CT imaging is one of the primary diagnostic medical imaging modalities. However, there is a long-standing technical limitation associated with conventional CT imaging: anatomical structures with different material compositions may have the same CT number, thereby limiting the ability to differentiate and classify different tissue types and contrast agents. To address this limitation, the currently available strategy is to modify the hardware acquisition systems such that dual energy CT (DECT) data acquisition scheme can be accommodated. In this work, we show that the elemental composition of a material can be directly extracted from a conventional single-kV CT acquisition without invoking DECT acquisition scheme.
Towards providing a “one-stop-shop” solution to anatomical and parenchymal perfusion imaging for pulmonary embolism (PE) evaluation, this work developed a method that uses a deep neural network to estimate effective atomic number (Zeff) information embedded in single-kV pulmonary CT angiography projection data. Based on the estimated Zeff map and the definition of perfusion blood volume (PBV), quantitatively accurate PBV maps can be generated. A multi-center human subject study demonstrates that the proposed single-kV CT and Zeff based PBV method provides a more sensitive and specific biomarker to quantify pulmonary perfusion defects compared with the iodine material image-based perfusion estimation method.
In the effort to contain the COVID-19 pandemic, quick and effective diagnosis is paramount in preventing the spread of the disease. While the reverse transcriptase polymerase chain reaction (RT-PCR) test is the gold standard method to identify COVID-19, the use of x-ray radiography (CXR) has been widely used in the clinical workup for patients suspected of infection as an additional means of diagnosis and treatment response monitoring. CXR is available in almost every medical center across the world, allowing a quick and protected means of identifying potential COVID-19 cases to subject to quarantine procedures. However, the major challenge with the use of CXR in COVID-19 diagnosis is its low sensitivity and specificity in current radiological practice due to the similarities in clinical presentation to other diseases. Machine learning methods, particularly deep learning, have been shown to perform extremely well in a variety of classification tasks, often exceeding human performance. To utilize these techniques, a large data set of over 12,000 CXR images, including over 6,000 confirmed COVID-19 positive cases, was collected to train and validate a deep learning model to differentiate COVID-19 pneumonia from other causes of CXR abnormalities. In this work we show that this deep learning method can differentiate between COVID-19 related pneumonia and non-COVID-19 pneumonia, with high sensitivity and specificity.
Dual energy CT acquisitions can produce two material basis images with high accuracy. From dual energy CT scans with contrast enhancement, one often attempts to generate the corresponding non-enhanced CT images for calcium scoring or for renal stone detection. However, to accurately generate the virtual non-contrast (VNC) images, three-material decomposition is needed. With dual energy CT images, one needs to introduce additional constraints such as the mass or volume conservation condition to approximately perform three-material decomposition. In this work, we present a deep learning strategy to generate VNC image from DECT material basis images. In this strategy, the needed constraint for three-material decomposition is accomplished by learning from the large amount of available training data. In practice, the supervised learning strategy requires matched training data, but this requirement is hard to satisfy in practice since the DECT and non-contrast CT images are acquired in two different CT scans and thus mis-registration often plagues the ordinary supervised learning strategies. In this paper, a new strategy was developed to enable the training of the proposed VNC-Net without using matched training data.
KEYWORDS: Temporal resolution, Data acquisition, Angiography, Human subjects, Arteries, Image restoration, Numerical simulations, Tissues, In vivo imaging, Superposition
Temporal resolution in time-resolved cone-beam CT (TR-CBCT) imaging is often limited by the time needed to acquire a complete dataset for image reconstruction. With the recent developments of performing nearly limited-view artifact-free reconstruction from data in a limited-view angle range and a prior image, temporal resolution of TR-CBCT imaging can be improved from the initial 0.2 frame per second (fps) to 1 fps which indicated a factor of five improvement in temporal resolution. In this paper, a new technique was introduced to further improve temporal resolution from 1.0 fps to about 7.5 fps which indicates a factor of more than 30 times improvement in temporal resolution for slow C-arm CBCT acquistions. This new technique was developed for multi-sweep data acquistion protocol and it is referred to as enhanced SMART-RECON (eSMART-RECON) in this paper. Numerical phantoms with ground truth and in vivo human subject data were used to validate the proposed eSMART-RECON method.
In recent years, it has been shown that it is possible to achieve time-resolved cone-beam CT imaging using a Carm cone-beam CT system equipped with a back-and-forth multi-sweep data acquisition mode. A reconstruction algorithm which has been referred to as SMART-RECON has demonstrated to achieve better than 1.0 frame per second (fps) temporal resolution. Recently, another innovation was introduced into SMART-RECON to achieve 7.5 fps, this innovation opens up a whole new opportunity to extract other physiological information besides the well-known cone-beam CT perfusion. In this work, we study the feasibility to obtain quantitative blood flow information from high temporal resolution time-resolved cone beam CT angiography. This new possibility would enable physicians to more accurately pinpoint occlusion sites and provide the needed image guidance to plan for endovascular therapy in acute ischemic stroke patients as an example. Numerical simulations with ground truth and preliminary clinical case studies have been conducted to demonstrate the feasibility of blood flow quantification from time-resolved CBCT angiography.
Flat-panel detector based cone-beam CT systems have been widely used in image-guided interventions and image-guided radiation therapy. However, several notoriously difficult challenges persist in these cone-beam CT systems: given the relatively large cone angles used in data acquisition, scatter induced artifacts significantly degrade image quality and thus the algorithms to reduce these artifacts have remained an active area of research through out the past decade. To accommodate for the limited detector dynamic range, these systems often use auto-exposure control to homogenize the noise distribution, and as a result, both kV and mA are modulated in some systems making beam hardening correction extremely difficult. Additionally, when data acquisition time is long, inadvertent motion artifacts often exacerbate the situation. In this paper, we develop a deep learning method to empirically correct these most often observed artifacts in flat-panel based CBCT images.
In the past decade, grating-based x-ray multi-contrast imaging has demonstrated potential advantages for breast imaging, including reduced anatomical noise, sharper tumor boundary and improved visibility of microcalcifications. However, most of the studies have been performed on benchtop-based systems. The experimental conditions including the dose, scanning time and system geometry may not meet clinical standards. Therefore, to evaluate true clinical benefits of grating-based multi-contrast breast imaging, in-vivo imaging should be performed, which requires a human-compatible system. The purpose of this paper is to report the development of a human-compatible prototype multi-contrast imaging system. In particular, this work focuses on several key challenges in building the prototype system. Regarding the challenge of patient safety, the mean glandular dose (MGD) and the scatter radiation were evaluated for the prototype system. Regarding the challenge of the limited field-of-view (FOV), the origin of the problem and corresponding technical solutions are presented. Finally, imaging results of several test phantoms are presented and strategies to improve the image quality are discussed.
Time-resolved cone beam CT angiography (CBCTA) imaging in the interventional suite has the potential to identify occluded vessels and the collaterals of symptomatic ischemic stroke patients. However, traditional C-arm gantries offer limited rotational speed and thus the temporal resolution is limited when the conventional filtered backprojection (FBP) reconstruction is used. Recently, a model based iterative image reconstruction algorithm: Synchronized MultiArtifact Reduction with Tomographic reconstruction (SMART-RECON) was proposed to reconstruct multiple CBCT image volumes per short-scan CBCT acquisition to improve temporal resolution. However, it is not clear how much temporal resolution can be improved using the SMART-RECON algorithm or what the corresponding reconstruction accuracy is. In this paper, a novel fractal tree based numerical timeresolved angiography phantom with ground truth temporal information was introduced to quantify temporal resolution using a temporal blurring model analysis along with other two quantification metrics introduced to quantify reconstruction accuracy: the relative root mean square error (rRMSE) and the Kullback-Leibler Divergence (DKL). The quantitative results show that the temporal resolution is 0.8 s for SMART-RECON and 3.6 s for the FBP reconstruction. The reconstruction fidelity with SMART-RECON was substantially improved with the rRMSE improved by at least 70% and the DKL was improved by at least 40%.
By integrating a grating-based interferometer with a clinical full field digital mammography (FFDM) system, a prototype multi-contrast (absorption, phase, and dark field) x-ray breast imaging system was developed in this work. Unlike previous benchtop-based multi-contrast x-ray imaging systems that usually have relatively long source-to-detector distance and vibration isolators or dampers for the interferometer, the FFDM hardware platform is subject to mechanical vibration and the constraint of compact system geometry. Current grating fabrication technology also imposes additional constraints on the design of the grating interferometer. Based on these technical constraints and the x-ray beam properties of the FFDM system, three gratings were designed and integrated with the FFDM system. When installing the gratings, no additional vibration damping device was used in order to test the robustness of multi-contrast imaging system against mechanical vibration. The measured visibility of the diffraction fringes was 23±3%, and two images acquired 60 minutes apart demonstrated good system reproducibility with no visible signal drift. Preliminary results generated from the prototype system demonstrate the multi-contrast imaging capability of the system. The three contrast mechanisms provide mutually complementary information of the phantom object. This prototype system provides a much needed platform for evaluating the true clinical utility of the multi-contrast x-ray imaging method for the diagnosis of breast cancer.
One of the main challenges in low dose x-ray computed tomography (CT) is the presence of highly structured noise. Model based iterative reconstruction methods (MBIR) have shown great potential to overcome this problem; however, they have also introduced an additional challenge: highly nonlinear behavior. One example is the noise variance vs. dose power-law, σ2 α (dose)−β, for which quasilinear FBP-based systems have a β value equal to 1, while MBIR methods have values in the range 0.4-0.6.1 This nonlinearity is attributed mainly to the regularization term of the objective function rather than the data fidelity term. Therefore, if statistical iterative reconstruction was performed in the absence of the regularization term, it could be possible to minimize the nonlinear imaging performance of these methods, while still taking advantage of the benefits from the data fidelity term. Once the image is reconstructed, an additional shift-invariant filter could be implemented to reduce the overall noise magnitude. In this work, the potential benefits of performing (I) unregularized statistical iterative reconstruction with additional image domain denoising are explored and compared against (II) regularized statistical iterative reconstruction using a total variation (TV) regularizer. Rigorous repeated phantom studies were performed at 5 exposure levels to assess the imaging performance in terms of noise and spatial resolution. Results regarding the power-law showed that for FBP reconstruction and for paradigm I, β= 1, while for paradigm II β= 0.6. Additionally, noise was independent of contrast in paradigm I, but was contrast dependent in paradigm II.
In a previous work [Cruz-Bastida et al Med. Phys. 43, 2399 (2016)], the spatial resolution performance of a new High-Resolution (Hi-Res) multi-detector row CT (MDCT) scan mode and the associated High Definition (HD) reconstruction kernels was systematically characterized. The purpose of the present work was to study the noise properties of the Hi-Res scan mode and the joint impact of spatial resolution and noise characteristics on high contrast and high spatial resolution imaging tasks. Using a physical phantom and a diagnostic MDCT system, equipped with both Hi-Res and conventional scan modes, noise power spectrum (NPS) measurements were performed at 8 off-centered positions (0 to 14 cm with an increment of 2 cm) for 8 non-HD kernels and 7 HD kernels. An in vivo rabbit experiment was then performed to demonstrate the potential clinical value of the Hi-Res scan mode. Without the HD kernels, the Hi-Res scan mode preserved the shape of the NPS and slightly increased noise magnitude across all object positions. The combined use of the Hi-Res scan mode and HD kernels led to a greater noise increase and pushed the NPS towards higher frequencies, particularly for those edge-preserving or edge-enhancing HD kernels. Results of the in vivo rabbit study demonstrate important trade-offs between spatial resolution and noise characteristics. Overall, for a given high contrast and high spatial resolution imaging task (bronchi imaging), the benefit of spatial resolution improvement introduced by the Hi-Res scan mode outweighs the potential noise amplification, leading to better overall imaging performance for both centered and off-centered positions.
Digital breast tomosynthesis (DBT) is a three dimensional (3D) breast imaging modality in which projections are acquired over a limited angular span around the compressed breast and reconstructed into image slices parallel to the detector. DBT has been shown to help alleviate the breast tissue overlapping issues of two dimensional (2D) mammography. Since the overlapping tissues may simulate cancer masses or obscure true cancers, this improvement is critically important for improved breast cancer screening and diagnosis. In this work, a model-based image reconstruction method is presented to show that spatial resolution in DBT volumes can be maintained while dose is reduced using the presented method when compared to that of a state-of-the-art commercial reconstruction technique. Spatial resolution was measured in phantom images and subjectively in a clinical dataset. Noise characteristics were explored in a cadaver study. In both the quantitative and subjective results the image sharpness was maintained and overall image quality was maintained at reduced doses when the model-based iterative reconstruction was used to reconstruct the volumes.
When an automatic exposure control is introduced in C-arm cone beam CT data acquisition, the spectral
inconsistencies between acquired projection data are exacerbated. As a result, conventional water/bone correction
schemes are not as effective as in conventional diagnostic x-ray CT acquisitions with a fixed tube potential. In this
paper, a new method was proposed to reconstruct several images with different degrees of spectral consistency
and thus different levels of beam hardening artifacts. The new method relies neither on prior knowledge of the
x-ray beam spectrum nor on prior compositional information of the imaging object. Numerical simulations were
used to validate the algorithm.
In x-ray breast imaging, the anatomical noise background of the breast has a significant impact on the detection of lesions and other features of interest. This anatomical noise is typically characterized by a parameter, β, which describes a power law dependence of anatomical noise on spatial frequency (the shape of the anatomical noise power spectrum). Large values of β have been shown to reduce human detection performance, and in conventional mammography typical values of β are around 3.2. Recently, x-ray differential phase contrast (DPC) and the associated dark field imaging methods have received considerable attention as possible supplements to absorption imaging for breast cancer diagnosis. However, the impact of these additional contrast mechanisms on lesion detection is not yet well understood. In order to better understand the utility of these new methods, we measured the β indices for absorption, DPC, and dark field images in 15 cadaver breast specimens using a benchtop DPC imaging system. We found that the measured β value for absorption was consistent with the literature for mammographic acquisitions (β = 3.61±0.49), but that both DPC and dark field images had much lower values of β (β = 2.54±0.75 for DPC and β = 1.44±0.49 for dark field). In addition, visual inspection showed greatly reduced anatomical background in both DPC and dark field images. These promising results suggest that DPC and dark field imaging may help provide improved lesion detection in breast imaging, particularly for those patients with dense breasts, in whom anatomical noise is a major limiting factor in identifying malignancies.
In a grating interferometer-based x-ray differential phase contrast (DPC) imaging system, an analyzer grating (i.e. a G2 grating) is typically used to help record the important refraction information obtained with the phase stepping technique. Such a method requires the sequential movement of the G2 grating as well as multiple x-ray exposures to perform phase stepping, and thus conventional DPC imaging is very time-consuming. Additionally, it also has some mechanical instability issues due to the movement of the G2 grating. To accelerate the data acquisition speed and achieve single shot x-ray DPC imaging with a collinear type G2 grating, in this study, a new signal extraction method had been investigated. With this alternative approach, a non-zero angle of rotation between the diffraction pattern (generated by the G1 grating) and the collinear G2 grating is used during the entire data acquisition. Due to this deliberate grating misalignment, a visible moiré pattern with a certain period shall be detected. Initial experiments have demonstrated that this new signal extraction method is able to provide us with three different types of signal: absorption, differential phase, and the dark field image signals. Although the spatial resolution for both the differential phase and the dark field images is blurred by several pixel length due to the used interpolation operation, the absorption image maintains the same spatial resolution as in the conventional x-ray imaging. This developed novel signal analysis method enables single shot DPC imaging and can greatly reduce the data acquisition time, thus facilitating the implementation of DPC imaging in the medical field.
Differential phase contrast imaging is a promising new image modality that utilizes the refraction rather than the absorption of x-rays to image an object. A Talbot-Lau interferometer may be used to permit differential phase contrast imaging with a conventional medical x-ray source and detector. However, the current size of the gratings fabricated for these interferometers are often relatively small. As a result, data truncation image artifacts are often observed in a tomographic acquisition and reconstruction. When data are truncated in x-ray absorption imaging, the methods have been introduced to mitigate the truncation artifacts. However, the same strategy to mitigate absorption truncation artifacts may not be appropriate for differential phase contrast or dark field tomographic imaging. In this work, several new methods to mitigate data truncation artifacts in a multi-contrast imaging system have been proposed and evaluated for tomosynthesis data acquisitions. The proposed methods were validated using experimental data acquired for a bovine udder as well as several cadaver breast specimens using a benchtop system at our facility.
A grating-based x-ray multi-contrast imaging system integrates a source grating G0, a diffraction grating G1, and an analyzer grating G2 into a conventional x-ray imaging system to generate images with three contrast mechanisms: absorption contrast, differential phase contrast, and dark field contrast. To facilitate the potential translation of this multi-contrast imaging system into a clinical setting, our group has developed several single-shot data acquisition methods to eliminate the necessity of the time-consuming phase stepping procedure. These methods have enabled us to acquire multi-contrast images with the same data acquisition time currently used for absorption imaging. One of the proposed methods is the use a staggered G2 grating. In this work, we propose to incorporate this staggered G2 grating into a state-of-the-art breast tomosynthesis imaging system to generate tomosynthesis images with three contrast mechanisms. The introduction of this staggered G2 grating will reject scatter and thus improve image contrast at the detector plane, but it will also absorb some x-ray photons reaching detector, thus increasing noise and reducing the contrast to noise ratio (CNR). Therefore, a key technical question is whether the CNR and dose efficiency can be maintained for absorption imaging after the introduction of this staggered G2 grating. In this paper, both the CNR and scatter-to-primary ratio (SPR) of absorption imaging were investigated with Monte Carlo simulations for a variety of staggered G2 grating designs.
Grating-based x-ray differential phase contrast imaging (DPCI) often uses a phase stepping procedure that involves sequential grating motion and multiple x-ray exposures to obtain x-ray phase information. Such a data acquisition process breaks the continuous data acquisition into several step-and-shoot data acquisition sequences. Between two neighboring x-ray pulses, the acquisition will have to be stopped for the grating to translate into the next phase stepping position. This setup also requires that the grating not be fixed. If the gratings are to be mounted onto a fast-rotating gantry (such as those used in x-ray CT), this translation of the grating would add another potential source of mechanical instability. To accelerate the data acquisition speed and improve the mechanical stability of of DPCI data acquisitions, a new grating design was developed. In this method, one of the gratings used in DPCI was divided into four-row groups, within each group, grating structures have a designed offset with respect to their neighboring rows. This design allows the acquired data from any adjacent four detector rows to be combined in order to retrieve the needed x-ray differential phase information from a single x-ray exposure. Both numerical simulations and initial phantom experiments have demonstrated that the new interferometer design can enable DPCI image acquisitions without this well-known overhead in data acquisition time.
Dual-energy CT has the potential to overcome many of the limitations of routine single-energy CT scanning, such a,.., the potential to provide quantitative imaging via electron density, effective atomic munber, and virtual monochromatic imaging and the potential to completely eliminate beam-hardening artifacts via projection space decomposition. While the potential clinical benefit is strong, a possible barrier to more frequent clinical use of dual-energy CT scanning is radiation dose for high quality images. While image quality in dual-energy CT depends on a munber of factors, including dose partitioning, the choice of kV pair, and the amount of pre filtration used, a munber of strategies have been employed to improve image quality in dual-energy CT. Four main methods are: (1) increa,..,e the radiation dose, (2) increase the slice thickness, (3) perform voxel averaging, or (4) use noise reduction algorithms. While these methods offer options for improving image quality, ideally, it is desirable not to have to increase radiation dose or sacrifice spatial resolution (in the x-y plane or in the z-direction). Therefore, it is the purpose of this work to investigate the application of Prior Image Constrained Compressed Sensing (PICCS) in dual-energy CT to reduce radiation dose without sacrificing image quality. In particular, we investigate the use of PICCS in dual-energy CT to generate material density images at half the radiation dose of a commonly used gemstone spectral imaging (GSI) protocol. lVIaterial density images are generated using half the radiation dose, and virtual monochromatic images are generated as a linear combination of half-dose material density images. In this abstract, qualitative and quantitative evaluation are provided to assess the performance of PICCS relative to FBP images at the full dose level and at the half dose level.
Although the relative ease of implementation and compact nature of grating-based differential phase contrast
CT (DPC-CT) has sparked tremendous enthusiasm for potential medical applications, the pros and cons of this
imaging method remains to be addressed before an actual clinical system can be constructed. To address these
unknowns, either numerical simulations or direct hardware implementations can be used. However, both approaches
have their limitations. It is highly desirable to develop a research method to enable imaging performance
prediction for a future DPC-CT system from the performance of an available absorption CT (ACT) system. In
this paper, a theoretical framework was developed to accurately predict the noise properties and detection performance
of DPC-CT from that of conventional ACT. The framework was derived based on a fundamental noise
relationship between DPC-CT and ACT and was experimentally validated. An example has been given in the
paper on how the framework can be utilized to predict model observer detectability index of a DPC breast CT
constructed based on an existing absorption breast CT. This framework is expected to become a valuable tool in
addressing the following questions: (i) With a fixed radiation dose in a particular clinical application, how well
can a specific detection/discrimination imaging task can be performed provided that an existing ACT scanner is
modified into a DPC-CT by inserting a grating interferometer, which is characterized by a few design parameters
(e.g., pitches and duty cycles of the gratings, relative distance between the gratings, etc.) into the ACT system?
(ii) If a DPC-CT system can outperform an ACT for certain detection/discrimination tasks under the constraint
of identical radiation dose to the image object, how would one optimize design parameters of the gratings in
order to maximize its potential clinical benefits?
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