Sparse-sampling computed tomography for pulmonary imaging

Since childhood exposure to radiation has been demonstrated to increase cancer risk with increase in radiation dose, reduced radiation exposure during computed tomography (CT) evaluation is desired for adolescent idiopathic scoliosis (AIS). Therefore, this retrospective study aimed to investigate the radiation dose of dual-source CT using a spectral shaping technique and the accuracy of the thoracic pedicle screw (TPS) placement for posterior spinal fusion (PSF) in patients with AIS. Fifty-nine female patients with thoracic AIS who underwent PSF using CT-guided TPSs were included and divided into two groups comprised of 23 patients who underwent dual-source CT (DSCT) with a tin filter (DSCT group) and 36 who underwent conventional multislice CT (MSCT group). We assessed the CT radiation dose using the CT dose index (CTDIvol), effective dose (ED), and accuracy of TPS insertion according to the established Neo's classification. The DSCT and MSCT groups differed significantly (p < 0.001) in the mean CTDIvol (0.76 vs. 3.31mGy, respectively) and ED (0.77 vs. 3.47mSv, respectively). Although the correction rate of the main thoracic curve in the DSCT group was lower (65.7% vs. 71.2%) (p = 0.0126), the TPS accuracy (Grades 0-1) was similar in both groups (381 screws [88.8%] vs. 600 screws [88.4%], respectively) (p = 0.8133). No patient required replacement of malpositioned screws. Spectral shaping DSCT with a tube-based tin filter allowed a 75% radiation dose reduction while achieving TPS insertion accuracy similar to procedures based on conventional CT without spectral shaping.

Background:Assessment of intestinal activity and severity of Crohn’s disease (CD) is crucial to guide treatment. In this study, we aimed to investigate the accuracy of spectral computed tomography (CT) in this assessment and make a comparison with conventional CT.Methods:A total of 50 patients with ileocolonic CD underwent spectral CT scanning. Conventional CT and spectral CT images were reconstructed. Endoscopic lesions were classified as absent, mild lesions and severe lesions. Qualitative and quantitative findings in CT images were compared in these segments. Logistic regressions were established, based on conventional and spectral CT parameters, to predict intestinal activity and severity. Comparisons were made by receiver operating characteristic (ROC) curve.Results:The results showed that bowel wall hyperenhancement, ulcers on CT images, comb sign, bowel wall thickness, normalized iodine concentration (NIC) and slope of HU curve (λHU) increased significantly (p < 0.01) with endoscopic severity. In predicting intestinal activity, spectral CT demonstrated higher accuracy (99.6% versus 94.7%), sensitivity (99.1% versus 93.4%) and specificity (99.9% versus 94.4%) than conventional CT. In predicting intestinal severity, spectral CT also had higher accuracy (96.5% versus 91.9%), sensitivity (96.5% versus 92.1%) and specificity (95.8% versus 89.8%) than conventional CT. Besides, both NIC and λHU correlated significantly with Simple Endoscopic Score for CD (r = 0.833 and r = 0.771; both p < 0.001), but their correlations with C-reactive protein (r = 0.578 and r = 0.513; both p < 0.01) and Harvey–Bradshaw Index (r = 0.545 and r = 0.522; both p < 0.01) were moderate.Conclusions:Compared with conventional CT, spectral CT had higher accuracy in detecting intestinal activity and severity of CD, which could be an alternative choice in evaluation of CD.

OA158] Noise equivalent number of quanta (NEQ): A tool for choosing the optimal reconstruction algorithm in computed tomography (CT)

Accurate noninvasive measurement of the volume of the liver has many potential clinical applications and is an important element in the preoperative evaluation of the liver transplant recipient. Helical (or spiral) computed tomography (CT) has theoretical advantages over conventional CT in this application because the entire liver can be imaged in a single breath-hold, limiting inaccuracies related to patient motion and respiratory variation. We compared liver volumes measured by conventional CT and helical CT in 22 patients undergoing preoperative evaluation for liver transplantation. The mean volume for helical CT is 1328 ml (SD 405) and conventional CT is 1323 ml (SD 417). We found no significant difference between the helical CT and conventional CT volumes for each patient.

Modern computed tomography (CT) systems are supporting increasingly fast rotation speeds, which are a prerequisite for fast dynamic acquisition, e.g. in perfusion imaging, and for new modalities such as dedicated breast CT, where breathhold scanning is indicated. However, not all detector technologies are supporting the high frame rates that are necessary to retain high resolution for objects far away from the isocenter. Even on systems that would support a sufficiently high frame rate, the necessary bandwidth of the data transfer from the rotating gantry stills remains challenging. The authors evaluated a pixel shifting technique termed time-delayed summation (TDS) as a method of increasing resolution on fast rotating CT systems without the need to increase the frame rate. In TDS mode, detector pixel values are shifted along rows during image acquisition to compensate for detector motion. In order to fully exploit TDS, focal spot position control (FSC) was used in combination with TDS. FSC applies a counter movement to the x-ray focal spot during image acquisition such that it is kept fixed in space. As a proof of concept, measurements were performed on a prototype photon counting detector capable of TDS. The detector was mounted on a movable table and a gold wire phantom was imaged with different TDS settings and detector velocities. Additionally, simulations of a broad range of TDS and FSC settings on two different modalities, a clinical CT scanner and a breast CT scanner, and two different detector geometries, flat and cylindrical, were performed to assess the gain in resolution and contrast in cylindrical water phantoms containing a small wire at distances from the phantom center varied from 5% to 90% of the phantom radius. As figures of merit, the modulation transfer function (MTF) at 10% and the maximum contrast were used and compared against the respective values when using step-and-shoot acquisition, which means stopping the rotation when a projection image is acquired. Measurements showed that detector movement and the resulting blurring of the wire projections were compensated to the expected degree when using the appropriate number of TDS shifts per frame (TDS factor). Using simulations it was found that when using the optimal TDS factor, over 90% of the resolution achieved in step-and-shot mode was reached for all investigated wire positions. TDS showed better performance on a cylindrical detector that on the same system with a flat detector. TDS factors that were deviating from the optimum by more than 1 shift led to a performance below that of standard continuous acquisition. The findings of this study encourage the combined usage of TDS and FSC in systems that require fast rotation. The integration of TDS in state-of-the-art x-ray detectors is feasible.

Sparse-view computed tomography (CT) has been attracting attention for its reduced radiation dose and scanning time. However, analytical image reconstruction methods suffer from streak artifacts due to insufficient projection views. Recently, various deep learning-based methods have been developed to solve this ill-posed inverse problem. Despite their promising results, they are easily overfitted to the training data, showing limited generalizability to unseen systems and patients. In this work, we propose a novel streak artifact reduction algorithm that provides a system- and patient-specificsolution. Motivated by the fact that streak artifacts are deterministic errors, we regenerate the same artifacts from a prior CT image under the same system geometry. This prior image need not be perfect but should contain patient-specific information and be consistent with full-view projection data for accurate regeneration of the artifacts. To this end, we use a coordinate-based neural representation that often causes image blur but can greatly suppress the streak artifacts while having multiview consistency. By employing techniques in neural radiance fields originally proposed for scene representations, the neural representation is optimized to the measured sparse-view projection data via self-supervised learning. Then, we subtract the regenerated artifacts from the analytically reconstructed original image to obtain the final correctedimage. To validate the proposed method, we used simulated data of extended cardiac-torso phantoms and the 2016 NIH-AAPM-Mayo Clinic Low-Dose CT Grand Challenge and experimental data of physical pediatric and head phantoms. The performance of the proposed method was compared with a total variation-based iterative reconstruction method, naive application of the neural representation, and a convolutional neural network-based method. In visual inspection, it was observed that the small anatomical features were best preserved by the proposed method. The proposed method also achieved the best scores in the visual information fidelity, modulation transfer function, and lung nodulesegmentation. The results on both simulated and experimental data suggest that the proposed method can effectively reduce the streak artifacts while preserving small anatomical structures that are easily blurred or replaced with misleading features by the existing methods. Since the proposed method does not require any additional training datasets, it would be useful in clinical practice where the large datasets cannot becollected.

To validate a normal-resolution (NR) simulation (NRsim) algorithm that uses high-resolution (HR) or super-high resolution (SHR) acquisitions on a commercial HR computed tomography (CT) scanner by comparing image quality between NRsim-generated images and actual NR images. NRsim is intended to allow direct comparison between normal-resolution CT and HR/SHR reconstructions in clinical investigations, without repeating exams. The Aquilion Precision CT (Canon Medical Systems Corporation) HR CT scanner has three resolution modes resulting from detector binning in the channel (x-y) and row (z) directions. For NR, each detector element is 0.5mm×0.5mm along the channel and row directions, 0.25mm×0.5mm for HR, and 0.25mm×0.25mm for SHR. The NRsim algorithm simulates NR acquisitions from HR or SHR acquisitions (termed NRHR and NRSHR , respectively) by downsampling the pre-log raw data in the channel direction for the HR acquisitions and in the channel and row direction for the SHR acquisition. The downsampled data are then reconstructed using the same process as NR. The axial modulation transfer function (MTF), slice sensitivity profile (SSP), and CT number accuracy were measured using the Catphan 600 phantom, and the three-dimensional noise power spectrum (NPS) was measured in water-equivalent phantoms for standard protocols across a range of size-specific dose estimates (SSDE): head (6.2-29.8mGy), lung (2.2-18.2mGy), and body (5.6-19.4mGy). The MTF and NPS measurements were combined to estimate low-contrast detectability (LCD) using a non-prewhitening model observer with an eye filter for a 5-mm disk with 10HU contrast. All metrics were compared for NR, NRHR , and NRSHR images reconstructed using filtered back projection (FBP) and an iterative reconstruction algorithm (AIDR3D). We chose a 15% error threshold as a reasonable definition of success for NRsim when compared against actual NR based on published studies showing that a just-noticeable difference in image noise level for human observers is typically <15%. The axial MTF and SSPs for NRsim were in good agreement with NR demonstrated by a maximum difference of 5.1% for the MTF at 10% and 50% across materials (air, Teflon, LDPE, and polystyrene) and a maximum SSP difference of 2.2%. Noise magnitude differences were within 15% across the SSDE levels with the exception of below 4.5mGy for the lung protocol with FBP. The relative RMSE of normalized NPS comparisons were all <15%. Differences in CT numbers for NRsim reconstructions were within 2HU of NR. LCD for NRsim was within 15% of NR with the exception of NRSHR for the lung protocol SSDE levels below 3.7mGy with FBP. NRsim, an algorithm for simulating NR acquisitions using HR and SHR raw data, was introduced and shown to generate images with spatial resolution, noise, HU accuracy, and LCD largely equivalent to scans acquired using an actual NR acquisition. At SSDE levels below ~5mGy for the lung protocol, differences in noise magnitude and LCD for NRSHR were >15% which defines a region where NRsim degrades due to contributions from electronic noise.

Patient dose estimation in cardiac computed tomography with a 320 detector row scanner based on Monte Carlo simulation

Planar X-ray mammography is the standard medical imaging modality for the early detection of breast cancer. Based on advancements in digital flat-panel detector technology, dedicated x-ray computed tomography (CT) mammography is a modality under investigation that offers the potential for improved breast tumor imaging. We have implemented a prototype half cone-beam CT breast imaging system that utilizes an indirect flat-panel detector. This prototype can be used to explore and evaluate the effect of varying acquisition and reconstruction parameters on image quality. This report describes our system and characterizes the performance of the system through the analysis of Modulation Transfer Function (MTF) and Noise Power Spectrum (NPS). All CT reconstructions were made using Feldkamp's filtered backprojection algorithm. The 3D MTF was determined by the analysis of the plane spread function (PlSF) derived from the surface spread function (SSF) of reconstructed 6.3mm spheres. 3D NPS characterization was performed through the analysis of a 3D volume extracted from zero-mean CT noise of air reconstructions. The effect of varying locations on MTF and the effect of different Butterworth filter cutoff frequencies on NPS are reported. Finally, we present CT images of mastectomy excised breast tissue. Breast specimen images were acquired on our CTMS using an x-ray technique similar to the one used during performance characterization. Specimen images demonstrate the inherent CT capability to reduce the masking effect of anatomical noise. Both the quantitative system characterization and the breast specimen images continue to reinforce the hope that dedicated flat-panel detector, x-ray cone-beam CT will eventually provide enhanced breast cancer detection capability.

Objectives:To evaluate the clinical performance of a cadmium-zinc-telluride- (CZT-) based photon-counting computed tomography (PCCT) system for low-dose lung cancer screening (LCS-LDCT) using patient-specific 3D-printed lung phantoms, and to compare its image quality and radiomics consistency with a conventional energy-integrating detector CT (EIDCT) system.Methods:Six 3D-printed lung phantoms, derived from patient CT datasets and representing various lesion types (solid, part-solid, and ground-glass), were imaged on PCCT and EIDCT scanners at matched dose levels (1.6 – 20.4 mGy). Quantitative image metrics, Hounsfield unit (HU) accuracy, image noise, and contrast-to-noise ratio (CNR), were assessed across dose levels. Radiomic features were extracted for each lesion and analyzed via principal component analysis to quantify feature consistency (within-cluster distance) and lesion type separability.Results:PCCT demonstrated significantly lower image noise and higher CNR compared with EIDCT, particularly at lower dose levels. HU values were consistent across doses for both systems, with reduced variability in PCCT (coefficient of variation < 0.004). Radiomics analysis revealed tighter clustering (reduced within-cluster distances) and comparable lesion type separability between PCCT and EIDCT, indicating enhanced feature stability and lesion differentiation. Qualitative review confirmed superior lesion conspicuity and margin delineation with PCCT.Conclusions:CZT-based PCCT outperforms conventional EIDCT in quantitative and qualitative imaging metrics for LCS-LDCT, enabling superior image quality and radiomics reproducibility at reduced radiation doses. These findings support the clinical translation of PCCT for lung cancer screening and radiomics-based lesion characterization.

When patient anatomy is positioned away from a CT scanner's isocenter, scans of limited diagnostic value may result. Yet in some cases, positioning of patient anatomy far from isocenter is unavoidable. This study examines the effect of position and reconstruction algorithm on image resolution achieved by a CT scanner operating in a high resolution (HR) scan mode which incorporates focal spot deflection and acquires an increased number of projections per rotation. Images of a metal bead contained in a phantom were acquired on a GE CT750 HD scanner with multiple reconstruction algorithms, in the normal and HR scan mode, and at two positions, scanner isocenter and 15 cm directly above isocenter. The images of the metal bead yielded two‐dimensional point spread functions which were averaged along two perpendicular directions to yield line spread functions. Fourier transforms of the line spread functions yielded radial and azimuthal modulation transfer functions (MTFs). At isocenter, the radial and azimuthal MTFs were averaged. MTF improvement depended on image position and modulation direction. The results from a single algorithm, Edge, can be generalized to other algorithms. At isocenter, the 10% MTF cutoff was 14.4 cycles/cm in normal and HR mode. At 15 cm above isocenter, the 10% cutoff was 6.0 and 8.5 cycles/cm for the azimuthal and radial MTFs in normal mode. In HR mode, the azimuthal and radial MTF 10% cutoff was 8.3 and 10.3 cycles/cm. Our results indicate that the best image resolution is achieved at scanner isocenter and that the azimuthal resolution degrades more significantly than the radial resolution. For the GE CT750 HD CT scanner, the resolution is significantly enhanced by the HR scan mode away from scanner isocenter, and the use of the HR scan mode has much more of an impact on image resolution away from isocenter than the choice of algorithm.PACS number(s): 87.57.Q‐

Low-dose computed tomography (CT) is desirable for treatment planning and simulation in radiation therapy. Multiple rescanning and replanning during the treatment course with a smaller amount of dose than a single conventional full-dose CT simulation is a crucial step in adaptive radiation therapy. We developed a machine learning-based method to improve image quality of low-dose CT for radiation therapy treatment simulation. We used a residual block concept and a self-attention strategy with a cycle-consistent adversarial network framework. A fully convolution neural network with residual blocks and attention gates (AGs) was used in the generator to enable end-to-end transformation. We have collected CT images from 30 patients treated with frameless brain stereotactic radiosurgery (SRS) for this study. These full-dose images were used to generate projection data, which were then added with noise to simulate the low-mAs scanning scenario. Low-dose CT images were reconstructed from this noise-contaminated projection data and were fed into our network along with the original full-dose CT images for training. The performance of our network was evaluated by quantitatively comparing the high-quality CT images generated by our method with the original full-dose images. When mAs is reduced to 0.5% of the original CT scan, the mean square error of the CT images obtained by our method is , with respect to the original full-dose images. The proposed method successfully improved the noise, contract-to-noise ratio, and nonuniformity level to be close to those of full-dose CT images and outperforms a state-of-the-art iterative reconstruction method. Dosimetric studies show that the average differences of dose-volume histogram metrics are ( ). These quantitative results strongly indicate that the denoised low-dose CT images using our method maintains image accuracy and quality and are accurate enough for dose calculation in current CT simulation of brain SRS treatment. We also demonstrate the great potential for low-dose CT in the process of simulation and treatment planning.

For a given imaging task and patient size, the optimal selection of x-ray tube potential (kV) and tube current-rotation time product (mAs) is pivotal in achieving the maximal radiation dose reduction while maintaining the needed diagnostic performance. Although contrast-to-noise (CNR)-based strategies can be used to optimize kV/mAs for computed tomography (CT) imaging systems employing the linear filtered backprojection (FBP) reconstruction method, a more general framework needs to be developed for systems using the nonlinear statistical model-based iterative reconstruction (MBIR) method. The purpose of this paper is to present such a unified framework for the optimization of kV/mAs selection for both FBP- and MBIR-based CT systems. The optimal selection of kV and mAs was formulated as a constrained optimization problem to minimize the objective function, Dose(kV,mAs), under the constraint that the achievable detectability index d'(kV,mAs) is not lower than the prescribed value of d'R for a given imaging task. Since it is difficult to analytically model the dependence of d' on kV and mAs for the highly nonlinear MBIR method, this constrained optimization problem is solved with comprehensive measurements of Dose(kV,mAs) and d'(kV,mAs) at a variety of kV-mAs combinations, after which the overlay of the dose contours and d' contours is used to graphically determine the optimal kV-mAs combination to achieve the lowest dose while maintaining the needed detectability for the given imaging task. As an example, d' for a 17 mm hypoattenuating liver lesion detection task was experimentally measured with an anthropomorphic abdominal phantom at four tube potentials (80, 100, 120, and 140 kV) and fifteen mA levels (25 and 50-700) with a sampling interval of 50 mA at a fixed rotation time of 0.5 s, which corresponded to a dose (CTDIvol) range of [0.6, 70] mGy. Using the proposed method, the optimal kV and mA that minimized dose for the prescribed detectability level of d'R=16 were determined. As another example, the optimal kV and mA for an 8 mm hyperattenuating liver lesion detection task were also measured using the developed framework. Both an in vivo animal and human subject study were used as demonstrations of how the developed framework can be applied to the clinical work flow. For the first task, the optimal kV and mAs were measured to be 100 and 500, respectively, for FBP, which corresponded to a dose level of 24 mGy. In comparison, the optimal kV and mAs for MBIR were 80 and 150, respectively, which corresponded to a dose level of 4 mGy. The topographies of the iso-d' map and the iso-CNR map were the same for FBP; thus, the use of d'- and CNR-based optimization methods generated the same results for FBP. However, the topographies of the iso-d' and iso-CNR map were significantly different in MBIR; the CNR-based method overestimated the performance of MBIR, predicting an overly aggressive dose reduction factor. For the second task, the developed framework generated the following optimization results: for FBP, kV = 140, mA = 350, dose = 37.5 mGy; for MBIR, kV = 120, mA = 250, dose = 18.8 mGy. Again, the CNR-based method overestimated the performance of MBIR. Results of the preliminary in vivo studies were consistent with those of the phantom experiments. A unified and task-driven kV/mAs optimization framework has been developed in this work. The framework is applicable to both linear and nonlinear CT systems such as those using the MBIR method. As expected, the developed framework can be reduced to the conventional CNR-based kV/mAs optimization frameworks if the system is linear. For MBIR-based nonlinear CT systems, however, the developed task-based kV/mAs optimization framework is needed to achieve the maximal dose reduction while maintaining the desired diagnostic performance.

To determine the accuracy of unenhanced helical computed tomography (CT) performed at reduced milliampere-second, and therefore at a reduced patient radiation dose, by using conventional unenhanced helical CT as the standard. Fifty patients with acute flank pain who weighed less than 200 lb (90 kg) were prospectively recruited for this study. Conventional helical CT scans were obtained with patients in the prone position by using 5-mm-thick sections, 140 kVp, 135-208 mAs (mean, 160 mAs), and a pitch of 1.5 (single-detector row CT) or 0.75 (multi-detector row CT, 4 x 5-mm detector configuration). Conventional CT was immediately followed by low-dose scanning, whereby the tube current was reduced to 100 mA (mean, 76 mAs). All other technical parameters and anatomic coverage remained constant. Three independent readers who were blinded to patient identity interpreted the scans in random order. The observers noted the location, size, and number of calculi; secondary signs of obstruction; and other clinically relevant findings. High- and low-dose scans were compared by using paired t tests and the signed rank test. Calculi were found in 33 (66%) patients; 25 (50%) had renal calculi and 19 (38%) had an obstructing ureteral calculus. The accuracy rates (averaged over the three readers) for determining the various findings on the low-dose scan compared with the high-dose scan were as follows: nephrolithiasis, 91%; ureterolithiasis, 94%; obstruction, 91%; and normal findings, 92%. When interpretations between readers were compared, agreement rates were 90%-95% for standard-dose scans and 90%-92% for reduced-dose scans (P >.5). Uncomplicated mild diverticulitis was found in three patients. No other clinically important abnormality was identified. A reduction in the tube current to 100 mA resulted in a dose reduction of 25% for multi-detector row CT and 42% for single-detector row CT. In patients who weighed less than 200 lb, unenhanced helical CT performed at a reduced tube current of 100 mA, and therefore at a reduced patient dose, resulted in scans of high accuracy.

To mitigate the public health risks associated with the increasing utilization of computed tomography (CT), it is desirable to implement a low-dose scanning protocol. However, low-dose CT produces poor image quality due to the increased quantum noise, requiring an effective image denoising method for its clinical use. Recently, convolutional neural network (CNN)-based methods showed state-of-the-art performance in CT image denoising. However, a large amount of paired training data is essential for their high performance, which is generally not available in medical imaging fields. To alleviate this problem, we propose a new framework to train a CNN-based denoiser without real paired CT images or access to the proprietary information such as raw scan data and a reconstruction kernel. Specifically, we estimate a reconstruction kernel from normal-dose CT images and synthesize paired CT images using an image domain noise insertion technique. To validate the proposed method, we used extended cardiac-torso phantoms and the 2016 Low-Dose CT Grand Challenge datasets. The denoising performance of the CNN-based denoiser trained with the proposed method was compared with the conventional approach that generated paired training data by adding noise to raw scan data and reconstructing images with the known reconstruction kernel. To quantitatively evaluate the image quality, we used the peak signal-to-noise ratio, structural similarity index measure, modulation transfer function, and noise power spectrum. The extensive qualitative and quantitative results suggest that the proposed method enables CNN-based denoisers to be trained without access to raw scan data and a reconstruction kernel while maintaining their denoising performance.