approaches, and clinical applications of DLR, including metal artifact reduction algorithms. In addition, emerging applications and prospects are discussed.

Contrast solution properties and scan parameters influence the apparent diffusivity of computed tomography contrast agents in articular cartilage

The inability to detect early degenerative changes to the articular cartilage surface that commonly precede bulk osteoarthritic degradation is an obstacle to early disease detection for research or clinical diagnosis. Leveraging a known artefact that blurs tissue boundaries in clinical arthrograms, contrast agent (CA) diffusivity can be derived from computed tomography arthrography (CTa) scans. We combined experimental and computational approaches to study protocol variations that may alter the CTa-derived apparent diffusivity. In experimental studies on bovine cartilage explants, we examined how CA dilution and transport direction (absorption versus desorption) influence the apparent diffusivity of untreated and enzymatically digested cartilage. Using multiphysics simulations, we examined mechanisms underlying experimental observations and the effects of image resolution, scan interval and early scan termination. The apparent diffusivity during absorption decreased with increasing CA concentration by an amount similar to the increase induced by tissue digestion. Models indicated that osmotically-induced fluid efflux strongly contributed to the concentration effect. Simulated changes to spatial resolution, scan spacing and total scan time all influenced the apparent diffusivity, indicating the importance of consistent protocols. With careful control of imaging protocols and interpretations guided by transport models, CTa-derived diffusivity offers promise as a biomarker for early degenerative changes.

Technical note: Evaluation of a V‐Net autosegmentation algorithm for pediatric CT scans: Performance, generalizability and application to patient‐specific CT dosimetry

PURPOSE

This study developed and evaluated a fully convolutional network (FCN) for pediatric CT organ segmentation and investigated the generalizability of the FCN across image heterogeneities such as CT scanner model protocols and patient age. We also evaluated the autosegmentation models as part of a software tool for patient-specific CT dose estimation.

METHODS

A collection of 359 pediatric CT datasets with expert organ contours were used for model development and evaluation. Autosegmentation models were trained for each organ using a modified FCN 3D V-Net. An independent test set of 60 patients was withheld for testing. To evaluate the impact of CT scanner model protocol and patient age heterogeneities, separate models were trained using a subset of scanner model protocols and pediatric age groups. Train and test sets were split to answer questions about the generalizability of pediatric FCN autosegmentation models to unseen age groups and scanner model protocols, as well as the merit of scanner model protocol or age-group-specific models. Finally, the organ contours resulting from the autosegmentation models were applied to patient-specific dose maps to evaluate the impact of segmentation errors on organ dose estimation.

RESULTS

Results demonstrate that the autosegmentation models generalize to CT scanner acquisition and reconstruction methods which were not present in the training dataset. While models are not equally generalizable across age groups, age-group-specific models do not hold any advantage over combining heterogeneous age groups into a single training set. Dice similarity coefficient (DSC) and mean surface distance results are presented for 19 organ structures, for example, median DSC of 0.52 (duodenum), 0.74 (pancreas), 0.92 (stomach), and 0.96 (heart). The FCN models achieve a mean dose error within 5% of expert segmentations for all 19 organs except for the spinal canal, where the mean error was 6.31%.

CONCLUSIONS

Overall, these results are promising for the adoption of FCN autosegmentation models for pediatric CT, including applications for patient-specific CT dose estimation.

Pediatric chest-abdomen-pelvis and abdomen-pelvis CT images with expert organ contours

PURPOSE

Organ autosegmentation efforts to date have largely been focused on adult populations, due to limited availability of pediatric training data. Pediatric patients may present additional challenges for organ segmentation. This paper describes a dataset of 359 pediatric chest-abdomen-pelvis and abdomen-pelvis CT images with expert contours of up to 29 anatomical organ structures to aid in the evaluation and development of autosegmentation algorithms for pediatric CT imaging.

ACQUISITION AND VALIDATION METHODS

The dataset collection consists of axial CT images in DICOM format of 180 male and 179 female pediatric chest-abdomen-pelvis or abdomen-pelvis exams acquired from one of three CT scanners at Children's Wisconsin. The datasets represent random pediatric cases based upon routine clinical indications. Subjects ranged in age from 5 days to 16 years, with a mean age of seven years. The CT acquisition, contrast, and reconstruction protocols varied across the scanner models and patients, with specifications available in the DICOM headers. Expert contours were manually labeled for up to 29 organ structures per subject. Not all contours are available for all subjects, due to limited field of view or unreliable contouring due to high noise.

DATA FORMAT AND USAGE NOTES

The data are available on TCIA (https://www.cancerimagingarchive.net/) under the collection Pediatric-CT-SEG. The axial CT image slices for each subject are available in DICOM format. The expert contours are stored in a single DICOM RTSTRUCT file for each subject. The contours are names as listed in Table 2.

POTENTIAL APPLICATIONS

This dataset will enable the evaluation and development of organ autosegmentation algorithms for pediatric populations, which exhibit variations in organ shape and size across age. Automated organ segmentation from CT images has numerous applications including radiation therapy, diagnostic tasks, surgical planning, and patient-specific organ dose estimation. This article is protected by copyright. All rights reserved.

Single-Shot Quantitative X-ray Imaging Using a Primary Modulator and Dual-Layer Detector: Simulation and Phantom Studies

Conventional x-ray imaging provides little quantitative information due to scatter, beam hardening, and overlaying tissues. A single-shot quantitative x-ray imaging (SSQI) method was previously developed to quantify material-specific densities in x-ray imaging by combining the use of a primary modulator (PM) and dual-layer (DL) detector. The feasibility of this concept was demonstrated with simulations using an iterative patch-based method. In this work, we propose a new algorithm pipeline for SSQI that enables accurate quantification and high computational efficiency. The DL images contain four measurements that are obtained behind the unattenuated and partially attenuated regions of the PM of each layer. Using the low-frequency property of scatter and a pre-calibrated material decomposition (MD), four unknowns (i.e., two scatter images and two material-specific images) are jointly recovered by directly solving four equations given by the four measurements. We tested this algorithm in simulations and further demonstrated its efficacy on chest phantom experiments. Through simulation, we show that the new method for MD is robust against scatter. Its performance improves with smaller PM pitch size and smaller focal spot blur. The RMSE in material-specific images compared to ground truth reduces by 52%-84% versus without scatter correction. For our experimental study, we successfully separated soft tissue and bone. The computational time for processing each view was ~8 s without optimization. The reported results further strengthen the potential of SSQI for widespread adoption, leading to quantitative imaging not only for x-ray imaging but also for real-time image guidance or cone-beam CT.

Upstream Machine Learning in Radiology

Machine learning (ML) and Artificial intelligence (AI) has the potential to dramatically improve radiology practice at multiple stages of the imaging pipeline. Most of the attention has been garnered by applications focused on improving the end of the pipeline: image interpretation. However, this article reviews how AI/ML can be applied to improve upstream components of the imaging pipeline, including exam modality selection, hardware design, exam protocol selection, data acquisition, image reconstruction, and image processing. A breadth of applications and their potential for impact is shown across multiple imaging modalities, including ultrasound, computed tomography, and MRI.

Deep learning-based reconstruction of interventional tools and devices from four X-ray projections for tomographic interventional guidance

PURPOSE

Image guidance for minimally invasive interventions is usually performed by acquiring fluoroscopic images using a monoplanar or a biplanar C-arm system. However, the projective data provide only limited information about the spatial structure and position of interventional tools and devices such as stents, guide wires, or coils. In this work, we propose a deep learning-based pipeline for real-time tomographic (four-dimensional [4D]) interventional guidance at conventional dose levels.

METHODS

Our pipeline is comprised of two steps. In the first one, interventional tools are extracted from four cone-beam CT projections using a deep convolutional neural network. These projections are then Feldkamp reconstructed and fed into a second network, which is trained to segment the interventional tools and devices in this highly undersampled reconstruction. Both networks are trained using simulated CT data and evaluated on both simulated data and C-arm cone-beam CT measurements of stents, coils, and guide wires.

RESULTS

The pipeline is capable of reconstructing interventional tools from only four X-ray projections without the need for a patient prior. At an isotropic voxel size of 100 μ m , our methods achieve a precision/recall within a 100 μ m environment of the ground truth of 93%/98%, 90%/71%, and 93%/76% for guide wires, stents, and coils, respectively.

CONCLUSIONS

A deep learning-based approach for 4D interventional guidance is able to overcome the drawbacks of today's interventional guidance by providing full spatiotemporal (4D) information about the interventional tools at dose levels comparable to conventional fluoroscopy.

Single-pass metal artifact reduction using a dual-layer flat panel detector

PURPOSE

Metal artifact remains a challenge in cone-beam CT images. Many image domain-based segmentation methods have been proposed for metal artifact reduction (MAR), which require two-pass reconstruction. Such methods first segment metal from a first-pass reconstruction and then forward-project the metal mask to identify them in projections. These methods work well in general but are limited when the metal is outside the scan field-of-view (FOV) or when the metal is moving during the scan. In the former, even reconstructing with a larger FOV does not guarantee a good estimate of metal location in the projections; and in the latter, the metal location in each projection is difficult to identify due to motion. Single-pass methods that detect metal in single-energy projections have also been developed, but often have imperfect metal detection that leads to residual artifacts. In this work, we develop a MAR method using a dual-layer (DL) flat panel detector, which improves performance for single-pass reconstruction.

METHODS

In this work, we directly detect metal objects in projections using dual-energy (DE) imaging that generates material-specific images (e.g., soft tissue and bone), where the metal stands out in bone images when nonuniform soft tissue background is removed. Metal is detected via simple thresholding, and entropy filtration is further applied to remove false-positive detections. A DL detector provides DE images with superior temporal and spatial registration and was used to perform the task. Scatter correction was first performed on DE raw projections to improve the accuracy of material decomposition. One phantom mimicking a liver biopsy setup and a cadaver head were used to evaluate the metal reduction performance of the proposed method and compared with that of a standard two-pass reconstruction, a previously published sinogram-based method using a Markov random field (MRF) model, and a single-pass projection-domain method using single-energy imaging. The phantom has a liver steering setup placed in a hollow chest phantom, with embedded metal and a biopsy needle crossing the phantom boundary. The cadaver head has dental fillings and a metal tag attached to its surface. The identified metal regions in each projection were corrected by interpolation using surrounding pixels, and the images were reconstructed using filtered backprojection.

RESULTS

Our current approach removes metal from the projections, which is robust to FOV truncation during imaging acquisition. In case of FOV truncation, the method outperformed the two-pass reconstruction method. The proposed method using DE renders better accuracy in metal segmentation than the MRF method and single-energy method, which were prone to false-positive errors that cause additional streaks. For the liver steering phantom, the average spatial nonuniformity was reduced from 0.127 in uncorrected images to 0.086 using a standard two-pass reconstruction and to 0.077 using the proposed method. For the cadaver head, the average standard deviation within selected soft tissue regions ( σ s ) was reduced from 209.1 HU in uncorrected images to 69.1 HU using a standard two-pass reconstruction and to 46.8 HU using our proposed method. The proposed method reduced the processing time by 31% as compared with the two-pass method.

CONCLUSIONS

We proposed a MAR method that directly detects metal in the projection domain using DE imaging, which is robust to truncation and superior to that of single-energy imaging. The method requires only a single-pass reconstruction that substantially reduces processing time compared with the standard two-pass metal reduction method.

Spectral Photon Counting CT: Imaging Algorithms and Performance Assessment

Photon counting x-ray detectors (PCDs) with spectral capabilities have the potential to revolutionize computed tomography (CT) for medical imaging. The ideal PCD provides accurate energy information for each incident x-ray, and at high spatial resolution. This information enables material-specific imaging, enhanced radiation dose efficiency, and improved spatial resolution in CT images. In practice, PCDs are affected by non-idealities, including limited energy resolution, pulse pileup, and cross talk due to charge sharing, K-fluorescence, and Compton scattering. In order to maximize their performance, PCDs must be carefully designed to reduce these effects and then later account for them during correction and post-acquisition steps. This review article examines algorithms for using PCDs in spectral CT applications, including how non-idealities impact image quality. Performance assessment metrics that account for spatial resolution and noise such as the detective quantum efficiency (DQE) can be used to compare different PCD designs, as well as compare PCDs with conventional energy integrating detectors (EIDs). These methods play an important role in enhancing spectral CT images and assessing the overall performance of PCDs.

Characterization of x-ray focal spots using a rotating edge

Purpose: The focal spot size and shape of an x-ray system are critical factors to the spatial resolution. Conventional approaches to characterizing the focal spot use specialized tools that usually require careful calibration. We propose an alternative to characterize the x-ray source's focal spot, simply using a rotating edge and flat-panel detector. Methods: An edge is moved to the beam axis, and an edge spread function (ESF) is obtained at a specific angle. Taking the derivative of the ESF provides the line spread function, which is the Radon transform of the focal spot in the direction parallel to the edge. By rotating the edge about the beam axis for 360 deg, we obtain a complete Radon transform, which is used for reconstructing the focal spot. We conducted a study on a clinical C-arm system with three focal spot sizes (0.3, 0.6, and 1.0 mm nominal size), then compared the focal spot imaged using the proposed method against the conventional pinhole approach. The full width at half maximum (FWHM) of the focal spots along the width and height of the focal spot were used for quantitative comparisons. Results: Using the pinhole method as ground truth, the proposed method accurately characterized the focal spot shapes and sizes. Quantitatively, the FWHM widths were 0.37, 0.65, and 1.14 mm for the pinhole method and 0.33, 0.60, and 1.15 mm for the proposed method for the 0.3, 0.6, and 1.0 mm nominal focal spots, respectively. Similar levels of agreement were found for the FWHM heights. Conclusions: The method uses a rotating edge to characterize the focal spot and could be automated in the future using a system's built-in collimator. The method could be included as part of quality assurance tests of image quality and tube health.

An analysis of scatter characteristics in x-ray CT spectral correction

X-ray scatter remains a major physics challenge in volumetric computed tomography (CT), whose physical and statistical behaviors have been commonly leveraged in order to eliminate its impact on CT image quality. In this work, we conduct an in-depth derivation of how the scatter distribution and scatter to primary ratio (SPR) will change during the spectral correction, leading to an interesting finding on the property of scatter. Such a characterization of scatter's behavior provides an analytic approach of compensating for the SPR as well as approximating the change of scatter distribution after spectral correction, even though both of them might be significantly distorted as the linearization mapping function in spectral correction could vary a lot from one detector pixel to another. We conduct an evaluation of SPR compensations on a Catphan phantom and an anthropomorphic chest phantom to validate the characteristics of scatter. In addition, this scatter property is also directly adopted into CT imaging using a spectral modulator with flying focal spot technology (SMFFS) as an example to demonstrate its potential in practical applications. For cone-beam CT scans at both 80 and 120 kVp, CT images with accurate CT numbers can be achieved after spectral correction followed by the appropriate SPR compensation based on our presented scatter property. In the case of the SMFFS based cone-beam CT scan of the Catphan phantom at 120 kVp, after a scatter correction using an analytic algorithm derived from the scatter property, CT image quality was significantly improved, with the averaged root mean square error reduced from 297.9 to 6.5 Hounsfield units (HU).

Densely sampled spectral modulation for x-ray CT using a stationary modulator with flying focal spot: a conceptual and feasibility study of scatter and spectral correction

PURPOSE

Modulation of the x-ray source in computed tomography (CT) by a designated filter to achieve a desired distribution of photon flux has been greatly advanced in recent years. In this work, we present a densely sampled spectral modulation (DSSM) as a promising low-cost solution to quantitative CT imaging in the presence of scatter. By leveraging a special stationary filter (namely a spectral modulator) and a flying focal spot, DSSM features a strong correlation in the scatter distributions across focal spot positions and sees no substantial projection sparsity or misalignment in data sampling, making it possible to simultaneously correct for scatter and spectral effects in a unified framework.

METHODS

The concept of DSSM is first introduced, followed by an analysis of the design and benefits of using the stationary spectral modulator with a flying focal spot (SMFFS) that dramatically changes the data sampling and its associated data processing. With an assumption that the scatter distributions across focal spot positions have strong correlation, a scatter estimation and spectral correction algorithm from DSSM is then developed, where a dual-energy modulator along with two flying focal spot positions is of interest. Finally, a phantom study on a tabletop cone-beam CT system is conducted to understand the feasibility of DSSM by SMFFS, using a copper modulator and by moving the x-ray tube position in the X direction to mimic the flying focal spot.

RESULTS

Based on our analytical analysis of the DSSM by SMFFS, the misalignment of low- and high-energy projection rays can be reduced by a factor of more than 10 when compared with a stationary modulator only. With respect to modulator design, metal materials such as copper, molybdenum, silver, and tin could be good candidates in terms of energy separation at a given attenuation of photon flux. Physical experiments using a Catphan phantom as well as an anthropomorphic chest phantom demonstrate the effectiveness of DSSM by SMFFS with much better CT number accuracy and less image artifacts. The root mean squared error was reduced from 297.9 to 6.5 Hounsfield units (HU) for the Catphan phantom and from 409.3 to 39.2 HU for the chest phantom.

CONCLUSIONS

The concept of DSSM using a SMFFS is proposed. Phantom results on its scatter estimation and spectral correction performance validate our main ideas and key assumptions, demonstrating its potential and feasibility for quantitative CT imaging.

Characterization and potential applications of a dual-layer flat-panel detector

PURPOSE

Dual-energy (DE) x-ray imaging has many clinical applications in radiography, fluoroscopy, and CT. This work characterizes a prototype dual-layer (DL) flat-panel detector (FPD) and investigates its DE imaging capabilities for applications in two-dimensional (2D) radiography/fluoroscopy and quantitative three-dimensional (3D) cone-beam CT. Unlike other DE methods like kV switching, a DL FPD obtains DE images from a single exposure, making it robust against patient and system motion.

METHODS

The DL FPD consists of a top layer with a 200 µm-thick CsI scintillator coupled to an amorphous silicon (aSi) FPD of 150 µm pixel size and a bottom layer with a 550 µm thick CsI scintillator coupled to an identical aSi FPD. The two layers are separated by a 1-mm Cu filter to increase spectral separation. Images (43 × 43 cm2 active area) can be readout in 2 × 2 binning mode (300 µm pixels) at up to 15 frames per second. Detector performance was first characterized by measuring the MTF, NPS, and DQE for the top and bottom layers. For 2D applications, a qualitative study was conducted using an anthropomorphic thorax phantom containing a porcine heart with barium-filled coronary arteries (similar to iodine). Additionally, fluoroscopic lung tumor tracking was investigated by superimposing a moving tumor phantom on the thorax phantom. Tracking accuracies of single-energy (SE) and DE fluoroscopy were compared against the ground truth motion of the tumor. For 3D quantitative imaging, a phantom containing water, iodine, and calcium inserts was used to evaluate overall DE material decomposition capabilities. Virtual monoenergetic (VM) images ranging from 40 to 100 keV were generated, and the optimal VM image energy which achieved the highest image uniformity and maximum contrast-to-noise ratio (CNR) was determined.

RESULTS

The spatial resolution of the top layer was substantially higher than that of the bottom layer (top layer 50% MTF = 2.2 mm-1 , bottom layer = 1.2 mm-1 ). A substantial increase in NNPS and reduction in DQE were observed for the bottom layer mainly due to photon loss within the top layer and Cu filter. For 2D radiographic and fluoroscopic applications, the DL FPD was capable of generating high-quality material-specific images separating soft tissue from bone and barium. For lung tumor tracking, DE fluoroscopy yielded more accurate results than SE fluoroscopy, with an average reduction in the root mean square error (RMSE) of over 10×. For the DE-CBCT studies, accurate basis material decompositions were obtained. The estimated material densities were 294.68  ±  17.41 and 92.14  ±  15.61 mg/ml for the 300 and 100 mg/ml calcium inserts, respectively, and 8.93  ±  1.45, 4.72  ±  1.44, and 2.11  ±  1.32 mg/ml for the 10, 5, and 2 mg/ml iodine inserts, respectively, with an average error of less than 5%. The optimal VM image energy was found to be 60 keV.

CONCLUSIONS

We characterized a prototype DL FPD and demonstrated its ability to perform accurate single-exposure DE radiography/fluoroscopy and DE-CBCT. The merits of the DL detector approach include superior spatial and temporal registration between its constituent images, and less complicated acquisition sequences.

Reconstruction of x-ray focal spot distribution using a rotating edge

The size and shape of an x-ray source's focal spot is a critical factor in the imaging system's overall spatial resolution. The conventional approach to imaging the focal spot uses a pinhole camera, but this requires careful, manual measurements. Instead, we propose a novel alternative, simply using the collimator available on many x-ray systems. After placing the edge of a collimator blade in the center of the beam, we can obtain an image of its edge spread function (ESF). Each ESF provides information about the focal spot distribution - specifically, the parallel projection of the focal spot in the direction parallel to the edge. If the edge is then rotated about the beam axis, each image provides a different parallel projection of the focal spot until a complete Radon transform of the focal spot distribution is obtained. The focal spot can then be reconstructed by the inverse Radon transform, or parallel-beam filtered backprojection. We conducted a study on a clinical C-arm system with 3 focal spot sizes (0.3, 0.6, 1.0 mm nominal size), comparing the focal spot obtained using the rotating edge method against the conventional pinhole approach. Our results demonstrate accurate characterization of the size and shape of the focal spot.

Comparative Study of Dual Energy Cone-Beam CT using a Dual-Layer Detector and kVp Switching for Material Decomposition

Cone-beam CT (CBCT) is widely used in diagnostic imaging and image-guided procedures, leading to an increasing need for advanced CBCT techniques, such as dual energy (DE) imaging. Previous studies have shown that DE-CBCT can perform quantitative material decomposition, including quantification of contrast agents, electron density, and virtual monoenergetic images. Currently, most CBCT systems perform DE imaging using a kVp switching technique. However, the disadvantages of this method are spatial and temporal misregistration as well as total scan time increase, leading to errors in the material decomposition. DE-CBCT with a dual layer flat panel detector potentially overcomes these limitations by acquiring the dual energy images simultaneously. In this work, we investigate the DE imaging performance of a prototype dual layer detector by evaluating its material decomposition capability and comparing its performance to that of the kVp switching method. Two sets of x-ray spectra were used for kVp switching: 80/120 kVp and 80/120 kVp + 1 mm Cu filtration. Our results show the dual layer detector outperforms kVp switching at 80/120 kVp with matched dose. The performance of kVp switching was better by adding 1 mm copper filtration to the high energy images (80/120 kVp + 1 mm Cu), though the dual layer detector still provided comparable performance for material decomposition tasks. Overall, both the dual layer detector and kVp switching methods provided quantitative material decomposition images in DE-CBCT, with the dual layer detector having additional potential advantages.

Projection-domain metal artifact correction using a dual layer detector

Metal artifact remains a challenge in cone-beam CT images. Many two-pass metal artifact reduction methods have been proposed, which work fairly well, but are limited when the metal is outside the scan field-of-view (FOV) or when the metal is moving during the scan. In the former, even reconstructing with a larger FOV does not guarantee a good estimate of metal location in the projections; and in the latter, the metal location in each projection is difficult to identify due to motion. Furthermore, two-pass methods increase the total reconstruction time. In this study, a projection-based metal detection and correction method with a dual layer detector is investigated. The dual layer detector provides dual energy images with perfect temporal and spatial registration in each projection, which aid in the identification of metal. A simple phantom with metal wires (copper) and a needle (steel) is used to evaluate the projection-based metal artifact reduction method from a dual layer scan and compared with that of a single layer scan. Preliminary results showed enhanced ability to identify metal regions, leading to substantially reduced metal artifact in reconstructed images. In summary, an effective single-pass, projection-domain method using a dual layer detector has been demonstrated, and it is expected to be robust against truncation and motion.

Accuracy of patient-specific organ dose estimates obtained using an automated image segmentation algorithm

The overall goal of this work is to develop a rapid, accurate, and automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using simulations to generate dose maps combined with automated segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. We hypothesized that the autosegmentation algorithm is sufficiently accurate to provide organ dose estimates, since small errors delineating organ boundaries will have minimal effect when computing mean organ dose. A leave-one-out validation study of the automated algorithm was performed with 20 head-neck CT scans expertly segmented into nine regions. Mean organ doses of the automatically and expertly segmented regions were computed from Monte Carlo-generated dose maps and compared. The automated segmentation algorithm estimated the mean organ dose to be within 10% of the expert segmentation for regions other than the spinal canal, with the median error for each organ region below 2%. In the spinal canal region, the median error was [Formula: see text], with a maximum absolute error of 28% for the single-atlas approach and 11% for the multiatlas approach. The results demonstrate that the automated segmentation algorithm can provide accurate organ dose estimates despite some segmentation errors.

Accelerated statistical reconstruction for C-arm cone-beam CT using Nesterov's method

PURPOSE

To accelerate model-based iterative reconstruction (IR) methods for C-arm cone-beam CT (CBCT), thereby combining the benefits of improved image quality and/or reduced radiation dose with reconstruction times on the order of minutes rather than hours.

METHODS

The ordered-subsets, separable quadratic surrogates (OS-SQS) algorithm for solving the penalized-likelihood (PL) objective was modified to include Nesterov's method, which utilizes "momentum" from image updates of previous iterations to better inform the current iteration and provide significantly faster convergence. Reconstruction performance of an anthropomorphic head phantom was assessed on a benchtop CBCT system, followed by CBCT on a mobile C-arm, which provided typical levels of incomplete data, including lateral truncation. Additionally, a cadaveric torso that presented realistic soft-tissue and bony anatomy was imaged on the C-arm, and different projectors were assessed for reconstruction speed.

RESULTS

Nesterov's method provided equivalent image quality to OS-SQS while reducing the reconstruction time by an order of magnitude (10.0 ×) by reducing the number of iterations required for convergence. The faster projectors were shown to produce similar levels of convergence as more accurate projectors and reduced the reconstruction time by another 5.3 ×. Despite the slower convergence of IR with truncated C-arm CBCT, comparison of PL reconstruction methods implemented on graphics processing units showed that reconstruction time was reduced from 106 min for the conventional OS-SQS method to as little as 2.0 min with Nesterov's method for a volumetric reconstruction of the head. In body imaging, reconstruction of the larger cadaveric torso was reduced from 159 min down to 3.3 min with Nesterov's method.

CONCLUSIONS

The acceleration achieved through Nesterov's method combined with ordered subsets reduced IR times down to a few minutes. This improved compatibility with clinical workflow better enables broader adoption of IR in CBCT-guided procedures, with corresponding benefits in overcoming conventional limits of image quality at lower dose.

3D–2D registration in mobile radiographs: algorithm development and preliminary clinical evaluation

An image-based 3D-2D registration method is presented using radiographs acquired in the uncalibrated, unconstrained geometry of mobile radiography. The approach extends a previous method for six degree-of-freedom (DOF) registration in C-arm fluoroscopy (namely 'LevelCheck') to solve the 9-DOF estimate of geometry in which the position of the source and detector are unconstrained. The method was implemented using a gradient correlation similarity metric and stochastic derivative-free optimization on a GPU. Development and evaluation were conducted in three steps. First, simulation studies were performed that involved a CT scan of an anthropomorphic body phantom and 1000 randomly generated digitally reconstructed radiographs in posterior-anterior and lateral views. A median projection distance error (PDE) of 0.007 mm was achieved with 9-DOF registration compared to 0.767 mm for 6-DOF. Second, cadaver studies were conducted using mobile radiographs acquired in three anatomical regions (thorax, abdomen and pelvis) and three levels of source-detector distance (~800, ~1000 and ~1200 mm). The 9-DOF method achieved a median PDE of 0.49 mm (compared to 2.53 mm for the 6-DOF method) and demonstrated robustness in the unconstrained imaging geometry. Finally, a retrospective clinical study was conducted with intraoperative radiographs of the spine exhibiting real anatomical deformation and image content mismatch (e.g. interventional devices in the radiograph that were not in the CT), demonstrating a PDE = 1.1 mm for the 9-DOF approach. Average computation time was 48.5 s, involving 687 701 function evaluations on average, compared to 18.2 s for the 6-DOF method. Despite the greater computational load, the 9-DOF method may offer a valuable tool for target localization (e.g. decision support in level counting) as well as safety and quality assurance checks at the conclusion of a procedure (e.g. overlay of planning data on the radiograph for verification of the surgical product) in a manner consistent with natural surgical workflow.

Known-Component 3D-2D Registration for Image Guidance and Quality Assurance in Spine Surgery Pedicle Screw Placement

PURPOSE

To extend the functionality of radiographic/fluoroscopic imaging systems already within standard spine surgery workflow to: 1) provide guidance of surgical device analogous to an external tracking system; and 2) provide intraoperative quality assurance (QA) of the surgical product.

METHODS

Using fast, robust 3D-2D registration in combination with 3D models of known components (surgical devices), the 3D pose determination was solved to relate known components to 2D projection images and 3D preoperative CT in near-real-time. Exact and parametric models of the components were used as input to the algorithm to evaluate the effects of model fidelity. The proposed algorithm employs the covariance matrix adaptation evolution strategy (CMA-ES) to maximize gradient correlation (GC) between measured projections and simulated forward projections of components. Geometric accuracy was evaluated in a spine phantom in terms of target registration error at the tool tip (TRE _x ), and angular deviation (TRE _ϕ ) from planned trajectory.

RESULTS

Transpedicle surgical devices (probe tool and spine screws) were successfully guided with TRE _x <2 mm and TRE _ϕ <0.5° given projection views separated by at least >30° (easily accommodated on a mobile C-arm). QA of the surgical product based on 3D-2D registration demonstrated the detection of pedicle screw breach with TRE _x <1 mm, demonstrating a trend of improved accuracy correlated to the fidelity of the component model employed.

CONCLUSIONS

3D-2D registration combined with 3D models of known surgical components provides a novel method for near-real-time guidance and quality assurance using a mobile C-arm without external trackers or fiducial markers. Ongoing work includes determination of optimal views based on component shape and trajectory, improved robustness to anatomical deformation, and expanded preclinical testing in spine and intracranial surgeries.

Low-dose preview for patient-specific, task-specific technique selection in cone-beam CT

PURPOSE

A method is presented for generating simulated low-dose cone-beam CT (CBCT) preview images from which patient- and task-specific minimum-dose protocols can be confidently selected prospectively in clinical scenarios involving repeat scans.

METHODS

In clinical scenarios involving a series of CBCT images, the low-dose preview (LDP) method operates upon the first scan to create a projection dataset that accurately simulates the effects of dose reduction in subsequent scans by injecting noise of proper magnitude and correlation, including both quantum and electronic readout noise as important components of image noise in flat-panel detector CBCT. Experiments were conducted to validate the LDP method in both a head phantom and a cadaveric torso by performing CBCT acquisitions spanning a wide dose range (head: 0.8-13.2 mGy, body: 0.8-12.4 mGy) with a prototype mobile C-arm system. After injecting correlated noise to simulate dose reduction, the projections were reconstructed using both conventional filtered backprojection (FBP) and an iterative, model-based image reconstruction method (MBIR). The LDP images were then compared to real CBCT images in terms of noise magnitude, noise-power spectrum (NPS), spatial resolution, contrast, and artifacts.

RESULTS

For both FBP and MBIR, the LDP images exhibited accurate levels of spatial resolution and contrast that were unaffected by the correlated noise injection, as expected. Furthermore, the LDP image noise magnitude and NPS were in strong agreement with real CBCT images acquired at the corresponding, reduced dose level across the entire dose range considered. The noise magnitude agreed within 7% for both the head phantom and cadaveric torso, and the NPS showed a similar level of agreement up to the Nyquist frequency. Therefore, the LDP images were highly representative of real image quality across a broad range of dose and reconstruction methods. On the other hand, naïve injection ofuncorrelated noise resulted in strong underestimation of the true noise, which would lead to overly optimistic predictions of dose reduction.

CONCLUSIONS

Correlated noise injection is essential to accurate simulation of CBCT image quality at reduced dose. With the proposed LDP method, the user can prospectively select patient-specific, minimum-dose protocols (viz., acquisition technique and reconstruction method) suitable to a particular imaging task and to the user's own observer preferences for CBCT scans following the first acquisition. The method could provide dose reduction in common clinical scenarios involving multiple CBCT scans, such as image-guided surgery and radiotherapy.

Efficacy of fixed filtration for rapid kVp-switching dual energy x-ray systems

PURPOSE

Dose efficiency of dual kVp imaging can be improved if the two beams are filtered to remove photons in the common part of their spectra, thereby increasing spectral separation. While there are a number of advantages to rapid kVp-switching for dual energy, it may not be feasible to have two different filters for the two spectra. Therefore, the authors are interested in whether a fixed added filter can improve the dose efficiency of kVp-switching dual energy x-ray systems.

METHODS

The authors hypothesized that a K-edge filter would provide the energy selectivity needed to remove overlap of the spectra and hence increase the precision of material separation at constant dose. Preliminary simulations were done using calcium and water basis materials and 80 and 140 kVp x-ray spectra. Precision of the decomposition was evaluated based on the propagation of the Poisson noise through the decomposition function. Considering availability and cost, the authors chose a commercial Gd2O2S screen as the filter for their experimental validation. Experiments were conducted on a table-top system using a phantom with various thicknesses of acrylic and copper and 70 and 125 kVp x-ray spectra. The authors kept the phantom exposure roughly constant with and without filtration by adjusting the tube current. The filtered and unfiltered raw data of both low and high energy were decomposed into basis material and the variance of the decomposition for each thickness pair was calculated. To evaluate the filtration performance, the authors measured the ratio of material decomposition variance with and without filtration.

RESULTS

Simulation results show that the ideal filter material depends on the object composition and thickness, and ranges across the lanthanide series, with higher atomic number filters being preferred for more attenuating objects. Variance reduction increases with filter thickness, and substantial reductions (40%) can be achieved with a 2× loss in intensity. The authors' experimental results validate the simulations, yet were overall slightly worse than expectation. For large objects, conventional (non-K-edge) beam hardening filters perform well.

CONCLUSIONS

This study demonstrates the potential of fixed K-edge filtration to improve the dose efficiency and material decomposition precision for rapid kVp-switching dual energy systems.

Dual-energy cone-beam CT with a flat-panel detector: effect of reconstruction algorithm on material classification

PURPOSE

Cone-beam CT (CBCT) with a flat-panel detector (FPD) is finding application in areas such as breast and musculoskeletal imaging, where dual-energy (DE) capabilities offer potential benefit. The authors investigate the accuracy of material classification in DE CBCT using filtered backprojection (FBP) and penalized likelihood (PL) reconstruction and optimize contrast-enhanced DE CBCT of the joints as a function of dose, material concentration, and detail size.

METHODS

Phantoms consisting of a 15 cm diameter water cylinder with solid calcium inserts (50-200 mg/ml, 3-28.4 mm diameter) and solid iodine inserts (2-10 mg/ml, 3-28.4 mm diameter), as well as a cadaveric knee with intra-articular injection of iodine were imaged on a CBCT bench with a Varian 4343 FPD. The low energy (LE) beam was 70 kVp (+0.2 mm Cu), and the high energy (HE) beam was 120 kVp (+0.2 mm Cu, +0.5 mm Ag). Total dose (LE+HE) was varied from 3.1 to 15.6 mGy with equal dose allocation. Image-based DE classification involved a nearest distance classifier in the space of LE versus HE attenuation values. Recognizing the differences in noise between LE and HE beams, the LE and HE data were differentially filtered (in FBP) or regularized (in PL). Both a quadratic (PLQ) and a total-variation penalty (PLTV) were investigated for PL. The performance of DE CBCT material discrimination was quantified in terms of voxelwise specificity, sensitivity, and accuracy.

RESULTS

Noise in the HE image was primarily responsible for classification errors within the contrast inserts, whereas noise in the LE image mainly influenced classification in the surrounding water. For inserts of diameter 28.4 mm, DE CBCT reconstructions were optimized to maximize the total combined accuracy across the range of calcium and iodine concentrations, yielding values of ∼ 88% for FBP and PLQ, and ∼ 95% for PLTV at 3.1 mGy total dose, increasing to ∼ 95% for FBP and PLQ, and ∼ 98% for PLTV at 15.6 mGy total dose. For a fixed iodine concentration of 5 mg/ml and reconstructions maximizing overall accuracy across the range of insert diameters, the minimum diameter classified with accuracy >80% was ∼ 15 mm for FBP and PLQ and ∼ 10 mm for PLTV, improving to ∼ 7 mm for FBP and PLQ and ∼ 3 mm for PLTV at 15.6 mGy. The results indicate similar performance for FBP and PLQ and showed improved classification accuracy with edge-preserving PLTV. A slight preference for increased smoothing of the HE data was found. DE CBCT discrimination of iodine and bone in the knee was demonstrated with FBP and PLTV at 6.2 mGy total dose.

CONCLUSIONS

For iodine concentrations >5 mg/ml and detail size ∼ 20 mm, material classification accuracy of >90% was achieved in DE CBCT with both FBP and PL at total doses <10 mGy. Optimal performance was attained by selection of reconstruction parameters based on the differences in noise between HE and LE data, typically favoring stronger smoothing of the HE data, and by using penalties matched to the imaging task (e.g., edge-preserving PLTV in areas of uniform enhancement).

dPIRPLE: a joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior images

Sequential imaging studies are conducted in many clinical scenarios. Prior images from previous studies contain a great deal of patient-specific anatomical information and can be used in conjunction with subsequent imaging acquisitions to maintain image quality while enabling radiation dose reduction (e.g., through sparse angular sampling, reduction in fluence, etc). However, patient motion between images in such sequences results in misregistration between the prior image and current anatomy. Existing prior-image-based approaches often include only a simple rigid registration step that can be insufficient for capturing complex anatomical motion, introducing detrimental effects in subsequent image reconstruction. In this work, we propose a joint framework that estimates the 3D deformation between an unregistered prior image and the current anatomy (based on a subsequent data acquisition) and reconstructs the current anatomical image using a model-based reconstruction approach that includes regularization based on the deformed prior image. This framework is referred to as deformable prior image registration, penalized-likelihood estimation (dPIRPLE). Central to this framework is the inclusion of a 3D B-spline-based free-form-deformation model into the joint registration-reconstruction objective function. The proposed framework is solved using a maximization strategy whereby alternating updates to the registration parameters and image estimates are applied allowing for improvements in both the registration and reconstruction throughout the optimization process. Cadaver experiments were conducted on a cone-beam CT testbench emulating a lung nodule surveillance scenario. Superior reconstruction accuracy and image quality were demonstrated using the dPIRPLE algorithm as compared to more traditional reconstruction methods including filtered backprojection, penalized-likelihood estimation (PLE), prior image penalized-likelihood estimation (PIPLE) without registration, and prior image penalized-likelihood estimation with rigid registration of a prior image (PIRPLE) over a wide range of sampling sparsity and exposure levels.

Deformable image registration with local rigidity constraints for cone-beam CT-guided spine surgery

Image-guided spine surgery (IGSS) is associated with reduced co-morbidity and improved surgical outcome. However, precise localization of target anatomy and adjacent nerves and vessels relative to planning information (e.g., device trajectories) can be challenged by anatomical deformation. Rigid registration alone fails to account for deformation associated with changes in spine curvature, and conventional deformable registration fails to account for rigidity of the vertebrae, causing unrealistic distortions in the registered image that can confound high-precision surgery. We developed and evaluated a deformable registration method capable of preserving rigidity of bones while resolving the deformation of surrounding soft tissue. The method aligns preoperative CT to intraoperative cone-beam CT (CBCT) using free-form deformation (FFD) with constraints on rigid body motion imposed according to a simple intensity threshold of bone intensities. The constraints enforced three properties of a rigid transformation-namely, constraints on affinity (AC), orthogonality (OC), and properness (PC). The method also incorporated an injectivity constraint (IC) to preserve topology. Physical experiments involving phantoms, an ovine spine, and a human cadaver as well as digital simulations were performed to evaluate the sensitivity to registration parameters, preservation of rigid body morphology, and overall registration accuracy of constrained FFD in comparison to conventional unconstrained FFD (uFFD) and Demons registration. FFD with orthogonality and injectivity constraints (denoted FFD+OC+IC) demonstrated improved performance compared to uFFD and Demons. Affinity and properness constraints offered little or no additional improvement. The FFD+OC+IC method preserved rigid body morphology at near-ideal values of zero dilatation (D = 0.05, compared to 0.39 and 0.56 for uFFD and Demons, respectively) and shear (S = 0.08, compared to 0.36 and 0.44 for uFFD and Demons, respectively). Target registration error (TRE) was similarly improved for FFD+OC+IC (0.7 mm), compared to 1.4 and 1.8 mm for uFFD and Demons. Results were validated in human cadaver studies using CT and CBCT images, with FFD+OC+IC providing excellent preservation of rigid morphology and equivalent or improved TRE. The approach therefore overcomes distortions intrinsic to uFFD and could better facilitate high-precision IGSS.

Soft-tissue imaging with C-arm cone-beam CT using statistical reconstruction

The potential for statistical image reconstruction methods such as penalized-likelihood (PL) to improve C-arm cone-beam CT (CBCT) soft-tissue visualization for intraoperative imaging over conventional filtered backprojection (FBP) is assessed in this work by making a fair comparison in relation to soft-tissue performance. A prototype mobile C-arm was used to scan anthropomorphic head and abdomen phantoms as well as a cadaveric torso at doses substantially lower than typical values in diagnostic CT, and the effects of dose reduction via tube current reduction and sparse sampling were also compared. Matched spatial resolution between PL and FBP was determined by the edge spread function of low-contrast (∼ 40-80 HU) spheres in the phantoms, which were representative of soft-tissue imaging tasks. PL using the non-quadratic Huber penalty was found to substantially reduce noise relative to FBP, especially at lower spatial resolution where PL provides a contrast-to-noise ratio increase up to 1.4-2.2 × over FBP at 50% dose reduction across all objects. Comparison of sampling strategies indicates that soft-tissue imaging benefits from fully sampled acquisitions at dose above ∼ 1.7 mGy and benefits from 50% sparsity at dose below ∼ 1.0 mGy. Therefore, an appropriate sampling strategy along with the improved low-contrast visualization offered by statistical reconstruction demonstrates the potential for extending intraoperative C-arm CBCT to applications in soft-tissue interventions in neurosurgery as well as thoracic and abdominal surgeries by overcoming conventional tradeoffs in noise, spatial resolution, and dose.

Patient-Specific Minimum-Dose Imaging Protocols for Statistical Image Reconstruction in C-arm Cone-Beam CT Using Correlated Noise Injection

Purpose

A new method for accurately portraying the impact of low-dose imaging techniques in C-arm cone-beam CT (CBCT) is presented and validated, allowing identification of minimum-dose protocols suitable to a given imaging task on a patient-specific basis in scenarios that require repeat intraoperative scans.

Method

To accurately simulate lower-dose techniques and account for object-dependent noise levels (x-ray quantum noise and detector electronics noise) and correlations (detector blur), noise of the proper magnitude and correlation was injected into the projections from an initial CBCT acquired at the beginning of a procedure. The resulting noisy projections were then reconstructed to yield low-dose preview (LDP) images that accurately depict the image quality at any level of reduced dose in both filtered backprojection and statistical image reconstruction. Validation studies were conducted on a mobile C-arm, with the noise injection method applied to images of an anthropomorphic head phantom and cadaveric torso across a range of lower-dose techniques.

Results

Comparison of preview and real CBCT images across a full range of techniques demonstrated accurate noise magnitude (within ~5%) and correlation (matching noise-power spectrum, NPS). Other image quality characteristics (e.g., spatial resolution, contrast, and artifacts associated with beam hardening and scatter) were also realistically presented at all levels of dose and across reconstruction methods, including statistical reconstruction.

Conclusion

Generating low-dose preview images for a broad range of protocols gives a useful method to select minimum-dose techniques that accounts for complex factors of imaging task, patient-specific anatomy, and observer preference. The ability to accurately simulate the influence of low-dose acquisition in statistical reconstruction provides an especially valuable means of identifying low-dose limits in a manner that does not rely on a model for the nonlinear reconstruction process or a model of observer performance.

3D-2D registration for surgical guidance: effect of projection view angles on registration accuracy

An algorithm for intensity-based 3D-2D registration of CT and x-ray projections is evaluated, specifically using single- or dual-projection views to provide 3D localization. The registration framework employs the gradient information similarity metric and covariance matrix adaptation evolution strategy to solve for the patient pose in six degrees of freedom. Registration performance was evaluated in an anthropomorphic phantom and cadaver, using C-arm projection views acquired at angular separation, Δθ, ranging from ∼0°-180° at variable C-arm magnification. Registration accuracy was assessed in terms of 2D projection distance error and 3D target registration error (TRE) and compared to that of an electromagnetic (EM) tracker. The results indicate that angular separation as small as Δθ ∼10°-20° achieved TRE <2 mm with 95% confidence, comparable or superior to that of the EM tracker. The method allows direct registration of preoperative CT and planning data to intraoperative fluoroscopy, providing 3D localization free from conventional limitations associated with external fiducial markers, stereotactic frames, trackers and manual registration.

Robust 3D-2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation

We present a framework for robustly estimating registration between a 3D volume image and a 2D projection image and evaluate its precision and robustness in spine interventions for vertebral localization in the presence of anatomical deformation. The framework employs a normalized gradient information similarity metric and multi-start covariance matrix adaptation evolution strategy optimization with local-restarts, which provided improved robustness against deformation and content mismatch. The parallelized implementation allowed orders-of-magnitude acceleration in computation time and improved the robustness of registration via multi-start global optimization. Experiments involved a cadaver specimen and two CT datasets (supine and prone) and 36 C-arm fluoroscopy images acquired with the specimen in four positions (supine, prone, supine with lordosis, prone with kyphosis), three regions (thoracic, abdominal, and lumbar), and three levels of geometric magnification (1.7, 2.0, 2.4). Registration accuracy was evaluated in terms of projection distance error (PDE) between the estimated and true target points in the projection image, including 14 400 random trials (200 trials on the 72 registration scenarios) with initialization error up to ±200 mm and ±10°. The resulting median PDE was better than 0.1 mm in all cases, depending somewhat on the resolution of input CT and fluoroscopy images. The cadaver experiments illustrated the tradeoff between robustness and computation time, yielding a success rate of 99.993% in vertebral labeling (with 'success' defined as PDE <5 mm) using 1,718 664 ± 96 582 function evaluations computed in 54.0 ± 3.5 s on a mid-range GPU (nVidia, GeForce GTX690). Parameters yielding a faster search (e.g., fewer multi-starts) reduced robustness under conditions of large deformation and poor initialization (99.535% success for the same data registered in 13.1 s), but given good initialization (e.g., ±5 mm, assuming a robust initial run) the same registration could be solved with 99.993% success in 6.3 s. The ability to register CT to fluoroscopy in a manner robust to patient deformation could be valuable in applications such as radiation therapy, interventional radiology, and an assistant to target localization (e.g., vertebral labeling) in image-guided spine surgery.

Deformable image registration for cone-beam CT guided transoral robotic base-of-tongue surgery

Transoral robotic surgery (TORS) offers a minimally invasive approach to resection of base-of-tongue tumors. However, precise localization of the surgical target and adjacent critical structures can be challenged by the highly deformed intraoperative setup. We propose a deformable registration method using intraoperative cone-beam computed tomography (CBCT) to accurately align preoperative CT or MR images with the intraoperative scene. The registration method combines a Gaussian mixture (GM) model followed by a variation of the Demons algorithm. First, following segmentation of the volume of interest (i.e. volume of the tongue extending to the hyoid), a GM model is applied to surface point clouds for rigid initialization (GM rigid) followed by nonrigid deformation (GM nonrigid). Second, the registration is refined using the Demons algorithm applied to distance map transforms of the (GM-registered) preoperative image and intraoperative CBCT. Performance was evaluated in repeat cadaver studies (25 image pairs) in terms of target registration error (TRE), entropy correlation coefficient (ECC) and normalized pointwise mutual information (NPMI). Retraction of the tongue in the TORS operative setup induced gross deformation >30 mm. The mean TRE following the GM rigid, GM nonrigid and Demons steps was 4.6, 2.1 and 1.7 mm, respectively. The respective ECC was 0.57, 0.70 and 0.73, and NPMI was 0.46, 0.57 and 0.60. Registration accuracy was best across the superior aspect of the tongue and in proximity to the hyoid (by virtue of GM registration of surface points on these structures). The Demons step refined registration primarily in deeper portions of the tongue further from the surface and hyoid bone. Since the method does not use image intensities directly, it is suitable to multi-modality registration of preoperative CT or MR with intraoperative CBCT. Extending the 3D image registration to the fusion of image and planning data in stereo-endoscopic video is anticipated to support safer, high-precision base-of-tongue robotic surgery.

Soft-Tissue Imaging in Low-Dose, C-Arm Cone-Beam CT Using Statistical Image Reconstruction

C-arm cone-beam CT (CBCT) is an emerging tool for intraoperative imaging, but current embodiments exhibit modest soft-tissue imaging capability and are largely constrained to high-contrast imaging tasks. A major advance in image quality is facilitated by statistical iterative reconstruction techniques. This work adapts a general penalized likelihood (PL) reconstruction approach with variable penalties and regularization to C-arm CBCT and investigates performance in imaging of large (>10 mm), low-contrast (<100 HU) tasks pertinent to soft-tissue surgical guidance. Experiments involved a mobile C-arm for CBCT with phantoms and cadavers presenting soft-tissue structures imaged using 3D filtered backprojection (FBP), quadratic, and non-quadratic PL reconstruction. Polyethylene phantoms with various tissue-equivalent inserts were used to quantity contrast-to-noise / resolution tradeoffs in low-contrast (~40 HU) structures, and the optimal reconstruction parameters were translated to imaging an anthropomorphic head phantom with low-contrasts targets and a cadaveric torso. Statistical reconstruction - especially non-quadratic PL variants - boosted soft-tissue image quality through reduction of noise and artifacts (e.g., a ~2-4 fold increase in contrast-to-noise ratio (CNR) at equivalent spatial resolution). For tasks relating to large, low-contrast tissues, even greater gains were possible using non-quadratic penalties and strong regularization that sacrificed spatial resolution in a manner still consistent with the imaging task. The advances in image quality offered by statistical reconstruction present promise and new challenges for interventional imaging, with high-speed computing facilitating realistic application. Careful investigation of performance relative to specific imaging tasks permits knowledgeable application of such techniques in a manner that overcomes conventional tradeoffs in noise, resolution, and dose and could extend application of CBCT-capable C-arms to soft-tissue interventions in neurosurgery as well as thoracic and abdominal interventions.

Sufficient statistics as a generalization of binning in spectral X-ray imaging

It is well known that the energy dependence of X-ray attenuation can be used to characterize materials. Yet, even with energy discriminating photon counting X-ray detectors, it is still unclear how to best form energy dependent measurements for spectral imaging. Common ideas include binning photon counts based on their energies and detectors with both photon counting and energy integrating electronics. These approaches can be generalized to energy weighted measurements, which we prove can form a sufficient statistic for spectral X-ray imaging if the weights used, which we term μ-weights, are basis attenuation functions that can also be used for material decomposition. To study the performance of these different methods, we evaluate the Cramér-Rao lower bound (CRLB) of material estimates in the presence of quantum noise. We found that the choice of binning and weighting schemes can greatly affect the performance of material decomposition. Even with optimized thresholds, binning condenses information but incurs penalties to decomposition precision and is not robust to changes in the source spectrum or object size, although this can be mitigated by adding more bins or removing photons of certain energies from the spectrum. On the other hand, because μ-weighted measurements form a sufficient statistic for spectral imaging, the CRLB of the material decomposition estimates is identical to the quantum noise limited performance of a system with complete energy information of all photons. Finally, we show that μ-weights lead to increased conspicuity over other methods in a simulated calcium contrast experiment.

Growth responses of cartilage to static and dynamic compression

During skeletal development, growth, and maturation, gradual changes in the material properties and physical dimensions of cartilage occur under the influence of mechanical loading. The objective of the current study was to compare glycosaminoglyean biosynthesis and cell proliferation in fetal, calf, and adult bovine cartilage explants, isolated from defined depths from the articular surface, in response to controlled compressive loads. Mechanical testing confirmed that for all cartilage samples subjected to load, there was a marked time-averaged (static) compression, whereas the addition of dynamic load at a frequency of 0.01 Hz induced dynamic strain with amplitude and phase shift characteristics typical of stimuli that previously were found to be associated with stimulation of glycosaminoglycan synthesis. In metabolic studies, the application of static loading (84 kPa) for 24 hours inhibited glycosaminoglycan and deoxyribonucleic acid synthesis in all cultured cartilage samples. The superposition of dynamic loading (200 kPa, 0.01 Hz) induced a 20% stimulation of glycosaminoglycan biosynthesis in calf cartilage from the middle-deep zones over statically-loaded samples and an additional approximate 50% suppression of deoxyribonucleic acid synthesis in fetal and calf cartilage from the articular surface. These results indicate that synthesis of glycosaminoglycan and deoxyribonucleic acid, two distinct indices of cartilage growth, are regulated independently by mechanical loading and that cartilage responds differently to static and dynamic loading at different stages of maturation.

Arsenite induces oxidative DNA adducts and DNA-protein cross-links in mammalian cells

Arsenic is generally recognized as a nonmutagenic carcinogen because sodium arsenite induces DNA damage only at very high concentrations. In this study we demonstrate that arsenite concentrations above 0.25 microM induce DNA strand breaks in both human leukemia cells and Chinese hamster ovary cells. Therefore, DNA damage may be involved in arsenic-induced carcinogenesis. Formamidopyrimidine-DNA glycosylase and proteinase K greatly increased DNA strand breaks in arsenite-treated cells, providing evidence that a large portion of arsenite-induced DNA strand breaks come from excision of oxidative DNA adducts and DNA-protein cross-links. Because DNA strand breaks appear only temporarily during excision repair, the level of detectable DNA strand breaks will be low at any given time point. For this reason many previous studies have only detected low levels of DNA strand breaks. We also show that catalase, and inhibitors of calcium, nitric oxide synthase, superoxide dismutase, and myeloperoxidase, could modulate arsenite-induced DNA damage. We conclude that arsenite induces DNA adducts through calcium-mediated production of peroxynitrite, hypochlorous acid, and hydroxyl radicals.

A mannose selection system for production of fertile transgenic maize plants from protoplasts

 Maize (Zea mays L.) callus cultures cannot use mannose as a sole carbohydrate source, but can utilize fructose for that purpose. Phosphomannose isomerase (PMI) can convert mannose to fructose. Transgenic maize plants were obtained by selecting polyethylene glycol (PEG)-mediated transformed protoplasts on mannose (20 g/l) containing medium. Transgenic calluses and plants carrying the PMI structural gene, manA, were able to convert mannose to fructose. The PEG-mediated protoplast transformation frequency was 0.06%. Stable transformation was confirmed by PCR, PMI activity, germination tests, and by histochemical staining with 5-bromo-4-chloro-3-indolyl-β-D-glucuronide (X-Gluc). Stable integration of the transgenes into the maize genome was demonstrated in T₁ and T₂ plants. Results indicate that the mannose selection system can be used for maize PEG-mediated protoplast transformation.

In vivo antitumor activity and induction of insulin-like growth factor-1-resistant apoptosis by SC-alphaalphadelta9

We previously showed that SC-alphaalphadelta9 (4-(benzyl-(2-[(2, 5-diphenyl-oxazole-4-carbonyl)-amino]-ethyl)-carbamoyl)-2-decanoylami no butyric acid) is a novel antiphosphatase agent that selectively inhibits the growth of transformed cells in culture and affects elements of insulin-like growth factor-1 (IGF-1) signaling. We now show that SC-alphaalphadelta9 induces IGF-1-resistant apoptosis and kills tumor cells in vivo. In cultured murine 32D cells, SC-alphaalphadelta9 induced concentration-dependent apoptosis that was blocked by ectopic Bcl-2 expression. No apoptosis was detected in 32D cells treated with the congener SC-alpha109, which lacks the ability to disrupt IGF-1 signaling. After interleukin-3 withdrawal or etoposide treatment, exogenous IGF-1 prevented apoptosis and elevated levels of Cdc2, a biochemical indicator of a functional IGF-1 receptor pathway. In contrast, exogenous IGF-1 did not prevent apoptosis or loss of Cdc2 expression caused by SC-alphaalphadelta9. Furthermore, IGF-1 receptor overexpression failed to protect cells against SC-alphaalphadelta9-induced apoptosis. Kinetic analyses demonstrated that Cdc2 down-regulation after SC-alphaalphadelta9 treatment preceded both apoptosis and loss of the IGF-1 receptor, indicating that loss of Cdc2 was a direct effect of SC-alphaalphadelta9 treatment and not secondary to cell death. IGF-1 receptor autophosphorylation studies indicated that SC-alphaalphadelta9 did not interact directly with the IGF-1 receptor nor bind to the growth factor itself, suggesting a site of action distal to the IGF-1 receptor. In the SCCVII murine tumor model, a single i.p. injection of SC-alphaalphadelta9 caused a dose-dependent decrease in clonogenic cell survival. The IC(50) of SC-alphaalphadelta9 was 35 mg/kg, comparable to 25 mg/kg carboplatin. The ability to induce IGF-1-resistant apoptosis distinguishes SC-alphaalphadelta9 from other apoptosis-inducing agents and suggests compounds of this class deserve further study as potential anticancer agents.

Energy-dependent nuclear binding dictates metallothionein localization

Metallothioneins (MTs) are low molecular weight, stress-activated proteins that protect cells against heavy metals, oxidants, and some electrophilic drugs. Both nuclear and cytoplasmic MT phenotypes have been observed in cells even though MTs (6 kDa) are well below the size exclusion limit for diffusion through the nuclear envelope. To study the factors controlling MT subcellular partitioning, we covalently linked MTII to a fluorescent label and examined its subcellular distribution in response both to pharmacologic and physical perturbations. Fluorescent MTII localized to the nucleus of digitonin-permeabilized human SCC25 carcinoma cells, consistent with its endogenous distribution in these cells. Nuclear sequestration of the fluorescent MTII was inhibited by a 100-fold molar excess of unlabeled MTII and by wheat germ agglutinin, indicating a saturable binding mechanism and the involvement of one or more glycoproteins, respectively. Depletion of adenosine triphosphate (ATP) inhibited MTII nuclear localization, implying energy-dependent nuclear translocation or retention of MT. Neither chilling nor the absence of cytosolic extracts inhibited nuclear sequestration of MTII, supporting diffusion-based entry mechanism. In situ biochemical extractions of the nuclear MTII revealed at least two distinct binding activities. Collectively, these data indicate that MTII diffuses into the nucleus of SCC25 cells, where it is selectively and actively retained by nuclear binding factors, imparting its localization phenotype.

Diversity of metallothionein content and subcellular localization in the National Cancer Institute tumor panel

Metallothioneins (MTs) are major thiol-containing intracellular proteins that bind metals, are induced by stress, and have been implicated in resistance to drugs and heavy metals.

PURPOSE

To examine the hypothesis that the protective functionality of MT may be dictated by its subcellular localization.

METHODS

We analyzed the basal MT content in 53 adherent cell lines of the National Cancer Institute (NCI) tumor panel and quantified the nuclear/cytoplasmic distribution of MT using confocal laser scanning microscopy and a recently described immunofluorescence-based algorithm.

RESULTS

Among these cell types we found a 400-fold range in the basal MT levels and a tenfold range in the ratio of the nuclear to cytoplasmic MT immunostaining that was independent of basal MT content. Total MT levels and nuclear/cytoplasmic distribution were independent of total glutathione content, suggesting autonomous regulation of these protective protein and nonprotein thiol pools. Approximately 50% (29/53) of the cell lines had a greater nuclear than cytoplasmic MT density and were defined as having a karyophilic phenotype. Tissue specificity of MT localization was seen with breast cancer cell lines, which were cytoplasmophilic, whereas prostate-derived cells were karyophilic. Among the 25000 unrestricted compounds in the NCI database, we detected a correlation between total basal MT levels and resistance to CdCl2, four Pt- and two Cu-containing compounds. High nuclear/cytoplasmic MT values correlated with resistance to six Cu-, six Pb-, and one Zn-containing compounds.

CONCLUSIONS

These results demonstrated significant diversity in MT content and subcellular localization in human tumor cells. Moreover, both basal MT levels and subcellular distribution appeared to be determinants of cellular responsiveness to metal-containing compounds.

Transcranial Doppler ultrasonography in neurosurgery: effects of intracranial tumour on right middle cerebral artery flow velocity during induction of anaesthesia

The right middle cerebral artery flow velocity (MCAFV) was measured by transcranial Doppler ultrasonography in neurosurgical patients with and without intracranial tumours during anaesthetic induction and endotracheal intubation. With institutional and patient consent, 20 non-tumour and 85 tumour-bearing neurosurgical patients were enlisted. The right middle cerebral artery was insonated with a pulsed-wave range-gated transcranial Doppler at 2 MHz, and MCAFV was recorded via a video graphics printer. The mean MCAFV, pulsatility index, use of anaesthetic drugs, heart rate, mean arterial pressure, and endtidal CO2 were recorded on preinduction, postinduction, intubation, and 90 to 180 s postintubation. There was no demographic, systemic haemodynamic, or anaesthetic difference between groups except for a predominance of women in the tumour group. In all patients, mean arterial pressure and MCAFV demonstrated with time a significant decrease with anaesthetic induction, increase with endotracheal intubation, and decrease post intubation. The right MCAFV was significantly higher in both tumour and right-sided tumour patients compared to non-tumour patients. There was no difference in left-sided tumour patients compared to non-tumour patients. These data indicate that intracranial tumours have cerebrovascular effects, causing either hyperaemia or vasoconstriction, and that the effects of anaesthetic induction and intubation agree with previously reported effects on cerebral blood flow and intracranial pressure.

[Pharmacognostic studies on the peel of Citrus reticulata Blanco cv. zhangshuensis and Citrus reticulata Blanco Var. kinokuni (Tanaka) H. H. Hu produced in Jiangxi]

Pharmacognostical studies on the peel of Citrus reticulata cv. zhangshuensis and Citrus reticulata var. kinokuni have been carried out in comparison with four crude drugs of Chenpi. Information on the research of resources of these two drugs is provided.

Callus induction and plant regeneration from maize mature embryos

Calli were induced from mature embryos of maize (Zea mays L.) inbred lines A632, B73 and Mol7 on MS medium supplemented with 1-2 mg/1 2,4-dichlorophenoxyacetic acid. Callus induction frequency ranged from 23-100%, with Mol7 having the highest frequency. Plants were regenerated from 4-5% of the B73 and Mol7 explants. Embryogenic and organogenic calli of B73 were maintained for more than two and one half years without losing regenerability. Of 95 regenerated plants, only one R0 plant with abnormal pollen was detected, and no morphological variants were observed in the R1 progeny.