Improving Stenosis Assessment in Energy Integrating Detector CT via Learned Monoenergetic Imaging Capability.

Coronary CT angiography (cCTA) is a fast non-invasive imaging exam for coronary artery disease (CAD) but struggles with dense calcifications and stents due to blooming artifacts, potentially causing stenosis overestimation. Virtual monoenergetic images (VMIs) at higher keV (e.g., 100 keV) from photon counting detector (PCD) CT have shown promise in reducing blooming artifacts and improving lumen visibility through its simultaneous high-resolution and multi-energy imaging capability. However, most cCTA exams are performed with single-energy CT (SECT) using conventional energy-integrating detectors (EID). Generating VMIs through EID-CT requires advanced multi-energy CT (MECT) scanners and potentially sacrifices temporal resolution. Given these limitations, MECT cCTA exams are not commonly performed on EID-CT and VMIs are not routinely generated. To tackle this, we aim to enhance the multi-energy imaging capability of EID-CT through the utilization of a convolutional neural network to LEarn MONoenergetic imAging from VMIs at Different Energies (LEMONADE). The neural network was trained using ten patient cCTA exams acquired on a clinical PCD-CT (NAEOTOM Alpha, Siemens Healthineers), with 70 keV VMIs as input (which is nominally equivalent to the SECT from EID-CT scanned at 120 kV) and 100 keV VMIs as the target. Subsequently, we evaluated the performance of EID-CT equipped with LEMONADE on both phantom and patient cases (n=10) for stenosis assessment. Results indicated that LEMONADE accurately quantified stenosis in three phantoms, aligning closely with ground truth and demonstrating stenosis percentage area reductions of 13%, 8%, and 9%. In patient cases, it led to a 12.9% reduction in average diameter luminal stenosis when compared to the original SECT without LEMONADE. These outcomes highlight LEMONADE's capacity to enable multi-energy CT imaging, mitigate blooming artifacts, and improve stenosis assessment for the widely available EID-CT. This has a high potential impact as most cCTA exams are performed on EID-CT.

Learned high-resolution cardiac CT imaging from ultra-high-resolution PCD-CT.

Coronary computed tomography angiography (cCTA) is a widely used non-invasive diagnostic exam for patients with coronary artery disease (CAD). However, most clinical CT scanners are limited in spatial resolution from use of energy-integrating detectors (EIDs). Radiological evaluation of CAD is challenging, as coronary arteries are small (3-4 mm diameter) and calcifications within them are highly attenuating, leading to blooming artifacts. As such, this is a task well suited for high spatial resolution. Recently, photon-counting-detector (PCD) CT became commercially available, allowing for ultra-high resolution (UHR) data acquisition. However, PCD-CTs are costly, restricting widespread accessibility. To address this problem, we propose a super resolution convolutional neural network (CNN): ILUMENATE (Improved LUMEN visualization through Artificial super-resoluTion imagEs), creating a high resolution (HR) image simulating UHR PCD-CT. The network was trained and validated using patches extracted from 8 patients with a modified U-Net architecture. Training input and labels consisted of UHR PCD-CT images reconstructed with a smooth kernel degrading resolution (LR input) and sharp kernel (HR label). The network learned the resolution difference and was tested on 5 unseen LR patients. We evaluated network performance quantitatively and qualitatively through visual inspection, line profiles to assess spatial resolution improvements, ROIs for CT number stability and noise assessment, structural similarity index (SSIM), and percent diameter luminal stenosis. Overall, ILUMENATE improved images quantitatively and qualitatively, creating sharper edges more closely resembling reconstructed HR reference images, maintained stable CT numbers with less than 4% difference, reduced noise by 28%, maintained structural similarity (average SSIM = 0.70), and reduced percent diameter stenosis with respect to input images. ILUMENATE demonstrates potential impact for CAD patient management, improving the quality of LR CT images bringing them closer to UHR PCD-CT images.

Proceedings of the 17th International Meeting on Fully 3D Image Reconstruction in Radiology and Nuclear Medicine.

Contained within this volume are the scholarly contributions presented in both oral and poster formats at Fully3D 2023: The 17th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine. This conference convened from July 16-21, 2023, at Stony Brook University in New York. For ease of reference, all papers are organized alphabetically according to the last names of the primary authors. Our heartfelt appreciation goes out to all participants who took the time to submit, present, and revise their work for inclusion in these proceedings. Collectively, we would also like to express our profound gratitude to our generous sponsors, detailed in subsequent pages, who have played an instrumental role in offering awards and facilitating the various conference activities. Additionally, our thanks extend to the diligent reporter who collated invaluable feedback from attendees, which can be found in the pages that follow. September 7, 2023 Fully3D 2023 Co-Chairs: Jerome Liang, Paul Vaska, and Chuan Huang.

Staging of breast cancer in the breast and regional lymph nodes using contrast-enhanced photon-counting detector CT: accuracy and potential impact on patient management.

OBJECTIVES: To describe the feasibility and evaluate the performance of multiphasic photon-counting detector (PCD) CT for detecting breast cancer and nodal metastases with correlative dynamic breast MRI and digital mammography as the reference standard. METHODS: Adult females with biopsy-proven breast cancer undergoing staging breast MRI were prospectively recruited to undergo a multiphasic PCD-CT using a 3-phase protocol: a non-contrast ultra-high-resolution (UHR) scan and 2 intravenous contrast-enhanced scans with 50 and 180 s delay. Three breast radiologists compared CT characteristics of the index malignancy, regional lymphadenopathy, and extramammary findings to MRI. RESULTS: Thirteen patients underwent both an MRI and PCD-CT (mean age: 53 years, range: 36-75 years). Eleven of thirteen cases demonstrated suspicious mass or non-mass enhancement on PCD-CT when compared to MRI. All cases with metastatic lymphadenopathy (3/3 cases) demonstrated early avid enhancement similar to the index malignancy. All cases with multifocal or multicentric disease on MRI were also identified on PCD-CT (3/3 cases), including a 4 mm suspicious satellite lesion. Four of five patients with residual suspicious post-biopsy calcifications on mammograms were detected on the UHR PCD-CT scan. Owing to increased field-of-view at PCD-CT, a 5 mm thoracic vertebral metastasis was identified at PCD-CT and not with the breast MRI. CONCLUSIONS: A 3-phase PCD-CT scan protocol shows initial promising results in characterizing breast cancer and regional lymphadenopathy similar to MRI and detects microcalcifications in 80% of cases. ADVANCES IN KNOWLEDGE: UHR and spectral capabilities of PCD-CT may allow for comprehensive characterization of breast cancer and may represent an alternative to breast MRI in select cases.

Motion artifact correction in cardiac CT using cross-phase temporospatial information and synergistic attention gate and spatial transformer sub-networks.

Objectives.To improve quality of coronary CT angiography (CCTA) images using a generalizable motion-correction algorithm.Approach. A neural network with attention gate and spatial transformer (ATOM) was developed to correct coronary motion. Phantom and patient CCTA images (39 males, 32 females, age range 19-92, scan date 02/2020 to 10/2021) retrospectively collected from dual-source CT were used to create training, development, and testing sets corresponding to 140- and 75 ms temporal resolution, with 75 ms images as labels. To test generalizability, ATOM was deployed for locally adaptive motion-correction in both 140- and 75 ms patient images. Objective metrics were used to assess motion-corrupted and corrected phantom and patient images, including structural-similarity-index (SSIM), dice-similarity-coefficient (DSC), peak-signal-noise-ratio (PSNR), and normalized root-mean-square-error (NRMSE). In objective quality assessment, ATOM was compared with several baseline networks, including U-net, U-net plus attention gate, U-net plus spatial transformer, VDSR, and ResNet. Two cardiac radiologists independently interpreted motion-corrupted and -corrected images at 75 and 140 ms in a blinded fashion and ranked diagnostic image quality (worst to best: 1-4, no ties).Main results. ATOM improved quality metrics (p< 0.05) before/after correction: in phantom, SSIM 0.87/0.95, DSC 0.85/0.93, PSNR 19.4/22.5, NRMSE 0.38/0.27; in patient images, SSIM 0.82/0.88, DSC 0.88/0.90, PSNR 30.0/32.0, NRMSE 0.16/0.12. ATOM provided more consistent improvement of objective image quality, compared to the presented baseline networks. The motion-corrected images received better ranks than un-corrected at the same temporal resolution (p< 0.05): 140 ms images 1.65/2.25, and 75 ms images 3.1/3.2. The motion-corrected 75 ms images received the best rank in 65% of testing cases. A fair-to-good inter-reader agreement was observed (Kappa score 0.58).Significance. ATOM reduces motion artifacts, improving visualization of coronary arteries. This algorithm can be used to virtually improve temporal resolution in both single- and dual-source CT.

Technical note: Exploring the detectability of coronary calcification using ultra-high-resolution photon-counting-detector CT.

BACKGROUND: Coronary calcification is a strong indicator of coronary artery disease, and patients with a "zero" coronary calcification score have a much lower risk of future cardiac events than those with even small amounts of calcium. However, false-negative (incorrect zero scores) may occur if small calcifications are missed at CT due to limited spatial resolution. PURPOSE: To demonstrate lower limits of detection for coronary calcification using an ultra-high-resolution (UHR) mode on a clinical photon-counting-detector CT (PCD-CT), compared to a conventional energy-integrating-detector CT (EID-CT). METHODS: Chicken eggshell fragments (0.4-0.8 mm) mimicking coronary calcifications were scanned on a clinical PCD-CT (NAEOTOM Alpha) in UHR mode and a conventional EID-CT (SOMATOM Force) with matched tube potential and radiation dose levels to the PCD-CT. PCD-CT images were reconstructed with a sharp kernel (Qr68) and a quantum iterative algorithm (QIR-3). Two sets of EID-CT images were reconstructed: routine clinical kernel (Qr36, ADMIRE-3) and a sharper kernel (Qr54) with similar noise to PCD-CT images. With institutional review board approval, in vivo exams performed with the PCD-CT in UHR mode were compared against patients' clinical EID-CT exams. The visibility of calcifications on PCD-CT and EID-CT images was assessed and compared qualitatively. RESULTS: PCD-CT images visualized all calcified fragments, while EID-CT failed to detect those below 0.6 mm using a routine protocol. EID-CT with Qr54 improved visibility but distorted boundaries. Calcifications were less visible on EID-CT than PCD-CT as phantom sizes increased. 0.6- and 0.7-mm calcified fragments were barely visible on 35- and 40-cm phantom EID-CT images. Patient cases showed small calcifications missed on EID-CT but detected on PCD-CT. CONCLUSION: At matched radiation dose, PCD-CT in UHR mode provided higher spatial resolution and improved the detectability of small calcified fragments for different phantom/patient sizes in comparison to EID-CT.

Transmission grating couples and enhances the second harmonic of the electron beam to generate tunable high-power terahertz radiation.

Vacuum electronic devices utilizing free-electron-based mechanisms are a crucial class of terahertz radiation sources that operate by modulating electron beams. In this study, we introduce what we believe is a novel approach to enhance the second harmonic of electron beams and substantially increase the output power at higher frequencies. Our method employs a planar grating for fundamental modulation and a transmission grating operating in the backward region to augment the harmonic coupling. The outcome is a high power output of the second harmonic signal. Contrasting with traditional linear electron beam harmonic devices, the proposed structure can achieve an output power increase of an order of magnitude. We have investigated this configuration computationally within the G-band. Our findings indicate that an electron beam density of 50 A/cm2 at 31.5 kV can produce a 0.202 THz center frequency signal with an output power of 4.59 W. As the electron beam voltage is adjusted from 23 kV to 38.5 kV, the output signal frequency shifts from 0.195 THz to 0.205 THz, generating several watts of power output. The starting oscillation current density at the center frequency point is 28 A/cm2, which is significantly lower in the G-band compared to conventional electron devices. This reduced current density has substantial implications for the advancement of terahertz vacuum devices.

PKAID-Net: Prior Knowledge Aware Iterative Denoising Neural Network for Photon Counting Detector CT.

An important feature enabled by Photon-Counting Detector (PCD) CT is the simultaneous acquisition of multi-energy data, which can produce virtual monoenergetic images (VMIs) at a high spatial resolution. However, noise levels observed in the high-resolution (HR) VMIs are markedly increased. Recent work involving deep learning methods has shown great potential in CT image denoising. Many CNN applications involve training using spatially co-registered low- and high-dose CT images featuring high and low image noise, respectively. However, this is implausible in routine clinical practice. Further, typical denoising methods treat each VMI energy level independently, without consideration of the valuable information in the spectral domain. To overcome these obstacles, we propose a prior knowledge-aware iterative denoising neural network (PKAID-Net). PKAID-Net offers two major benefits: first, it utilizes spectral information by including a lower-noise VMI as a prior input; and second, it iteratively constructs refined datasets for neural network training to improve the denoising performance. This study includes 10 patient coronary CT angiography (CTA) exams acquired on a clinical HR PCD-CT (NAEOTOM Alpha, Siemens Healthineers). The HR VMIs were reconstructed at 50 and 70 keV, using a sharp kernel (Bv68) and thin (0.6 mm, 0.3 mm increment) slice thickness. Results showed that the PKAID-Net provided a noise reduction of 96% and 70% relative to FBP and iterative reconstruction, respectively while maintaining spatial and spectral fidelity and a natural noise texture. These results demonstrate the noise reduction capacity of PKAID-Net as applied to cutting-edge PCD-CT data to enable HR, multi-energy cardiac CT imaging.

Quantifying lumen diameter in coronary artery stents with high-resolution photon counting detector CT and convolutional neural network denoising.

BACKGROUND: Small coronary arteries containing stents pose a challenge in CT imaging due to metal-induced blooming artifact. High spatial resolution imaging capability is as the presence of highly attenuating materials limits noninvasive assessment of luminal patency. PURPOSE: The purpose of this study was to quantify the effective lumen diameter within coronary stents using a clinical photon-counting-detector (PCD) CT in concert with a convolutional neural network (CNN) denoising algorithm, compared to an energy-integrating-detector (EID) CT system. METHODS: Seven coronary stents of different materials and inner diameters between 3.43 and 4.72 mm were placed in plastic tubes of diameters 3.96-4.87 mm containing 20 mg/mL of iodine solution, mimicking stented contrast-enhanced coronary arteries. Tubes were placed parallel with or perpendicular to the scanner's z-axis in an anthropomorphic phantom emulating an average-sized patient and scanned with a clinical EID-CT and PCD-CT. EID scans were performed using our standard coronary computed tomography angiography (cCTA) protocol (120 kV, 180 quality reference mAs). PCD scans were performed using the ultra-high-resolution (UHR) mode (120 × 0.2 mm collimation) at 120 kV with tube current adjusted so that CTDIvol was matched to that of EID scans. EID images were reconstructed per our routine clinical protocol (Br40, 0.6 mm thickness), and with the sharpest available kernel (Br69). PCD images were reconstructed at a thickness of 0.6 mm and a dedicated sharp kernel (Br89) which is only possible with the PCD UHR mode. To address increased image noise introduced by the Br89 kernel, an image-based CNN denoising algorithm was applied to the PCD images of stents scanned parallel to the scanner's z-axis. Stents were segmented based on full-width half maximum thresholding and morphological operations, from which effective lumen diameter was calculated and compared to reference sizes measured with a caliper. RESULTS: Substantial blooming artifacts were observed on EID Br40 images, resulting in larger stent struts and reduced lumen diameter (effective diameter underestimated by 41% and 47% for parallel and perpendicular orientations, respectively). Blooming artifacts were observed on EID Br69 images with 19% and 31% underestimation of lumen diameter compared to the caliper for parallel and perpendicular scans, respectively. Overall image quality was substantially improved on PCD, with higher spatial resolution and reduced blooming artifacts, resulting in the clearer delineation of stent struts. Effective lumen diameters were underestimated by 9% and 19% relative to the reference for parallel and perpendicular scans, respectively. CNN reduced image noise by about 50% on PCD images without impacting lumen quantification (<0.3% difference). CONCLUSION: The PCD UHR mode improved in-stent lumen quantification for all seven stents as compared to EID images due to decreased blooming artifacts. Implementation of CNN denoising algorithms to PCD data substantially improved image quality.

Pie-Net: Prior-information-enabled deep learning noise reduction for coronary CT angiography acquired with a photon counting detector CT.

BACKGROUND: Photon-counting-detector CT (PCD-CT) enables the production of virtual monoenergetic images (VMIs) at a high spatial resolution (HR) via simultaneous acquisition of multi-energy data. However, noise levels in these HR VMIs are markedly increased. PURPOSE: To develop a deep learning technique that utilizes a lower noise VMI as prior information to reduce image noise in HR, PCD-CT coronary CT angiography (CTA). METHODS: Coronary CTA exams of 10 patients were acquired using PCD-CT (NAEOTOM Alpha, Siemens Healthineers). A prior-information-enabled neural network (Pie-Net) was developed, treating one lower-noise VMI (e.g., 70 keV) as a prior input and one noisy VMI (e.g., 50 keV or 100 keV) as another. For data preprocessing, noisy VMIs were reconstructed by filtered back-projection (FBP) and iterative reconstruction (IR), which were then subtracted to generate "noise-only" images. Spatial decoupling was applied to the noise-only images to mitigate overfitting and improve randomization. Thicker slice averaging was used for the IR and prior images. The final training inputs for the convolutional neural network (CNN) inside the Pie-Net consisted of thicker-slice signal images with the reinsertion of spatially decoupled noise-only images and the thicker-slice prior images. The CNN training labels consisted of the corresponding thicker-slice label images without noise insertion. Pie-Net's performance was evaluated in terms of image noise, spatial detail preservation, and quantitative accuracy, and compared to a U-net-based method that did not include prior information. RESULTS: Pie-Net provided strong noise reduction, by 95 ± 1% relative to FBP and by 60 ± 8% relative to IR. For HR VMIs at different keV (e.g., 50 keV or 100 keV), Pie-Net maintained spatial and spectral fidelity. The inclusion of prior information from the PCD-CT data in the spectral domain was able to improve a robust deep learning-based denoising performance compared to the U-net-based method, which caused some loss of spatial detail and introduced some artifacts. CONCLUSION: The proposed Pie-Net achieved substantial noise reduction while preserving HR VMI's spatial and spectral properties.

Exploring Dual-Energy CT Spectral Information for Machine Learning-Driven Lesion Diagnosis in Pre-Log Domain.

In this study, we proposed a computer-aided diagnosis (CADx) framework under dual-energy spectral CT (DECT), which operates directly on the transmission data in the pre-log domain, called CADxDE, to explore the spectral information for lesion diagnosis. The CADxDE includes material identification and machine learning (ML) based CADx. Benefits from DECT's capability of performing virtual monoenergetic imaging with the identified materials, the responses of different tissue types (e.g., muscle, water, and fat) in lesions at each energy can be explored by ML for CADx. Without losing essential factors in the DECT scan, a pre-log domain model-based iterative reconstruction is adopted to obtain decomposed material images, which are then used to generate the virtual monoenergetic images (VMIs) at selected n energies. While these VMIs have the same anatomy, their contrast distribution patterns contain rich information along with the n energies for tissue characterization. Thus, a corresponding ML-based CADx is developed to exploit the energy-enhanced tissue features for differentiating malignant from benign lesions. Specifically, an original image-driven multi-channel three-dimensional convolutional neural network (CNN) and extracted lesion feature-based ML CADx methods are developed to show the feasibility of CADxDE. Results from three pathologically proven clinical datasets showed 4.01% to 14.25% higher AUC (area under the receiver operating characteristic curve) scores than the scores of both the conventional DECT data (high and low energy spectrum separately) and the conventional CT data. The mean gain >9.13% in AUC scores indicated that the energy spectral-enhanced tissue features from CADxDE have great potential to improve lesion diagnosis performance.

A Joint-Parameter Estimation and Bayesian Reconstruction Approach to Low-Dose CT.

Most penalized maximum likelihood methods for tomographic image reconstruction based on Bayes' law include a freely adjustable hyperparameter to balance the data fidelity term and the prior/penalty term for a specific noise-resolution tradeoff. The hyperparameter is determined empirically via a trial-and-error fashion in many applications, which then selects the optimal result from multiple iterative reconstructions. These penalized methods are not only time-consuming by their iterative nature, but also require manual adjustment. This study aims to investigate a theory-based strategy for Bayesian image reconstruction without a freely adjustable hyperparameter, to substantially save time and computational resources. The Bayesian image reconstruction problem is formulated by two probability density functions (PDFs), one for the data fidelity term and the other for the prior term. When formulating these PDFs, we introduce two parameters. While these two parameters ensure the PDFs completely describe the data and prior terms, they cannot be determined by the acquired data; thus, they are called complete but unobservable parameters. Estimating these two parameters becomes possible under the conditional expectation and maximization for the image reconstruction, given the acquired data and the PDFs. This leads to an iterative algorithm, which jointly estimates the two parameters and computes the to-be reconstructed image by maximizing a posteriori probability, denoted as joint-parameter-Bayes. In addition to the theoretical formulation, comprehensive simulation experiments are performed to analyze the stopping criterion of the iterative joint-parameter-Bayes method. Finally, given the data, an optimal reconstruction is obtained without any freely adjustable hyperparameter by satisfying the PDF condition for both the data likelihood and the prior probability, and by satisfying the stopping criterion. Moreover, the stability of joint-parameter-Bayes is investigated through factors such as initialization, the PDF specification, and renormalization in an iterative manner. Both phantom simulation and clinical patient data results show that joint-parameter-Bayes can provide comparable reconstructed image quality compared to the conventional methods, but with much less reconstruction time. To see the response of the algorithm to different types of noise, three common noise models are introduced to the simulation data, including white Gaussian noise to post-log sinogram data, Poisson-like signal-dependent noise to post-log sinogram data and Poisson noise to the pre-log transmission data. The experimental outcomes of the white Gaussian noise reveal that the two parameters estimated by the joint-parameter-Bayes method agree well with simulations. It is observed that the parameter introduced to satisfy the prior's PDF is more sensitive to stopping the iteration process for all three noise models. A stability investigation showed that the initial image by filtered back projection is very robust. Clinical patient data demonstrated the effectiveness of the proposed joint-parameter-Bayes and stopping criterion.

Immunotherapy treatment outcome prediction in metastatic melanoma through an automated multi-objective delta-radiomics model.

Based on recent studies, immunotherapy led by immune checkpoint inhibitors has significantly improved the patient survival rate and effectively reduced the recurrence risk. However, immunotherapy has different therapeutic effects for different patients, leading to difficulties in predicting the treatment response. Conversely, delta-radiomic features, which measure the difference between pre- and post-treatment through quantitative image features, have proven to be promising descriptors for treatment outcome prediction. Consequently, we developed an effective model termed as the automated multi-objective delta-radiomics (Auto-MODR) model for the prediction of immunotherapy response in metastatic melanoma. In Auto-MODR, delta-radiomic features and traditional radiomic features were used as inputs. Furthermore, a novel automated multi-objective model was developed to obtain more reliable and balanced results between sensitivity and specificity. We conducted extensive comparisons with existing studies on treatment outcome prediction. Our method achieved an area under the curve (AUC) of 0.86 in a cross-validation study and an AUC of 0.73 in an independent study. Compared with the model using conventional radiomic features (pre- and post-treatment) only, better performance can be obtained when conventional radiomic and delta-radiomic features are combined. Furthermore, Auto-MODR outperformed the currently available radiomic strategies.

A multiscale continuum model of the vertebrate outer retina: The temporal dynamics of background-induced flicker enhancement.

The retina is a part of the central nervous system that is accessible, well documented, and studied by researchers spanning the clinical, experimental, and theoretical sciences. Here, we mathematically model the subcircuits of the outer plexiform layer of the retina on two spatial scales: that of an individual synapse and that of the scale of the receptive field (hundreds to thousands of synapses). To this end we formulate a continuum spine model (a partial differential equation system) that incorporates the horizontal cell syncytium and its numerous processes (spines) within cone pedicles. With this multiscale modeling approach, detailed biophysical mechanisms at the synaptic level are retained while scaling up to the receptive field level. As an example of its utility, the model is applied to study background-induced flicker enhancement in which the onset of a dim background enhances the center flicker response of horizontal cells. Simulation results, in comparison with flicker enhancement data for square, slit, and disk test regions, suggest that feedback mechanisms that are voltage-axis modulators of cone calcium channels (for example, ephaptic and/or pH feedback) are robust in capturing the temporal dynamics of background-induced flicker enhancement. The value and potential of this continuum spine approach is that it provides a framework for mathematically modeling the input-output properties of the entire receptive field of the outer retina while implementing the latest models for transmission mechanisms at the synaptic level.

Spectrum Estimation-Guided Iterative Reconstruction Algorithm for Dual Energy CT.

X-ray spectrum plays a very important role in dual energy computed tomography (DECT) reconstruction. Because it is difficult to measure x-ray spectrum directly in practice, efforts have been devoted into spectrum estimation by using transmission measurements. These measurement methods are independent of the image reconstruction, which bring extra cost and are time consuming. Furthermore, the estimated spectrum mismatch would degrade the quality of the reconstructed images. In this paper, we propose a spectrum estimation-guided iterative reconstruction algorithm for DECT which aims to simultaneously recover the spectrum and reconstruct the image. The proposed algorithm is formulated as an optimization framework combining spectrum estimation based on model spectra representation, image reconstruction, and regularization for noise suppression. To resolve the multi-variable optimization problem of simultaneously obtaining the spectra and images, we introduce the block coordinate descent (BCD) method into the optimization iteration. Both the numerical simulations and physical phantom experiments are performed to verify and evaluate the proposed method. The experimental results validate the accuracy of the estimated spectra and reconstructed images under different noise levels. The proposed method obtains a better image quality compared with the reconstructed images from the known exact spectra and is robust in noisy data applications.

Crystal structure of a papain-fold protein without the catalytic residue: a novel member in the cysteine proteinase family.

A 31kDa cysteine protease, SPE31, was isolated from the seeds of a legume plant, Pachyrizhus erosus. The protein was purified, crystallized and the 3D structure solved using molecular replacement. The cDNA was obtained by RT PCR followed by amplification using mRNA isolated from the seeds of the legume plant as a template. Analysis of the cDNA sequence and the 3D structure indicated the protein to belong to the papain family. Detailed analysis of the structure revealed an unusual replacement of the conserved catalytic Cys with Gly. Replacement of another conserved residue Ala/Gly by a Phe sterically blocks the access of the substrate to the active site. A polyethyleneglycol molecule and a natural peptide fragment were bound to the surface of the active site. Asn159 was found to be glycosylated. The SPE31 cDNA sequence shares several features with P34, a protein found in soybeans, that is implicated in plant defense mechanisms as an elicitor receptor binding to syringolide. P34 has also been shown to interact with vegetative storage proteins and NADH-dependent hydroxypyruvate reductase. These roles suggest that SPE31 and P34 form a unique subfamily within the papain family. The crystal structure of SPE31 complexed with a natural peptide ligand reveals a unique active site architecture. In addition, the clear evidence of glycosylated Asn159 provides useful information towards understanding the functional mechanism of SPE31/P34.

Purification, characterization and preliminary crystallographic studies of a cysteine protease from Pachyrrhizus erosus seeds.

The proteins Spe31 and Spe32, named after their respective molecular weights of about 31 and 32 kDa, were purified simultaneously from the seeds of Pachyrrhizus erosus. They cannot be separated from each other by column chromatography. N-terminal sequence analysis indicated that they belonged to the papain family of cysteine proteases. An in-gel activity assay revealed that Spe31 possesses proteolytic activity while Spe32 only displays very weak activity for protein degradation. Both of them are glycoproteins as detected by the periodic acid and Schiff's reagent method. Crystals were obtained from the protein mixture by the hanging-drop vapour-diffusion method; they diffracted to a resolution of 2.61 A on an in-house X-ray source. The crystals belong to space group P4(1(3))2(1)2, with unit-cell parameters a = b = 61.96, c = 145.61 A. Gel electrophoresis under non-denaturing conditions showed that the protein crystallized was Spe31.

cDNA cloning, expression, and mutagenesis of a PR-10 protein SPE-16 from the seeds of Pachyrrhizus erosus.

SPE-16 is a new 16kDa protein that has been purified from the seeds of Pachyrrhizus erosus. It's N-terminal amino acid sequence shows significant sequence homology to pathogenesis-related class 10 proteins. cDNA encoding 150 amino acids was cloned by RT-PCR and the gene sequence proved SPE-16 to be a new member of PR-10 family. The cDNA was cloned into pET15b plasmid and expressed in Escherichia coli. The bacterially expressed SPE-16 also demonstrated ribonuclease-like activity in vitro. Site-directed mutation of three conserved amino acids E95A, E147A, Y150A, and a P-loop truncated form were constructed and their different effects on ribonuclease activities were observed. SPE-16 is also able to bind the fluorescent probe 8-anilino-1-naphthalenesulfonate (ANS) in the native state. The ANS anion is a much-utilized "hydrophobic probe" for proteins. This binding activity indicated another biological function of SPE-16.

Purification, characterization and preliminary crystallographic studies of a PR-10 protein from Pachyrrhizus erosus seeds.

A 16 kDa protein SPE16 was purified from the seeds of Pachyrrhizus erosus. Its N-terminal amino-acid sequence showed significant sequence homology to pathogenesis-related proteins from the PR-10 family. An activity assay indicated that SPE16 possesses ribonuclease activity as do some other PR-10 proteins. SPE16 crystals were obtained by the hanging-drop vapour-diffusion method. The space group is P2(1)2(1)2(1), with unit-cell parameters a = 53.36, b = 63.70, c = 72.96 A.