3D Algebraic Iterative Reconstruction for Cone-Beam X-Ray Differential Phase-Contrast Computed Tomography

Due to the potential of compact imaging systems with magnified spatial resolution and contrast, cone-beam x-ray differential phase-contrast computed tomography (DPC-CT) has attracted significant interest. The current proposed FDK reconstruction algorithm with the Hilbert imaginary filter will induce severe cone-beam artifacts when the cone-beam angle becomes large. In this paper, we propose an algebraic iterative reconstruction (AIR) method for cone-beam DPC-CT and report its experiment results. This approach considers the reconstruction process as the optimization of a discrete representation of the object function to satisfy a system of equations that describes the cone-beam DPC-CT imaging modality. Unlike the conventional iterative algorithms for absorption-based CT, it involves the derivative operation to the forward projections of the reconstructed intermediate image to take into account the differential nature of the DPC projections. This method is based on the algebraic reconstruction technique, reconstructs the image ray by ray, and is expected to provide better derivative estimates in iterations. This work comprises a numerical study of the algorithm and its experimental verification using a dataset measured with a three-grating interferometer and a mini-focus x-ray tube source. It is shown that the proposed method can reduce the cone-beam artifacts and performs better than FDK under large cone-beam angles. This algorithm is of interest for future cone-beam DPC-CT applications.     

Noise properties of reconstructed images in a kilo-voltage on-board imaging system with iterative reconstruction techniques: A phantom study

X-ray computed tomography (CT) images obtained with a kilo-voltage (kV) on-board imaging (OBI) system improve the accuracy of patient setup and treatment planning. The use of iterative reconstruction techniques (IRTs) for CT imaging can also reduce radiation dose compared to analytic reconstruction techniques. Despite these improvements, the image quality varies with IRTs, and the noise structure of reconstructed images can be distorted by IRTs. In this study, the noise properties and spatial resolution of the images reconstructed by IRTs were evaluated in terms of conventional noise metrics, high-order statistics, noise spectral density (NSD) and modulation transfer function (MTF) at different radiation doses. A kV OBI system mounted on a Varian Trilogy machine and a CATPHAN600 phantom were used to obtain projections, and the projections were reconstructed by Feldkamp (FDK), algebraic reconstruction technique (ART), maximum-likelihood expectation–maximization (MLEM) and total variation (TV) minimization algorithms. The reconstructed images were compared according to mean, standard deviation, skewness, kurtosis, NSD and MTF at different radiation doses. The results demonstrated that the noise properties and spatial resolution of reconstructed images depend on the type of IRT and the radiation dose. The noise structures are altered by IRTs and can be characterized by high-order statistics and NSD, as well as conventional noise metrics. In conclusion, high-order statistics and NSD should be considered in order to provide detailed information for the images reconstructed by IRTs. Also, trade-off among noise properties, spatial resolution and contrast is important to optimize image quality obtained using IRTs.     

Evaluation of digital tomosynthesis reconstruction algorithms used to reduce metal artifacts for arthroplasty: A phantom study

To investigate methods to reduce metal artifacts during digital tomosynthesis for arthroplasty, we evaluated five algorithms with and without metal artifact reduction (MAR)-processing tested under different radiation doses (0.54, 0.47, and 0.33 mSv): adaptive steepest descent projection onto convex sets (ASD-POCS), simultaneous algebraic reconstruction technique total variation (SART-TV), filtered back projection (FBP), maximum likelihood expectation maximization (MLEM), and SART. The algorithms were assessed by determining the artifact index (AI) and artifact spread function (ASF) on a prosthesis phantom. The AI data were statistically analyzed by two-way analysis of variance. Without MAR-processing, the greatest degree of effectiveness of the MLEM algorithm for reducing prosthetic phantom-related metal artifacts was achieved by quantification using the AI (MLEM vs. ASD-POCS, SART-TV, SART, and FBP; all P < 0.05). With MAR-processing, the greatest degree of effectiveness of the MLEM, ASD-POCS, SART-TV, and SART algorithms for reducing prosthetic phantom-related metal artifacts was achieved by quantification using the AI (MLEM, ASD-POCS, SART-TV, and SART vs. FBP; all P < 0.05). When assessed by ASF, metal artifact reduction was largest for the MLEM algorithm without MAR-processing and ASD-POCS, SART-TV, and SART algorithm with MAR-processing. In ASF, the effect of metal artifact reduction was always greater at reduced radiation doses, regardless of which reconstruction algorithm with and without MAR-processing was used. In this phantom study, the MLEM algorithm without MAR-processing and ASD-POCS, SART-TV, and SART algorithm with MAR-processing gave improved metal artifact reduction.     

Image reconstruction of optical computed tomography by using the algebraic reconstruction technique for dose readouts of polymer gel dosimeters

Optical computed tomography (optical CT) has been proven to be a useful tool for dose readouts of polymer gel dosimeters. In this study, the algebraic reconstruction technique (ART) for image reconstruction of gel dosimeters was used to improve the image quality of optical CT. Cylindrical phantoms filled with N-isopropyl-acrylamide polymer gels were irradiated using a medical linear accelerator. A circular dose distribution and a hexagonal dose distribution were produced by applying the VMAT technique and the six-field dose delivery, respectively. The phantoms were scanned using optical CT, and the images were reconstructed using the filtered back-projection (FBP) algorithm and the ART. For the circular dose distribution, the ART successfully reduced the ring artifacts and noise in the reconstructed image. For the hexagonal dose distribution, the ART reduced the hot spots at the entrances of the beams and increased the dose uniformity in the central region. Within 50% isodose line, the gamma pass rates for the 2 mm/3% criteria for the ART and FBP were 99.2% and 88.1%, respectively. The ART could be used for the reconstruction of optical CT images to improve image quality and provide accurate dose conversion for polymer gel dosimeters.     

A Novel Prior- and Motion-Based Compressed Sensing Method for Small-Animal Respiratory Gated CT

Low-dose protocols for respiratory gating in cardiothoracic small-animal imaging lead to streak artifacts in the images reconstructed with a Feldkamp-Davis-Kress (FDK) method. We propose a novel prior- and motion-based reconstruction (PRIMOR) method, which improves prior-based reconstruction (PBR) by adding a penalty function that includes a model of motion. The prior image is generated as the average of all the respiratory gates, reconstructed with FDK. Motion between respiratory gates is estimated using a nonrigid registration method based on hierarchical B-splines. We compare PRIMOR with an equivalent PBR method without motion estimation using as reference the reconstruction of high dose data. From these data acquired with a micro-CT scanner, different scenarios were simulated by changing photon flux and number of projections. Methods were evaluated in terms of contrast-to-noise-ratio (CNR), mean square error (MSE), streak artefact indicator (SAI), solution error norm (SEN), and correction of respiratory motion. Also, to evaluate the effect of each method on lung studies quantification, we have computed the Jaccard similarity index of the mask obtained from segmenting each image as compared to those obtained from the high dose reconstruction. Both iterative methods greatly improved FDK reconstruction in all cases. PBR was prone to streak artifacts and presented blurring effects in bone and lung tissues when using both a low number of projections and low dose. Adopting PBR as a reference, PRIMOR increased CNR up to 33% and decreased MSE, SAI and SEN up to 20%, 4% and 13%, respectively. PRIMOR also presented better compensation for respiratory motion and higher Jaccard similarity index. In conclusion, the new method proposed for low-dose respiratory gating in small-animal scanners shows an improvement in image quality and allows a reduction of dose or a reduction of the number of projections between two and three times with respect to previous PBR approaches.     

The Impact of Iterative Reconstruction on Computed Tomography Radiation Dosimetry: Evaluation in a Routine Clinical Setting

Purpose

To evaluate the effect of introduction of iterative reconstruction as a mandated software upgrade on radiation dosimetry in routine clinical practice over a range of computed tomography examinations.

Methods

Random samples of scanning data were extracted from a centralised Picture Archiving Communication System pertaining to 10 commonly performed computed tomography examination types undertaken at two hospitals in Western Australia, before and after the introduction of iterative reconstruction. Changes in the mean dose length product and effective dose were evaluated along with estimations of associated changes to annual cancer incidence.

Results

We observed statistically significant reductions in the effective radiation dose for head computed tomography (22–27%) consistent with those reported in the literature. In contrast the reductions observed for non-contrast chest (37–47%); chest pulmonary embolism study (28%), chest/abdominal/pelvic study (16%) and thoracic spine (39%) computed tomography. Statistically significant reductions in radiation dose were not identified in angiographic computed tomography. Dose reductions translated to substantial lowering of the lifetime attributable risk, especially for younger females, and estimated numbers of incident cancers.

Conclusion

Reduction of CT dose is a priority Iterative reconstruction algorithms have the potential to significantly assist with dose reduction across a range of protocols. However, this reduction in dose is achieved via reductions in image noise. Fully realising the potential dose reduction of iterative reconstruction requires the adjustment of image factors and forgoing the noise reduction potential of the iterative algorithm. Our study has demonstrated a reduction in radiation dose for some scanning protocols, but not to the extent experimental studies had previously shown or in all protocols expected, raising questions about the extent to which iterative reconstruction achieves dose reduction in real world clinical practice.     

Automatic exposure control at single- and dual-heartbeat CTCA on a 320-MDCT volume scanner: Effect of heart rate,exposure phase window setting,and reconstruction algorithm

PurposeTo investigate whether electrocardiogram (ECG)-gated single- and dual-heartbeat computed tomography coronary angiography (CTCA) with automatic exposure control (AEC) yields images with uniform image noise at reduced radiation doses.Materials and methodsUsing an anthropomorphic chest CT phantom we performed prospectively ECG-gated single- and dual-heartbeat CTCA on a second-generation 320-multidetector CT volume scanner. The exposure phase window was set at 75%, 70–80%, 40–80%, and 0–100% and the heart rate at 60 or 80 or corr80 bpm; images were reconstructed with filtered back projection (FBP) or iterative reconstruction (IR, adaptive iterative dose reduction 3D). We applied AEC and set the image noise level to 20 or 25 HU. For each technique we determined the image noise and the radiation dose to the phantom center.ResultsWith half-scan reconstruction at 60 bpm, a 70–80% phase window- and a 20-HU standard deviation (SD) setting, the imagenoise level and -variation along the z axis manifested similar curves with FBP and IR. With half-scan reconstruction, the radiation dose to the phantom center with 70–80% phase window was 18.89 and 12.34 mGy for FBP and 4.61 and 3.10 mGy for IR at an SD setting SD of 20 and 25 HU, respectively. At 80 bpm with two-segment reconstruction the dose was approximately twice that of 60 bpm at both SD settings. However, increasing radiation dose at corr80 bpm was suppressed to 1.39 times compared to 60 bpm.ConclusionAEC at ECG-gated single- and dual-heartbeat CTCA controls the image noise at different radiation dose.     

Enhancement of soft-tissue contrast in cone-beam CT using an anti-scatter grid with a sparse sampling approach

PurposeAnti-scatter grids suppress the scatter substantially thus improving image contrast in radiography. However, its active use in cone-beam CT for the purpose of improving contrast-to-noise ratio (CNR) has not been successful mainly due to the increased noise related to Poisson statistics of photons. This paper proposes a sparse-view scanning approach to address the above issue.MethodCompared to the conventional cone-beam CT imaging framework, the proposed method reduces the number of projections and increases exposure in each projection to enhance image quality without an additional cost of radiation dose to patients. For image reconstruction from sparse-view data, an adaptive-steepest-descent projection-onto-convex-sets (ASD POCS) algorithm regularized by total-variation (TV) minimization was adopted. Contrast and CNR with various scattering conditions were evaluated in projection domain by a simulation study using GATE. Then we evaluated contrast, resolution, and image uniformity in CT image domain with Catphan phantom. A head phantom with soft-tissue structures was also employed for demonstrating a realistic application. A virtual grid-based estimation and reduction of scatter has also been implemented for comparison with the real anti-scatter grid.ResultsIn the projection domain evaluation, contrast and CNR enhancement was observed when using an anti-scatter grid compared to the virtual grid. In the CT image domain, the proposed method produced substantially higher contrast and CNR of the low-contrast structures with much improved image uniformity.ConclusionWe have shown that the proposed method can provide high-quality CBCT images particularly with an increased contrast of soft-tissue at a neutral dose for image-guidance.     

Sparse-view CT reconstruction based on multi-level wavelet convolution neural network

Sparse-view computed tomography (CT) is a recent approach to reducing the radiation dose in patients and speeding up the data acquisition. Consequently, sparse-view CT has been of particular interest among researchers within the CT community. Advanced reconstruction algorithms for sparse-view CT, such as iterative algorithms with total-variation (TV), have been studied along with the problem of increasing computational burden and the blurring of artifacts in the reconstructed images. Studies on deep-learning-based approaches applying U-NET have recently achieved remarkable outcomes in various domains including low-dose CT. In this study, we propose a new method for sparse-view CT reconstruction based on a multi-level wavelet convolutional neural network (MWCNN). First, a filtered backprojection (FBP) was used to reconstruct a sparsely sampled sinogram from 60, 120, and 180 projections. Subsequently, the sparse-view data obtained from FBP were fed to a deep-learning network, i.e., the MWCNN. Our network architecture combines a wavelet transform and modified U-NET without pooling. By replacing the pooling function with the wavelet transform, the receptive field is enlarged to improve the performance. We qualitatively and quantitatively evaluated the interpolation, iterative TV method, and standard U-NET in terms of a reduction in the streaking artifacts and a preservation of the anatomical structures. When compared with other methods, the proposed method showed the highest performance based on various evaluation parameters such as the structural similarity, root mean square error, and resolution. These results indicate that the MWCNN possesses a powerful potential for achieving a sparse-view CT reconstruction.     

Resolution and noise performance of sparse view X-ray CT reconstruction via Lp-norm regularization

ObjectivesAdaptive steepest descent projection onto convex sets (ASD-POCS) algorithms with Lp-norm (0 < p ≤ 1) regularization have shown great promise in sparse-view X-ray CT reconstruction. However, the difference in p value selection can lead to varying algorithm performance of noise and resolution. Therefore, it is imperative to develop a reliable method to evaluate the resolution and noise properties of the ASD-POCS algorithms under different Lp-norm priors.MethodsA comparative performance evaluation of ASD-POCS algorithms under different Lp-norm (0 < p ≤ 2) priors were performed in terms of modulation transfer function (MTF), noise power spectrum (NPS) and noise equivalent quanta (NEQ). Simulation data sets from the EGSnrc/BEAMnrc Monte Carlo system and an actual mouse data set were used for algorithms comparison.ResultsA considerable MTF improvement can be achieved with the decrement of p. L1 regularization based algorithm obtains the best noise performance, and shows superiority in NEQ evaluation. The advantage of L1-norm prior is also confirmed by the reconstructions from the actual mouse data set through contrast to noise ratio (CNR) comparison.ConclusionAlthough the ASD-POCS algorithms using small Lp-norm (p ≤ 0.5) priors yield a higher MTF than do the high Lp-norms, the best noise-resolution performance is achieved when p is between 0.8 and 1. The results are expected to be a reference to the choice of p in Lp-norm (0 < p ≤ 2) regularization.     

Elimination of biological and physical artifacts in abdomen and brain computed tomography procedures using filtering techniques

IntroductionMedical images are usually affected by biological and physical artifacts or noise, which reduces image quality and hence poses difficulties in visual analysis, interpretation and thus requires higher doses and increased radiographs repetition rate.ObjectivesThis study aims at assessing image quality during CT abdomen and brain examinations using filtering techniques as well as estimating the radiogenic risk associated with CT abdomen and brain examinations.Materials and MethodsThe data were collected from the Radiology Department at Royal Care International (RCI) Hospital, Khartoum, Sudan. The study included 100 abdominal CT images and 100 brain CT images selected from adult patients. Filters applied are namely: Mean filter, Gaussian filter, Median filter and Minimum filter. In this study, image quality after denoising is measured based on the Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and the Structural Similarity Index Metric (SSIM).ResultsThe results show that the images quality parameters become higher after applications of filters. Median filter showed improved image quality as interpreted by the measured parameters: PSNR and SSIM, and it is thus considered as a better filter for removing the noise from all other applied filters.DiscussionThe noise removed by the different filters applied to the CT images resulted in enhancing high quality images thereby effectively revealing the important details of the images without increasing the patients’ risks from higher doses.ConclusionsFiltering and image reconstruction techniques not only reduce the dose and thus the radiation risks, but also enhances high quality imaging which allows better diagnosis.     

System Matrix Analysis for Computed Tomography Imaging

In practical applications of computed tomography imaging (CT), it is often the case that the set of projection data is incomplete owing to the physical conditions of the data acquisition process. On the other hand, the high radiation dose imposed on patients is also undesired. These issues demand that high quality CT images can be reconstructed from limited projection data. For this reason, iterative methods of image reconstruction have become a topic of increased research interest. Several algorithms have been proposed for few-view CT. We consider that the accurate solution of the reconstruction problem also depends on the system matrix that simulates the scanning process. In this work, we analyze the application of the Siddon method to generate elements of the matrix and we present results based on real projection data.     

Imaging performance of phase-contrast breast computed tomography with synchrotron radiation and a CdTe photon-counting detector

PurposeWithin the SYRMA-CT collaboration based at the ELETTRA synchrotron radiation (SR) facility the authors investigated the imaging performance of the phase-contrast computed tomography (CT) system dedicated to monochromatic in vivo 3D imaging of the female breast, for breast cancer diagnosis.MethodsTest objects were imaged at 38 keV using monochromatic SR and a high-resolution CdTe photon-counting detector. Signal and noise performance were evaluated using modulation transfer function (MTF) and noise power spectrum. The analysis was performed on the images obtained with the application of a phase retrieval algorithm as well as on those obtained without phase retrieval. The contrast to noise ratio (CNR) and the capability of detecting test microcalcification clusters and soft masses were investigated.ResultsFor a voxel size of (60 μm)³, images without phase retrieval showed higher spatial resolution (6.7 mm⁻¹ at 10% MTF) than corresponding images with phase retrieval (2.5 mm⁻¹). Phase retrieval produced a reduction of the noise level and an increase of the CNR by more than one order of magnitude, compared to raw phase-contrast images. Microcalcifications with a diameter down to 130 μm could be detected in both types of images.ConclusionsThe investigation on test objects indicates that breast CT with a monochromatic SR source is technically feasible in terms of spatial resolution, image noise and contrast, for in vivo 3D imaging with a dose comparable to that of two-view mammography. Images obtained with the phase retrieval algorithm showed the best performance in the trade-off between spatial resolution and image noise.     

First application of the GPU-based software framework TIGRE for proton CT image reconstruction

In proton therapy, the knowledge of the proton stopping power, i.e. the energy deposition per unit length within human tissue, is essential for accurate treatment planning. One suitable method to directly measure the stopping power is proton computed tomography (pCT). Due to the proton interaction mechanisms in matter, pCT image reconstruction faces some challenges: the unique path of each proton has to be considered separately in the reconstruction process adding complexity to the reconstruction problem. This study shows that the GPU-based open-source software toolkit TIGRE, which was initially intended for X-ray CT reconstruction, can be applied to the pCT image reconstruction problem using a straight line approach for the proton path. This simplified approach allows for reconstructions within seconds.To validate the applicability of TIGRE to pCT, several Monte Carlo simulations modeling a pCT setup with two Catphan® modules as phantoms were performed. Ordered-Subset Simultaneous Algebraic Reconstruction Technique (OS-SART) and Adaptive-Steepest-Descent Projection Onto Convex Sets (ASD-POCS) were used for image reconstruction. Since the accuracy of the approach is limited by the straight line approximation of the proton path, requirements for further improvement of TIGRE for pCT are addressed.     

Combining Automatic Tube Current Modulation with Adaptive Statistical Iterative Reconstruction for Low-Dose Chest CT Screening

Objective

To reduce radiation dose while maintaining image quality in low-dose chest computed tomography (CT) by combining adaptive statistical iterative reconstruction (ASIR) and automatic tube current modulation (ATCM).

Methods

Patients undergoing cancer screening (n = 200) were subjected to 64-slice multidetector chest CT scanning with ASIR and ATCM. Patients were divided into groups 1, 2, 3, and 4 (n = 50 each), with a noise index (NI) of 15, 20, 30, and 40, respectively. Each image set was reconstructed with 4 ASIR levels (0% ASIR, 30% ASIR, 50% ASIR, and 80% ASIR) in each group. Two radiologists assessed subjective image noise, image artifacts, and visibility of the anatomical structures. Objective image noise and signal-to-noise ratio (SNR) were measured, and effective dose (ED) was recorded.

Results

Increased NI was associated with increased subjective and objective image noise results (P<0.001), and SNR decreased with increasing NI (P<0.001). These values improved with increased ASIR levels (P<0.001). Images from all 4 groups were clinically diagnosable. Images with NI = 30 and 50% ASIR had average subjective image noise scores and nearly average anatomical structure visibility scores, with a mean objective image noise of 23.42 HU. The EDs for groups 1, 2, 3 and 4 were 2.79±1.17, 1.69±0.59, 0.74±0.29, and 0.37±0.22 mSv, respectively. Compared to group 1 (NI = 15), the ED reductions were 39.43%, 73.48%, and 86.74% for groups 2, 3, and 4, respectively.

Conclusions

Using NI = 30 with 50% ASIR in the chest CT protocol, we obtained average or above-average image quality but a reduced ED.     

Development of a novel algorithm for metal artifact reduction in digital tomosynthesis using projection-based dual-energy material decomposition for arthroplasty: A phantom study

In this study, a novel dual-energy (DE) material decomposition reconstruction algorithm (DEMDRA) was developed using projection data with the aim of reducing metal artifacts during digital tomosynthesis (DT) for implants. Using the three-material decomposition method and decomposition projection data specific for each material, a novel DEMDRA was implemented to reduce metal artifacts via weighted hybrid reconstructed images [maximum likelihood expectation maximization (MLEM) and shift-and-add (SAA)]. Pulsed X-ray exposures with rapid switching between low and high tube potential kVp were used for DE-DT imaging, and the images were compared using conventional filtered back projection (FBP), MLEM, the simultaneous algebraic reconstruction technique total variation (SART-TV), virtual monochromatic processing, and metal artifact reduction (MAR)-processing algorithms. The reductions in metal artifacts were compared using an artifact index (AI), Gumbel distribution of the largest variations, and the artifact spread functions (ASFs) for prosthesis phantom. The novel DEMDRA yielded an adequately effective overall performance in terms of the AI, and the resulting images yielded good results independently of the type of metal used in the prosthetic phantom, as well as good noise artifact removal, particularly at greater distances from metal objects. Furthermore, the DEMDRA represented the minimum in the model of largest variations. Regarding the ASF analysis, the novel DEMDRA yielded superior metal artifact reduction when compared with conventional reconstruction algorithms with and without MAR processing. Finally, the DEMDRA was particularly useful for reducing high-frequency artifacts.     

Technical Note: Quality assessment of virtual monochromatic spectral images on a dual energy CT scanner

PurposeTo assess the quality of images obtained on a dual energy computed tomography (CT) scanner.MethodsImage quality was assessed on a 64 detector-row fast kVp-switching dual energy CT scanner (Revolution GSI, GE Medical Systems). The Catphan phantom and a low contrast resolution phantom were employed. Acquisitions were performed at eight different radiation dose levels that ranged from 9 mGy to 32 mGy. Virtual monochromatic spectral images (VMI) were reconstructed in the 40–140 keV range using all available kernels and iterative reconstruction (IR) at four different blending levels. Modulation Transfer Function (MTF) curves, image noise, image contrast, noise power spectrum and contrast to noise ratio were assessed.ResultsIn-plane spatial resolution at the 10% of the MTF curve was 0.60 mm⁻¹. In-plane spatial resolution was not modified with VMI energy and IR blending level. Image noise was reduced from 16.6 at 9 mGy to 6.7 at 32 mGy, while peak frequency remained within 0.14 ± 0.01 mm⁻¹. Image noise was reduced from 14.3 at IR 10% to 11.5 at IR 50% at a constant peak frequency. The lowest image noise and maximum peak frequency were recorded at 70 keV.ConclusionsOur results have shown how objective image quality is varied when different levels of radiation dose and different settings in IR are applied. These results provide CT operators an in depth understanding of the imaging performance characteristics in dual energy CT.     

Analysis of a novel offset cone-beam computed mammotomography system geometry for accomodating various breast sizes

We evaluate a newly developed dedicated cone-beam transmission computed mammotomography (CmT) system configuration using an optimized quasi-monochromatic cone beam technique for attenuation correction of SPECT in a planned dual-modality emission and transmission system for pendant, uncompressed breasts. In this study, we perform initial CmT acquisitions using various sized breast phantoms to evaluate an offset cone-beam geometry. This offset geometry provides conjugate projections through a full 360 degree gantry rotation, and thus yields a greatly increased effective field of view, allowing a much wider range of breast sizes to be imaged without truncation in reconstructed images. Using a tungsten X-ray tube and digital flat-panel X-ray detector in a compact geometry, we obtained initial CmT scans without shift and with the offset geometry, using geometrical frequency/resolution phantoms and two different sizes of breast phantoms. Acquired data were reconstructed using an ordered subsets transmission iterative algorithm. Projection images indicate that the larger, 20 cm wide, breast requires use of a half-cone-beam offset scan to eliminate truncation artifacts. Reconstructed image results illustrate elimination of truncation artifacts, and that the novel quasi-monochromatic beam yields reduced beam hardening. The offset geometry CmT system can indeed potentially be used for structural imaging and accurate attenuation correction for the functional dedicated breast SPECT system.     

管电压对下肢CT血管成像辐射剂量及图像质量的影响研究

目的:研究管电压对下肢CT血管成像辐射剂量及图像质量的影响。方法:选取2016年1月-2017年10月于我院行下肢CT血管成像的患者102例,将其以随机数字表法均分成观察组与对照组,每组51例。对照组管电压取120kV,观察组管电压取80kV,其他扫描参数相同。分别比较两组辐射剂量情况、客观图像质量以及主观图像质量情况。结果:观察组CT剂量指数(CTDIvol)、计量长度乘积(DLP)、有效辐射剂量(ED)水平均明显低于对照组,差异均有统计学意义(P0.05)。观察组腹主动脉、髂总动脉、股动脉、腘动脉、胫前动脉、胫后动脉、腓动脉CT值以及图像噪声均明显高于对照组,差异均有统计学意义(P0.05),两组动脉对比噪声比(CNR)和信噪比(SNR)比较差异无统计学意义(P0.05)。观察组血管阶段显示评分、血管细节分支显示评分均明显高于对照组,而血管边缘锐利度评分明显低于对照组,差异均有统计学意义(P0.05)。结论:下肢CT血管成像采用80kV管电压扫描可有效降低患者所接受的辐射剂量,且能获取较为满意的图像,值得临床推广应用。     

Non-rigid registration of serial dedicated breast CT,longitudinal dedicated breast CT and PET/CT images using the diffeomorphic demons method

Rationale and objectivesDedicated breast CT and PET/CT scanners provide detailed 3D anatomical and functional imaging data sets and are currently being investigated for applications in breast cancer management such as diagnosis, monitoring response to therapy and radiation therapy planning. Our objective was to evaluate the performance of the diffeomorphic demons (DD) non-rigid image registration method to spatially align 3D serial (pre- and post-contrast) dedicated breast computed tomography (CT), and longitudinally-acquired dedicated 3D breast CT and positron emission tomography (PET)/CT images.MethodsThe algorithmic parameters of the DD method were optimized for the alignment of dedicated breast CT images using training data and fixed. The performance of the method for image alignment was quantitatively evaluated using three separate data sets; (1) serial breast CT pre- and post-contrast images of 20 women, (2) breast CT images of 20 women acquired before and after repositioning the subject on the scanner, and (3) dedicated breast PET/CT images of 7 women undergoing neo-adjuvant chemotherapy acquired pre-treatment and after 1 cycle of therapy.ResultsThe DD registration method outperformed no registration (p < 0.001) and conventional affine registration (p ≤ 0.002) for serial and longitudinal breast CT and PET/CT image alignment. In spite of the large size of the imaging data, the computational cost of the DD method was found to be reasonable (3–5 min).ConclusionsCo-registration of dedicated breast CT and PET/CT images can be performed rapidly and reliably using the DD method. This is the first study evaluating the DD registration method for the alignment of dedicated breast CT and PET/CT images.