Dataset Information

Single-scan patient-specific scatter correction in computed tomography using peripheral detection of scatter and compressed sensing scatter retrieval.

ABSTRACT: X-ray scatter results in a significant degradation of image quality in computed tomography (CT), representing a major limitation in cone-beam CT (CBCT) and large field-of-view diagnostic scanners. In this work, a novel scatter estimation and correction technique is proposed that utilizes peripheral detection of scatter during the patient scan to simultaneously acquire image and patient-specific scatter information in a single scan, and in conjunction with a proposed compressed sensing scatter recovery technique to reconstruct and correct for the patient-specific scatter in the projection space.The method consists of the detection of patient scatter at the edges of the field of view (FOV) followed by measurement based compressed sensing recovery of the scatter through-out the projection space. In the prototype implementation, the kV x-ray source of the Varian TrueBeam OBI system was blocked at the edges of the projection FOV, and the image detector in the corresponding blocked region was used for scatter detection. The design enables image data acquisition of the projection data on the unblocked central region of and scatter data at the blocked boundary regions. For the initial scatter estimation on the central FOV, a prior consisting of a hybrid scatter model that combines the scatter interpolation method and scatter convolution model is estimated using the acquired scatter distribution on boundary region. With the hybrid scatter estimation model, compressed sensing optimization is performed to generate the scatter map by penalizing the L1 norm of the discrete cosine transform of scatter signal. The estimated scatter is subtracted from the projection data by soft-tuning, and the scatter-corrected CBCT volume is obtained by the conventional Feldkamp-Davis-Kress algorithm. Experimental studies using image quality and anthropomorphic phantoms on a Varian TrueBeam system were carried out to evaluate the performance of the proposed scheme.The scatter shading artifacts were markedly suppressed in the reconstructed images using the proposed method. On the Catphan©504 phantom, the proposed method reduced the error of CT number to 13 Hounsfield units, 10% of that without scatter correction, and increased the image contrast by a factor of 2 in high-contrast regions. On the anthropomorphic phantom, the spatial nonuniformity decreased from 10.8% to 6.8% after correction.A novel scatter correction method, enabling unobstructed acquisition of the high frequency image data and concurrent detection of the patient-specific low frequency scatter data at the edges of the FOV, is proposed and validated in this work. Relative to blocker based techniques, rather than obstructing the central portion of the FOV which degrades and limits the image reconstruction, compressed sensing is used to solve for the scatter from detection of scatter at the periphery of the FOV, enabling for the highest quality reconstruction in the central region and robust patient-specific scatter correction.

SUBMITTER: Meng B

PROVIDER: S-EPMC3543379 | biostudies-other | 2013 Jan

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:To improve image quality and accuracy in dedicated breast computed tomography (BCT) by removing the x-ray scatter signal included in the BCT projections.The previously characterized magnitude and distribution of x-ray scatter in BCT results in both cupping artifacts and reduction of contrast and accuracy in the reconstructions. In this study, an image processing method is proposed that estimates and subtracts the low-frequency x-ray scatter signal included in each BCT projection postacquisition and prereconstruction. The estimation of this signal is performed using simple additional hardware, one additional BCT projection acquisition with negligible radiation dose, and simple image processing software algorithms. The high frequency quantum noise due to the scatter signal is reduced using a noise filter postreconstruction. The dosimetric consequences and validity of the assumptions of this algorithm were determined using Monte Carlo simulations. The feasibility of this method was determined by imaging a breast phantom on a BCT clinical prototype and comparing the corrected reconstructions to the unprocessed reconstructions and to reconstructions obtained from fan-beam acquisitions as a reference standard. One-dimensional profiles of the reconstructions and objective image quality metrics were used to determine the impact of the algorithm.The proposed additional acquisition results in negligible additional radiation dose to the imaged breast (∼0.4% of the standard BCT acquisition). The processed phantom reconstruction showed substantially reduced cupping artifacts, increased contrast between adipose and glandular tissue equivalents, higher voxel value accuracy, and no discernible blurring of high frequency features.The proposed scatter correction method for dedicated breast CT is feasible and can result in highly improved image quality. Further optimization and testing, especially with patient images, is necessary to characterize its impact on clinical performance.

Project description:PURPOSE:Scatter is a major factor degrading the image quality of cone beam computed tomography (CBCT). Conventional scatter correction strategies require handcrafted analytical models with ad hoc assumptions, which often leads to less accurate scatter removal. This study aims to develop an effective scatter correction method using a residual convolutional neural network (CNN). METHODS:A U-net based 25-layer CNN was constructed for CBCT scatter correction. The establishment of the model consists of three steps: model training, validation, and testing. For model training, a total of 1800 pairs of x-ray projection and the corresponding scatter-only distribution in nonanthropomorphic phantoms taken in full-fan scan were generated using Monte Carlo simulation of a CBCT scanner installed with a proton therapy system. An end-to-end CNN training was implemented with two major loss functions for 100 epochs with a mini-batch size of 10. Image rotations and flips were randomly applied to augment the training datasets during training. For validation, 200 projections of a digital head phantom were collected. The proposed CNN-based method was compared to a conventional projection-domain scatter correction method named fast adaptive scatter kernel superposition (fASKS) method using 360 projections of an anthropomorphic head phantom. Two different loss functions were applied for the same CNN to evaluate the impact of loss functions on the final results. Furthermore, the CNN model trained with full-fan projections was fine-tuned for scatter correction in half-fan scan by using transfer learning with additional 360 half-fan projection pairs of nonanthropomorphic phantoms. The tuned-CNN model for half-fan scan was compared with the fASKS method as well as the CNN-based method without the fine-tuning using additional lung phantom projections. RESULTS:The CNN-based method provides projections with significantly reduced scatter and CBCT images with more accurate Hounsfield Units (HUs) than that of the fASKS-based method. Root mean squared error of the CNN-corrected projections was improved to 0.0862 compared to 0.278 for uncorrected projections or 0.117 for the fASKS-corrected projections. The CNN-corrected reconstruction provided better HU quantification, especially in regions near the air or bone interfaces. All four image quality measures, which include mean absolute error (MAE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM), indicated that the CNN-corrected images were significantly better than that of the fASKS-corrected images. Moreover, the proposed transfer learning technique made it possible for the CNN model trained with full-fan projections to be applicable to remove scatters in half-fan projections after fine-tuning with only a small number of additional half-fan training datasets. SSIM value of the tuned-CNN-corrected images was 0.9993 compared to 0.9984 for the non-tuned-CNN-corrected images or 0.9990 for the fASKS-corrected images. Finally, the CNN-based method is computationally efficient - the correction time for the 360 projections only took less than 5 s in the reported experiments on a PC (4.20 GHz Intel Core-i7 CPU) with a single NVIDIA GTX 1070 GPU. CONCLUSIONS:The proposed deep learning-based method provides an effective tool for CBCT scatter correction and holds significant value for quantitative imaging and image-guided radiation therapy.

Project description:X-ray scatter is a significant barrier to image quality improvements in cone-beam computed tomography (CBCT). A moving blocker-based strategy was previously proposed to simultaneously estimate scatter and reconstruct the complete volume within the field of view (FOV) from a single CBCT scan. A blocker consisting of lead stripes is inserted between the X-ray source and the imaging object, and moves back and forth along the rotation axis during gantry rotation. While promising results were obtained in our previous studies, the geometric design and moving speed of the blocker were set empirically. The goal of this work is to optimize the geometry and speed of the moving block system.Performance of the blocker was examined through Monte Carlo (MC) simulation and experimental studies with various geometry designs and moving speeds. All hypothetical designs employed an anthropomorphic pelvic phantom. The scatter estimation accuracy was quantified by using lead stripes ranging from 5 to 100 pixels on the detector plane. An iterative reconstruction based on total variation minimization was used to reconstruct CBCT images from unblocked projection data after scatter correction. The reconstructed image was evaluated under various combinations of lead strip width and interspace (ranging from 10 to 60 pixels) and different moving speed (ranging from 1 to 30 pixels per projection).MC simulation showed that the scatter estimation error varied from 0.8% to 5.8%. Phantom experiment showed that CT number error in the reconstructed CBCT images varied from 13 to 35. Highest reconstruction accuracy was achieved when the strip width was 20 pixels and interspace was 60 pixels and the moving speed was 15 pixels per projection.Scatter estimation can be achieved in a large range of lead strip width and interspace combinations. The moving speed does not have a very strong effect on reconstruction result if it is above 5 pixels per projection. Geometry design of the blocker affected image reconstruction accuracy more. The optimal geometry of the blocker has a strip width of 20 pixels and an interspace three times the strip width, which means 25% detector is covered by the blocker, while the optimal moving speed is 15 pixels per projection.

Project description:ObjectivesComputed tomography angiography (CTA) is the imaging modality most frequently used to diagnosis type A aortic dissection for chest pain with a high degree of sensitivity and specificity. False negative and positive errors in diagnosis are infrequent. Despite initial negative imaging studies for dissection, surgeons must consider early operation in patients with recent onset chest pain in the presence of an ascending aortic aneurysm.MethodsWe report 4 cases (2004-2021) of patients with a mean age of 51.25 years (male:female = 3:1) who presented with chest pain. Two had a history of syncope. On computed tomography angiography, all had an ascending aortic aneurysm and no dissection flap. Three had thickening of the ascending aorta and 1 had a chronic type B dissection. On echocardiography, aortic regurgitation was moderate-severe in 2 patients, 2 had a pericardial effusion, and none had a definitive dissection flap. All were operated on because of the surgeon's concern for the association of chest pain and an ascending aortic aneurysm. The Institutional Review Board at George Washington University Committee on Human Research has determined that the research is exempt from review under Department of Health and Human Services regulatory category 4 (#FWA00005945). The project as described in the application may proceed without further oversight by the Office of Human Resources on September 9, 2022.ResultsThe diagnosis of type A aortic dissection was made at operation (3 on median sternotomy and 1 after aortotomy). All patients did well. One patient who had peripheral cannulation (no malperfusion) and severe coagulopathy developed a compartment syndrome and postoperatively required a below-knee amputation.ConclusionsSurgeons must maintain a high index of suspicion for aortic dissection when patients present with chest pain and are found to have an ascending aortic aneurysm even in the absence of initial, classic features of dissection on computed tomography angiography. Improvements in imaging techniques and analysis are required.

Dataset Information

Single-scan patient-specific scatter correction in computed tomography using peripheral detection of scatter and compressed sensing scatter retrieval.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets