PurposeOur aim was to develop a high‐quality, mobile cone‐beam computed tomography (CBCT) scanner for point‐of‐care detection and monitoring of low‐contrast, soft‐tissue abnormalities in the head/brain, such as acute intracranial hemorrhage (ICH). This work presents an integrated framework of hardware and algorithmic advances for improving soft‐tissue contrast resolution and evaluation of its technical performance with human subjects.MethodsFour configurations of a CBCT scanner prototype were designed and implemented to investigate key aspects of hardware (including system geometry, antiscatter grid, bowtie filter) and technique protocols. An integrated software pipeline (c.f., a serial cascade of algorithms) was developed for artifact correction (image lag, glare, beam hardening and x‐ray scatter), motion compensation, and three‐dimensional image (3D) reconstruction [penalized weighted least squares (PWLS), with a hardware‐specific statistical noise model]. The PWLS method was extended in this work to accommodate multiple, independently moving regions with different resolution (to address both motion compensation and image truncation). Imaging performance was evaluated quantitatively and qualitatively with 41 human subjects in the neurosciences critical care unit (NCCU) at our institution.ResultsThe progression of four scanner configurations exhibited systematic improvement in the quality of raw data by variations in system geometry (source‐detector distance), antiscatter grid, and bowtie filter. Quantitative assessment of CBCT images in 41 subjects demonstrated: ~70% reduction in image nonuniformity with artifact correction methods (lag, glare, beam hardening, and scatter); ~40% reduction in motion‐induced streak artifacts via the multi‐motion compensation method; and ~15% improvement in soft‐tissue contrast‐to‐noise ratio (CNR) for PWLS compared to filtered backprojection (FBP) at matched resolution. Each of these components was important to improve contrast resolution for point‐of‐care cranial imaging.ConclusionsThis work presents the first application of a high‐quality, point‐of‐care CBCT system for imaging of the head/ brain in a neurological critical care setting. Hardware configuration iterations and an integrated software pipeline for artifacts correction and PWLS reconstruction mitigated artifacts and noise to achieve image quality that could be valuable for point‐of‐care detection and monitoring of a variety of intracranial abnormalities, including ICH and hydrocephalus.

Purpose: Improving soft-tissue contrast resolution beyond the capability of current cone-beam CT (CBCT) systems is essential to a growing range of image guidance and diagnostic imaging scenarios. We present a framework for CBCT model-based image reconstruction (MBIR) combining artifact corrections with multi-resolution reconstruction and multi-region motion compensation and apply the method for the first time in a clinical study of CBCT for high-quality imaging of head injury. Methods: A CBCT prototype was developed for mobile point-of-care imaging in the neuro-critical care unit (NCCU). Projection data were processed via poly-energetic gain correction and an artifacts correction pipeline treating scatter, beam hardening, and motion compensation. The scatter correction was modified to use a penalized weighted least-squares (PWLS) image in the Monte-Carlo (MC) object model for better uniformity in truncated data. The PWLS method included: (1) multi-resolution reconstruction to mitigate lateral truncation from the head-holder; (2) multi-motion compensation allowing separate motion of the head and head-holder; and (3) modified statistical weights to account for electronics noise and fluence modulation by the bowtie filter. Imaging performance was evaluated in simulation and in the first clinical study (N = 54 patients) conducted with the system. Results: Using a PWLS object model in the final iteration of the MC scatter estimate improved image uniformity by 40.4% for truncated datasets. The multi-resolution, multi-motion PWLS method greatly reduced streak artifacts and non-uniformity both in simulation (RMSE reduced by 65.5%) and in the clinical study (visual image quality assessed by a neuroradiologist). Up to 15% reduction in variance was achieved using statistical weights modified according to a model for electronic noise from the detector. Each component was important for improved contrast resolution in the patient data. Conclusion: An integrated pipeline for artifacts correction and PWLS reconstruction mitigated artifacts and noise to a level supporting visualization of low-contrast brain lesions and warranting future studies of diagnostic performance in the NCCU.

Purpose: Cone-beam CT (CBCT) systems with a flat-panel detector (FPD) have advanced in a variety of specialty diagnostic imaging scenarios, with fluence modulation and multiple-gain detectors playing important roles in extending dynamic range and improving image quality. We present a penalized weighted least-squares (PWLS) reconstruction approach with a noise model that includes the effects of fluence modulation and electronic readout noise, and we show preliminary results that tests the concept with a CBCT head scanner prototype. Methods: Statistical weights in PWLS were modified using a realistic noise model for the FPD that considers factors such as system blur and spatially varying electronic noise in multiple-gain readout detectors (PWLSe). A spatially varying gain term was then introduced in the calculation of statistical weights to account for the change in quantum noise due to fluence modulation (e.g. bowtie filter) (PWLS∗). The methods were tested in phantom experiments involving an elliptical phantom specially designed to stress dual-gain readout, and a water phantom and an anthropomorphic head phantom to quantify improvements in noise-resolution characteristics for the new PWLS methods (PWLS𝑒 and PWLS∗, and combined PWLS∗e). The proposed methods were further tested using a high-quality, low-dose CBCT head scanner prototype in a clinical study involving patients with head injury. Results: Preliminary results show that the PWLSe method demonstrated superior noise-resolution tradeoffs compared to conventional PWLS, with variance reduced by ~15-25% at matched resolution of 0.65 mm edge-spread-function (ESF) width. Clinical studies confirmed these findings, with variance reduced by ~15% in peripheral regions of the head without loss in spatial resolution, improving visual image quality in detection of peridural hemorrhage. A bowtie filter and polyenergetic gain correction improved image uniformity, and early results demonstrated that the proposed PWLS∗ method showed a ~40% reduction in variance compared to conventional PWLS when used with a bowtie filter. Conclusion: A more accurate noise model incorporated in PWLS statistical weights to account for fluence modulation and electronic readout noise reduces image noise and improves soft-tissue imaging performance in CBCT for clinical applications requiring a high degree of contrast resolution.

Timely evaluation of neurovasculature via CT angiography (CTA) is critical to the detection of pathology such as ischemic stroke. Cone-beam CTA (CBCT-A) systems provide potential advantages in the timely use at the point-of-care, although challenges of a relatively slow gantry rotation speed introduce tradeoffs among image quality, data consistency and data sparsity. This work describes and evaluates a new reconstruction-of-difference (RoD) approach that is robust to such challenges. A fast digital simulation framework was developed to test the performance of the RoD over standard reference reconstruction methods such as filtered back-projection (FBP) and penalized likelihood (PL) over a broad range of imaging conditions, grouped into three scenarios to test the trade-off between data consistency, data sparsity and peak contrast. Two experiments were also conducted using a CBCT prototype and an anthropomorphic neurovascular phantom to test the simulation findings in real data. Performance was evaluated primarily in terms of normalized root mean square error (NRMSE) in comparison to truth, with reconstruction parameters chosen to optimize performance in each case to ensure fair comparison. The RoD approach reduced NRMSE in reconstructed images by up to 50%-53% compared to FBP and up to 29%-31% compared to PL for each scenario. Scan protocols well suited to the RoD approach were identified that balance tradeoffs among data consistency, sparsity and peak contrast-for example, a CBCT-A scan with 128 projections acquired in 8.5 s over a 180°  +  fan angle half-scan for a time attenuation curve with ~8.5 s time-to-peak and 600 HU peak contrast. With imaging conditions such as the simulation scenarios of fixed data sparsity (i.e. varying levels of data consistency and peak contrast), the experiments confirmed the reduction of NRMSE by 34% and 17% compared to FBP and PL, respectively. The RoD approach demonstrated superior performance in 3D angiography compared to FBP and PL in all simulation and physical experiments, suggesting the possibility of CBCT-A on low-cost, mobile imaging platforms suitable to the point-of-care. The algorithm demonstrated accurate reconstruction with a high degree of robustness against data sparsity and inconsistency.

Cone-beam CT (CBCT) systems commonly incorporate a flat-panel detector (FPD) with multiple-gain readout capability to reduce electronic noise and extend dynamic range. In this work, we report a penalized weighted least-squares (PWLS) method for CBCT image reconstruction with a system model that includes the electronic noise characteristics of FPDs, including systems with dynamic-gain or dual-gain (DG) readout in which the electronic noise is spatially varying. Statistical weights in PWLS were modified to account for the contribution of the electronic noise (algorithm denoted ), and the method was combined with a certainty-based approach that improves the homogeneity of spatial resolution (algorithm denoted ). The methods were tested in phantom studies designed to stress DG readout characteristics and translated to a clinical study for CBCT of patients with head traumas. The method demonstrated superior noise-resolution tradeoffs compared to filtered back-projection (FBP) and conventional PWLS. For example, with spatial resolution (edge-spread function width) matched at 0.65 mm, reduced variance by 28%–39% and 15%–25% compared to FBP and PWLS, respectively. The method achieved more homogeneous spatial resolution than while maintaining similar variance reduction. These findings were confirmed in clinical studies, which showed ~20% variance reduction in peripheral regions of the brain, potentially improving visual image quality in detection of epidural and/or subdural intracranial hemorrhage. The results are consistent with the general notion that incorporating a more accurate system model improves performance in optimization-based statistical CBCT reconstruction—in this case, a more accurate model for (spatially varying) electronic noise to improve detectability of low-contrast lesions.

Purpose: Timely detection of neurovascular pathology such as ischemic stroke is essential to effective treatment, and systems for cone-beam CT (CBCT) could provide CT angiography (CTA) assessment in a timely manner close to the point of care. CBCT systems suffer from slow rotation speed and readout speed, which leads to inconsistent or sparse dataset. This work describes a new reconstruction method using a reconstruction of difference (RoD) approach that is robust against such factors. Methods: Important aspects of CBCT angiography were investigated, weighting tradeoffs among the magnitude of iodine enhancement (peak contrast), the degree of data consistency, and the degree of data sparsity. Simulation studies were performed across a range of CBCT half-scan acquisition speed ranging similar to 3-17 s. Experiments were conducted using a CBCT prototype and an anthropomorphic neurovascular phantom incorporating a vessel with contrast injection with a time-attenuation (TAC) injection giving low data consistency but high peak contrast. Images were reconstructed using filtered back-projection (FBP), penalized likelihood (PL), and the RoD algorithm. Data were evaluated in terms of root mean square error (RMSE) in image enhancement as well as overall image noise and artifact. Results: Feasibility was demonstrated for 3D angiographic assessment in CBCT images acquired across a range of data consistency and sparsity. Compared to FBP, the RoD method reduced the RMSE in reconstructed images by 50.0% in simulation studies (fixed peak contrast; variable data consistency and sparsity). The improvement in RMSE compared to PL reconstruction was 28.8%. The phantom experiments investigated conditions of low data consistency, RoD provided a 15.6% reduction in RMSE compared to FBP and a 16.3% reduction compared to PL, showing the feasibility of RoD method for slow-rotating CBCT-A system. Conclusions: Simulations and phantom experiments show the feasibility and improved performance of the RoD approach compared to FBP and PL reconstruction, enabling 3D neuro-angiography on a slowly rotating CBCT system (e.g., 17.1s for a half-scan). The algorithm is relatively robust against data sparsity and is sensitive in detecting low levels of contrast enhancement from the baseline (mask) scan. Tradeoffs among peak contrast, data consistency, and data sparsity are demonstrated clearly in each experiment and help to guide the development of optimal contrast injection protocols for future preclinical and clinical studies.

Purpose: This work reports the design, development, and first technical assessment of a cone‐beam CT (CBCT) scanner developed specifically for imaging of acute intracranial hemorrhage (ICH) at the point of care, with target applications in diagnosis and monitoring of traumatic brain injury, stroke, and postsurgical hemorrhage. Methods: System design employed a task‐based image quality model to quantify the influence of factors such as additive noise and high‐gain (HG) detector readout on ICH detectability. Three bowtie filters with varying bare‐beam attenuation strength and curvature were designed to enable HG readout without detector saturation, and a polyenergetic gain correction was developed to minimize artifacts from bowtie flood‐field calibration. Image reconstruction used an iterative penalized weighted least squares (PWLS) method with artifact correction including Monte Carlo scatter estimation, Joseph‐Spital beam hardening correction, and spatiotemporal deconvolution of detector glare and lag. Radiation dose was characterized for half‐scan and full‐scan protocols at various kV, and imaging performance was assessed in a head phantom presenting simulated ICH with diameter ranging 2–12 mm. Results: The image quality model guided system design and was validated by measurements on a CBCT imaging bench. Compared to low‐gain readout without a bowtie filter, the combination of HG readout and a modest bowtie improved the contrast‐to‐noise ratio (CNR per unit square‐root dose) by 20% in the center of the image but degraded noise performance near the periphery (20% reduction in CNR). Low‐frequency bowtie artifacts (∼100 HU magnitude) were corrected by the polyenergetic gain correction. Image reconstructions on the prototype scanner demonstrate clear visibility of the smallest ICH insert (2 mm diameter) in both HG readout (with a bowtie) and dual‐gain readout (without bowtie). Conclusion: Technical assessment of the prototype scanner suggests the capability for reliable visualization of small (2 mm), low‐contrast (50 HU) ICH at <20 mGy dose and motivates translation to clinical studies, now underway. Xiaohui Wang and David Foos (co‐authors) are employees of Carestream Health. Research funding support from Carestream Health.

To assess visualization tasks using cone-beam CT (CBCT) compared to multi-detector CT (MDCT) for musculoskeletal extremity imaging.

PURPOSE To assess the diagnostic performance of a prototype cone-beam CT (CBCT) scanner developed for musculoskeletal extremity imaging. Studies involved controlled observer studies conducted subsequent to rigorous technical assessment as well as patient images from the first clinical trial in imaging the hand and knee. METHODS Performance assessment included: 1.) rigorous technical assessment; 2.) controlled observer studies using CBCT images of cadaveric specimens; and 3.) first clinical images. Technical assessment included measurement of spatial resolution (MTF), constrast, and noise (SDNR) versus kVp and dose using standard CT phantoms. Diagnostic performance in comparison to multi- detector CT (MDCT) was assessed in controlled observer studies involving 12 cadaveric hands and knees scanned with and without abnormality (fracture). Observer studies involved five radiologists rating pertinent diagnostics tasks in 9-point preference and 10-point diagnostic satisfaction scales. Finally, the first clinical images from an ongoing pilot study were assessed in terms of diagnostic utility in disease assessment and overall workflow in patient setup. RESULTS Quantitative assessment demonstrated sub-mm spatial resolution (MTF exceeding 10% out to 15-20 cm-1) and SDNR sufficient for relevant soft-tissue visualization tasks at dose <10 mGy. Observer studies confirmed optimal acquisition techniques and demonstrated superior utility of combined soft-tissue visualization and isotropic spatial resolution in diagnostic tasks. Images from the patient trial demonstrate exquisite contrast and detail and the ability to detect tissue impingement in weight-bearing exams. CONCLUSIONS The prototype CBCT scanner provides isotropic spatial resolution superior to standard-protocol MDCT with soft-tissue visibility sufficient for a broad range of diagnostic tasks in musculoskeletal radiology. Dosimetry and workflow were advantageous in comparison to whole-body MDCT. Multi-mode and weight-bearing capabilities add valuable functionality. An ongoing clinical study further assesses diagnostic utility and defines the role of such technology in the diagnostic arsenal. - Research Grant, Carestream Health - Research Grant, National Institutes of Health 2R01-CA-112163.

An apparatus for cone-beam CT scan of a lower limb of a patient, the apparatus comprising: a digital detector (24) of radiation; a first device (34) attached to a turntable (64) C-shaped, configured to rotate about an axis (A) of rotation to move the detector (24) along at least a portion of a circular path (28) the detector centered on said axis (a) of rotation, extending the at least one portion of the path (28) of the detector so that the detector (24) move both at least partially around a first lower end of patient between the first lower end and a second lower end adjacent the patient, the at least one portion of the path (28) of the detector a radius R1 that is long enough to allow the first lower limb of the patient is positioned approximately in the path center (28) of the detector; a source (22) of radiation; a second device (32) connected to the turntable (64) to move the source (22) along at least a portion of a circular path concentric (26) of the source having a radius R2 greater than the radius R1 being sufficiently long radius R2 to allow adequate exposure to radiation of the first lower end to the detector (24) captures a volumetric three-dimensional image; and a circumferential space (38) extending both on the route (26) of the source, as in the path (28) of the detector to allow the second lower end is positioned in the circumferential space (38) during capture of image, wherein the second device (32) comprises an arm (66) support connected to the turntable and extending outwardly radially from the turntable and from the path detector to connect the source to the turntable and provide greater radius R2.

Purpose: Although bedside imaging is among the most common radiographic exams (e.g., line and catheter placement in the ICU), poor image quality often challenges even conspicuous tasks. We hypothesize that dual-energy (DE) imaging using a novel wireless detector (with form factor equivalent to a film cassette) improves the conspicuity of interventional devices through material decomposition. This work optimizes the system configuration, acquisition techniques, and decomposition techniques for this novel bedside imaging technology. Method and Materials: A mobile x-ray system was integrated with a wireless DR detector (DRX-1) to permit DE image acquisition. Preliminary experimentation involved a benchtop and phantoms simulating bedside imaging. The contrast-to-noise ratio (CNR) was evaluated in DE images for common ICU imaging tasks. Measurements were performed as a function of antiscatter grid ratio (from gridless to 12:1), kVp pair (kVpLow = 40–90 kVp and kVpHigh = 90–140 kVp), and added filtration. Image quality was further improved by implementing linear and nonlinear noise-reduction algorithms. Expert radiologists assessed DR and DE imaging task performance in anthropomorphic phantoms implanted with interventional tools. Results: DE imaging significantly boosted conspicuity of interventional devices in bedside imaging tasks. Antiscatter grids increased CNR by ∼50% without dose increase. Optimal kVp pair was [50/90] kVp, with lower techniques distinct from previous studies attributed to high x-ray scatter in bedside configurations. Optimal filters were identified, with ∼0.2 mm Ag or Sn found optimal for the fixed-filter scenario. Noise-reduction algorithms preserved CNR at reduced imaging dose and indicate distinct processing for soft-tissue and metal decomposition. Qualitative reader studies show a dramatic boost in conspicuity for DE imaging compared to DR. Conclusion: Advances in portable DR enable superior bedside imaging through advanced DE imaging functionality. Optimization of DE imaging and decomposition techniques render interventional tools conspicuous compared to DR without increasing patient dose. Research funded by Carestream Health

Digital radiography systems are capable of capturing a wide range of x‐ray exposures in a single image. The wide latitude of these capture devices dictates that images are digitally processed before display. Attempting to directly print or display the full dynamic range of the captured image will result in an image with low contrast that is unacceptable for diagnostic interpretation. Image processing algorithms are used to identify the range of exposures (represented as code values) that correspond to the diagnostically relevant regions, followed by a rendering process to transform signal values in the diagnostically relevant regions to display values. The image processing algorithms need to perform these steps completely automatically, or with a minimum amount of human intervention, to facilitate technologists' workflow. Image processing performance is ultimately gauged by the ability to automatically and consistently generate images of diagnostic quality. Recognition of the diagnostically relevant exposures consists of segmentation of the collimation, direct exposure, and anatomy regions. This is a challenging computer‐recognition problem because several factors cause the characteristics of the image, and corresponding code value histogram, to vary. These factors include the body part and projection, exposure technique, collimation, use of anti‐scatter devices, use of contrast media, etc. Robust segmentation will also automatically recognize other features such as prosthetic implants, pacemakers, and left‐right markers. Once the image is properly segmented, the signal values for the diagnostically relevant regions are transformed into values for display. Rendering the region of interest for display generally consists of establishing the image grayscale rendition, and signal equalization and contrast enhancement, which is based on spatial frequency decomposition and reconstruction. Because of the tremendous flexibility to render images to different aims, understanding the rendering preferences of radiologists is a critical component of digital radiography image processing. Establishing the aim appearance for different exam types, then adapting the image processing algorithms and database parameters to automatically and consistently deliver the preferred rendering, will affect both the technologists' and the radiologists' satisfaction with the capture device. This course will review fundamentals of image processing for digital radiography. Specific course content will include a discussion of radiographic image segmentation, correlation of image content with different regions of the histogram, and a review of image‐rendering functions, including the tone scale, spatial frequency methods to enhance contrast, and signal equalization. The course will also include a review of the role of image processing in the end‐to‐end image chain (capture through interpretation), with considerations for image quality performance of different capture and display devices, the effect of the human visual system and ambient viewing conditions, and grayscale calibration.

Image processing is a critical part of obtaining high-quality digital radiographs. Fortunately, the user of these systems does not need to understand image processing in detail, because the manufacturers provide good starting values. Because radiologists may have different preferences in image appearance, it is helpful to know that many aspects of image appearance can be changed by image processing, and a new preferred setting can be loaded into the computer and saved so that it can become the new standard processing method.Image processing allows one to change the overall optical density of an image and to change its contrast. Spatial frequency processing allows an image to be sharpened, improving its appearance. It also allows noise to be blurred so that it is less visible. Care is necessary to avoid the introduction of artifacts or the hiding of mediastinal tubes.

Image processing is used to transform raw digital radiographic image data, captured using CR (computed radiography) and DR (flat panel direct digital radiography) systems into a display-ready form. Ideally, an image-processing algorithm automatically renders an image for display, based on aims derived from observer performance studies. Establishing the rendering aim for different exam types, however, can be complex because the effects on image appearance introduced by the various steps in the rendering process are interdependent. This paper describes a new rendering algorithm that provides orthogonal control, to the first order, of five fundamental attributes of perceived image quality. These attributes are brightness, latitude, detail contrast, sharpness, and appearance of noise. The detail contrast and sharpness can be controlled in a density-dependent manner. The algorithm uses a multifrequency-band decomposition wherein the bands of the decomposition are manipulated, and the reconstructed image is passed through a tone-scale process that prepares the image for display. The rendering method is implemented in software on a workstation that enables interactive control of these image quality attributes in order to facilitate the determination of rendering aims for different exam types.

Display processing is used to transform digital radiography raw data in log-signal units to display values for presentation using a workstation or film printer. Radiographic appearance with respect to subject latitude and detail contrast varies significantly depending on the signal equalization and grayscale rendition used for processing. A human observer study was conducted to define the latitude and detail contrast that is judged optimal for a broad spectrum of chest radiographs. Raw data for 12 chest radiographs acquired with storage phosphor digital radiography systems were transformed using 52 different combinations of latitude and detail contrast. For specific latitude values, contrast was adjusted by varying the equalization gain. Three radiologists at three different medical centers evaluated the images. Each image was compared to a reference image using a calibrated display on a computer workstation. For PA views, processing that produced a detail contrast of 3.14 (DeltaD/Delta logE) and latitude of 1.47 (Delta logE for DeltaD = 1.75) was determined to be best for all cases and was achieved with an equalization gain of 2.64. For lateral views, a detail contrast of 3.42 and latitude of 1.17 was best for all cases (gain = 2.29). For individual cases, the preferred processing varied from the global average primarily with respect to latitude.