Calycosin protects against oxidative stress-induced cardiomyocyte apoptosis by activating aldehyde dehydrogenase 2.

Myocardial infarction (MI) is the leading cause of death worldwide, and oxidative stress is part of the process that causes MI. Calycosin, a naturally occurring substance with cardioprotective properties, is one of the major active constituents in Radix Astragali. In this study, effect of Calycosin was investigated in vivo and in vitro to determine whether it could alleviate oxidative stress and oxidative stress-induced cardiac apoptosis in neonatal cardiomyocytes (NCMs) via activation of aldehyde dehydrogenase 2 (ALDH2). Calycosin protected against oxidative stress and oxidative stress-induced apoptosis in NCMs. Molecular docking revealed that the ALDH2-Calycosin complex had a binding energy of -9.885 kcal/mol. In addition, molecular docking simulations demonstrated that the ALDH2-Calycosin complex was stable. Using BLI assays, we confirmed that Calycosin could interact with ALDH2 (KD  = 1.9 × 10-4 M). Furthermore, an ALDH2 kinase activity test revealed that Calycosin increased ALDH2 activity, exhibiting an EC50 of 91.79 µM. Pre-incubation with ALDH2 inhibitor (CVT-10216 or disulfiram) reduced the cardio-protective properties Calycosin. In mice with MI, Calycosin therapy substantially reduced myocardial apoptosis, oxidative stress, and activated ALDH2. Collectively, our findings clearly suggest that Calycosin reduces oxidative stress and oxidative stress-induced apoptosis via the regulation of ALDH2 signaling, which supports potential therapeutic use in MI.

Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence.

Background Radiologists are proficient in differentiating between chest radiographs with and without symptoms of pneumonia but have found it more challenging to differentiate coronavirus disease 2019 (COVID-19) pneumonia from non-COVID-19 pneumonia on chest radiographs. Purpose To develop an artificial intelligence algorithm to differentiate COVID-19 pneumonia from other causes of abnormalities at chest radiography. Materials and Methods In this retrospective study, a deep neural network, CV19-Net, was trained, validated, and tested on chest radiographs in patients with and without COVID-19 pneumonia. For the chest radiographs positive for COVID-19, patients with reverse transcription polymerase chain reaction results positive for severe acute respiratory syndrome coronavirus 2 with findings positive for pneumonia between February 1, 2020, and May 30, 2020, were included. For the non-COVID-19 chest radiographs, patients with pneumonia who underwent chest radiography between October 1, 2019, and December 31, 2019, were included. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated to characterize diagnostic performance. To benchmark the performance of CV19-Net, a randomly sampled test data set composed of 500 chest radiographs in 500 patients was evaluated by the CV19-Net and three experienced thoracic radiologists. Results A total of 2060 patients (5806 chest radiographs; mean age, 62 years ± 16 [standard deviation]; 1059 men) with COVID-19 pneumonia and 3148 patients (5300 chest radiographs; mean age, 64 years ± 18; 1578 men) with non-COVID-19 pneumonia were included and split into training and validation and test data sets. For the test set, CV19-Net achieved an AUC of 0.92 (95% CI: 0.91, 0.93). This corresponded to a sensitivity of 88% (95% CI: 87, 89) and a specificity of 79% (95% CI: 77, 80) by using a high-sensitivity operating threshold, or a sensitivity of 78% (95% CI: 77, 79) and a specificity of 89% (95% CI: 88, 90) by using a high-specificity operating threshold. For the 500 sampled chest radiographs, CV19-Net achieved an AUC of 0.94 (95% CI: 0.93, 0.96) compared with an AUC of 0.85 (95% CI: 0.81, 0.88) achieved by radiologists. Conclusion CV19-Net was able to differentiate coronavirus disease 2019-related pneumonia from other types of pneumonia, with performance exceeding that of experienced thoracic radiologists. © RSNA, 2021 Online supplemental material is available for this article.

Prospective Evaluation of Reduced Dose Computed Tomography for the Detection of Low-Contrast Liver Lesions: Direct Comparison with Concurrent Standard Dose Imaging.

OBJECTIVES: To prospectively compare the diagnostic performance of reduced-dose (RD) contrast-enhanced CT (CECT) with standard-dose (SD) CECT for detection of low-contrast liver lesions. METHODS: Seventy adults with non-liver primary malignancies underwent abdominal SD-CECT immediately followed by RD-CECT, aggressively targeted at 60-70 % dose reduction. SD series were reconstructed using FBP. RD series were reconstructed with FBP, ASIR, and MBIR (Veo). Three readers-blinded to clinical history and comparison studies-reviewed all series, identifying liver lesions ≥4 mm. Non-blinded review by two experienced abdominal radiologists-assessing SD against available clinical and radiologic information-established the reference standard. RESULTS: RD-CECT mean effective dose was 2.01 ± 1.36 mSv (median, 1.71), a 64.1 ± 8.8 % reduction. Pooled per-patient performance data were (sensitivity/specificity/PPV/NPV/accuracy) 0.91/0.78/0.60/0.96/0.81 for SD-FBP compared with RD-FBP 0.79/0.75/0.54/0.91/0.76; RD-ASIR 0.84/0.75/0.56/0.93/0.78; and RD-MBIR 0.84/0.68/0.49/0.92/0.72. ROC AUC values were 0.896/0.834/0.858/0.854 for SD-FBP/RD-FBP/RD-ASIR/RD-MBIR, respectively. RD-FBP (P = 0.002) and RD-MBIR (P = 0.032) AUCs were significantly lower than those of SD-FBP; RD-ASIR was not (P = 0.052). Reader confidence was lower for all RD series (P < 0.001) compared with SD-FBP, especially when calling patients entirely negative. CONCLUSIONS: Aggressive CT dose reduction resulted in inferior diagnostic performance and reader confidence for detection of low-contrast liver lesions compared to SD. Relative to RD-ASIR, RD-FBP showed decreased sensitivity and RD-MBIR showed decreased specificity. KEY POINTS: • Reduced-dose CECT demonstrates inferior diagnostic performance for detecting low-contrast liver lesions. • Reader confidence is lower with reduced-dose CECT compared to standard-dose CECT. • Overly aggressive dose reduction may result in misdiagnosis, regardless of reconstruction algorithm. • Careful consideration of perceived risks versus benefits of dose reduction is crucial.

Accuracy of Liver Fat Quantification With Advanced CT, MRI, and Ultrasound Techniques: Prospective Comparison With MR Spectroscopy.

OBJECTIVE: The purpose of this study was to prospectively evaluate the accuracy of proton-density fat-fraction, single- and dual-energy CT (SECT and DECT), gray-scale ultrasound (US), and US shear-wave elastography (US-SWE) in the quantification of hepatic steatosis with MR spectroscopy (MRS) as the reference standard. SUBJECTS AND METHODS: Fifty adults who did not have symptoms (23 men, 27 women; mean age, 57 ± 5 years; body mass index, 27 ± 5) underwent liver imaging with un-enhanced SECT, DECT, gray-scale US, US-SWE, proton-density fat-fraction MRI, and MRS for this prospective trial. MRS voxels for the reference standard were colocalized with all other modalities under investigation. For SECT (120 kVp), attenuation values were recorded. For rapid-switching DECT (80/140 kVp), monochromatic images (70-140 keV) and fat density-derived material decomposition images were reconstructed. For proton-density fat fraction MRI, a quantitative chemical shift-encoded method was used. For US, echogenicity was evaluated on a qualitative 0-3 scale. Quantitative US shear-wave velocities were also recorded. Data were analyzed by linear regression for each technique compared with MRS. RESULTS: There was excellent correlation between MRS and both proton-density fat-fraction MRI (r2 = 0.992; slope, 0.974; intercept, -0.943) and SECT (r2 = 0.856; slope, -0.559; intercept, 35.418). DECT fat attenuation had moderate correlation with MRS measurements (r2 = 0.423; slope, 0.034; intercept, 8.459). There was good correlation between qualitative US echogenicity and MRS measurements with a weighted kappa value of 0.82. US-SWE velocity did not have reliable correlation with MRS measurements (r2 = 0.004; slope, 0.069; intercept, 6.168). CONCLUSION: Quantitative MRI proton-density fat fraction and SECT fat attenuation have excellent linear correlation with MRS measurements and can serve as accurate noninvasive biomarkers for quantifying steatosis. Material decomposition with DECT does not improve the accuracy of fat quantification over conventional SECT attenuation. US-SWE has poor accuracy for liver fat quantification.

Improving radiation dose efficiency of X-ray differential phase contrast imaging using an energy-resolving grating interferometer and a novel rank constraint.

In this paper, a novel method was developed to improve the radiation dose efficiency, viz., contrast to noise ratio normalized by dose (CNRD), of the grating-based X-ray differential phase contrast (DPC) imaging system that is integrated with an energy-resolving photon counting detector. The method exploits the low-dimensionality of the spatial-spectral DPC image matrix acquired from different energy windows. A low rank approximation of the spatial-spectral image matrix was developed to reduce image noise while retaining the DPC signal accuracy for every energy window. Numerical simulations and experimental phantom studies have been performed to validate the proposed method by showing noise reduction and CNRD improvement for each energy window.

Prior Image Constrained Compressed Sensing Metal Artifact Reduction (PICCS-MAR): 2D and 3D Image Quality Improvement with Hip Prostheses at CT Colonography.

PURPOSE: To assess the effect of the prior-image-constrained-compressed-sensing-based metal-artefact-reduction (PICCS-MAR) algorithm on streak artefact reduction and 2D and 3D-image quality improvement in patients with total hip arthroplasty (THA) undergoing CT colonography (CTC). MATERIALS AND METHODS: PICCS-MAR was applied to filtered-back-projection (FBP)-reconstructed DICOM CTC-images in 52 patients with THA (unilateral, n = 30; bilateral, n = 22). For FBP and PICCS-MAR series, ROI-measurements of CT-numbers were obtained at predefined levels for fat, muscle, air, and the most severe artefact. Two radiologists independently reviewed 2D and 3D CTC-images and graded artefacts and image quality using a five-point-scale (1 = severe streak/no-diagnostic confidence, 5 = no streak/excellent image-quality, high-confidence). Results were compared using paired and unpaired t-tests and Wilcoxon signed-rank and Mann-Whitney-tests. RESULTS: Streak artefacts and image quality scores for FBP versus PICCS-MAR 2D-images (median: 1 vs. 3 and 2 vs. 3, respectively) and 3D images (median: 2 vs. 4 and 3 vs. 4, respectively) showed significant improvement after PICCS-MAR (all P < 0.001). PICCS-MAR significantly improved the accuracy of mean CT numbers for fat, muscle and the area with the most severe artefact (all P < 0.001). CONCLUSIONS: PICCS-MAR substantially reduces streak artefacts related to THA on DICOM images, thereby enhancing visualization of anatomy on 2D and 3D CTC images and increasing diagnostic confidence. KEY POINTS: • PICCS-MAR significantly reduces streak artefacts associated with total hip arthroplasty on 2D and 3D CTC. • PICCS-MAR significantly improves 2D and 3D CTC image quality and diagnostic confidence. • PICCS-MAR can be applied retrospectively to DICOM images from single-kVp CT.

Time-Resolved C-Arm Computed Tomographic Angiography Derived From Computed Tomographic Perfusion Acquisition: New Capability for One-Stop-Shop Acute Ischemic Stroke Treatment in the Angiosuite.

BACKGROUND AND PURPOSE: Multimodal imaging using cone beam C-arm computed tomography (CT) may shorten the delay from ictus to revascularization for acute ischemic stroke patients with a large vessel occlusion. Largely because of limited temporal resolution, reconstruction of time-resolved CT angiography (CTA) from these systems has not yielded satisfactory results. We evaluated the image quality and diagnostic value of time-resolved C-arm CTA reconstructed using novel image processing algorithms. METHODS: Studies were done under an Institutional Review Board approved protocol. Postprocessing of data from 21 C-arm CT dynamic perfusion acquisitions from 17 patients with acute ischemic stroke were done to derive time-resolved C-arm CTA images. Two observers independently evaluated image quality and diagnostic content for each case. ICC and receiver-operating characteristic analysis were performed to evaluate interobserver agreement and diagnostic value of this novel imaging modality. RESULTS: Time-resolved C-arm CTA images were successfully generated from 20 data sets (95.2%, 20/21). Two observers agreed well that the image quality for large cerebral arteries was good but was more limited for small cerebral arteries (distal to M1, A1, and P1). receiver-operating characteristic curves demonstrated excellent diagnostic value for detecting large vessel occlusions (area under the curve=0.987-1). CONCLUSIONS: Time-resolved CTAs derived from C-arm CT perfusion acquisitions provide high quality images that allowed accurate diagnosis of large vessel occlusions. Although image quality of smaller arteries in this study was not optimal ongoing modifications of the postprocessing algorithm will likely remove this limitation. Adding time-resolved C-arm CTAs to the capabilities of the angiography suite further enhances its suitability as a one-stop shop for care for patients with acute ischemic stroke.

Sub-milliSievert (sub-mSv) CT colonography: a prospective comparison of image quality and polyp conspicuity at reduced-dose versus standard-dose imaging.

OBJECTIVE: To prospectively compare reduced-dose (RD) CT colonography (CTC) with standard-dose (SD) imaging using several reconstruction algorithms. METHODS: Following SD supine CTC, 40 patients (mean age, 57.3 years; 17 M/23 F; mean BMI, 27.2) underwent an additional RD supine examination (targeted dose reduction, 70-90%). DLP, CTDI(vol), effective dose, and SSDE were compared. Several reconstruction algorithms were applied to RD series. SD-FBP served as reference standard. Objective image noise, subjective image quality and polyp conspicuity were assessed. RESULTS: Mean CTDI(vol) and effective dose for RD series was 0.89 mGy (median 0.65) and 0.6 mSv (median 0.44), compared with 3.8 mGy (median 3.1) and 2.8 mSv (median 2.3) for SD series, respectively. Mean dose reduction was 78%. Mean image noise was significantly reduced on RD-PICCS (24.3 ± 19HU) and RD-MBIR (19 ± 18HU) compared with RD-FBP (90 ± 33), RD-ASIR (72 ± 27) and SD-FBP (47 ± 14 HU). 2D image quality score was higher with RD-PICCS, RD-MBIR, and SD-FBP (2.7 ± 0.4/2.8 ± 0.4/2.9 ± 0.6) compared with RD-FBP (1.5 ± 0.4) and RD-ASIR (1.8 ± 0.44). A similar trend was seen with 3D image quality scores. Polyp conspicuity scores were similar between SD-FBP/RD-PICCS/RD-MBIR (3.5 ± 0.6/3.2 ± 0.8/3.3 ± 0.6). CONCLUSION: Sub-milliSievert CTC performed with iterative reconstruction techniques demonstrate decreased image quality compared to SD, but improved image quality compared to RD images reconstructed with FBP. KEY POINTS: • CT colonography dose can be substantially lowered using advanced iterative reconstruction techniques. • Iterative reconstruction techniques (MBIR/PICCS) reduce image noise and improve image quality. • The PICCS/MBIR-reconstructed, reduced-dose series shows decreased 2D/3D image quality compared to the standard-dose series. • Polyp conspicuity was similar on standard-dose images compared to reduced-dose images reconstructed with MBIR/PICCS.

Demonstration of temperature imaging by H2O absorption spectroscopy using compressed sensing tomography.

This paper introduces temperature imaging by total-variation-based compressed sensing (CS) tomography of H2O vapor absorption spectroscopy. A controlled laboratory setup is used to generate a constant two-dimensional temperature distribution in air (a roughly Gaussian temperature profile with a central temperature of 677 K). A wavelength-tunable laser beam is directed through the known distribution; the beam is translated and rotated using motorized stages to acquire complete absorption spectra in the 1330-1365 nm range at each of 64 beam locations and 60 view angles. Temperature reconstructions are compared to independent thermocouple measurements. Although the distribution studied is approximately axisymmetric, axisymmetry is not assumed and simulations show similar performance for arbitrary temperature distributions. We study the measurement error as a function of number of beams and view angles used in reconstruction to gauge the potential for application of CS in practical test articles where optical access is limited.

Prospective evaluation of prior image constrained compressed sensing (PICCS) algorithm in abdominal CT: a comparison of reduced dose with standard dose imaging.

PURPOSE: To prospectively study CT dose reduction using the "prior image constrained compressed sensing" (PICCS) reconstruction technique. METHODS: Immediately following routine standard dose (SD) abdominal MDCT, 50 patients (mean age, 57.7 years; mean BMI, 28.8) underwent a second reduced dose (RD) scan (targeted dose reduction, 70%-90%). DLP, CTDIvol, and SSDE were compared. Several reconstruction algorithms (FBP, ASIR, and PICCS) were applied to the RD series. SD images with FBP served as reference standard. Two blinded readers evaluated each series for subjective image quality and focal lesion detection. RESULTS: Mean DLP, CTDIvol, and SSDE for RD series were 140.3 mGy cm (median 79.4), 3.7 mGy (median 1.8), and 4.2 mGy (median 2.3) compared with 493.7 mGy cm (median 345.8), 12.9 mGy (median 7.9 mGy), and 14.6 mGy (median 10.1) for SD series, respectively. Mean effective patient diameter was 30.1 cm (median 30), which translates to a mean SSDE reduction of 72% (P < 0.001). RD-PICCS image quality score was 2.8 ± 0.5, improved over the RD-FBP (1.7 ± 0.7) and RD-ASIR (1.9 ± 0.8) (P < 0.001), but lower than SD (3.5 ± 0.5) (P < 0.001). Readers detected 81% (184/228) of focal lesions on RD-PICCS series, vs. 67% (153/228) and 65% (149/228) for RD-FBP and RD-ASIR, respectively. Mean image noise was significantly reduced on RD-PICCS series (13.9 HU) compared with RD-FBP (57.2) and RD-ASIR (44.1) (P < 0.001). CONCLUSION: PICCS allows for marked dose reduction at abdominal CT with improved image quality and diagnostic performance over reduced dose FBP and ASIR. Further study is needed to determine indication-specific dose reduction levels that preserve acceptable diagnostic accuracy relative to higher dose protocols.

Prospective trial of the detection of urolithiasis on ultralow dose (sub mSv) noncontrast computerized tomography: direct comparison against routine low dose reference standard.

PURPOSE: In this prospective trial we compared ultralow dose computerized tomography reconstruction algorithms and routine low dose computerized tomography for detecting urolithiasis. MATERIALS AND METHODS: A total of 48 consenting adults prospectively underwent routine low dose noncontrast computerized tomography immediately followed by an ultralow dose series targeted at a 70% to 90% reduction from the routine low dose technique (sub mSv range). Ultralow dose series were reconstructed with filtered back projection, and adaptive statistical and model based iterative reconstruction techniques. Transverse (axial) and coronal images were sequentially reviewed by 3 relatively inexperienced trainees, including a radiology resident, a urology fellow and an abdominal imaging fellow. Three experienced abdominal radiologists independently reviewed the routine low dose filtered back projection images, which served as the reference standard. RESULTS: The mean effective dose for the ultralow dose scans was 0.91 mSv (median 0.82), representing a mean ± SD 78% ± 5% decrease compared to the routine low dose. Overall sensitivity and positive predictive value per stone for ultralow dose computerized tomography at a 4 mm threshold was 0.91 and 0.98, respectively. Sensitivity, specificity, positive and negative predictive values, and accuracy per patient were 0.87, 1.00, 1.00, 0.94 and 0.96, respectively. At a 4 mm threshold the sensitivity and positive predictive value per stone of the ultralow dose series for filtered back projection, and adaptive statistical and model based iterative reconstruction was 0.89 and 0.96, 0.91 and 0.98, and 0.93 and 1.00, respectively. Sensitivity, specificity, positive and negative predictive values, and accuracy per patient at the 4 mm threshold were 0.82, 1.00, 1.00, 0.91 and 0.94 for filtered back projection, 0.85, 1.00, 1.00, 0.93 and 0.95 for adaptive statistical iterative reconstruction, and 0.94, 1.00, 1.00, 0.97 and 0.98 for model based iterative reconstruction, respectively. Sequential review of coronal images changed the final stone reading in 13% of cases and improved diagnostic confidence in 49%. CONCLUSIONS: At a 4 mm renal calculus size threshold ultralow dose computerized tomography is accurate for detection when referenced against routine low dose series with dose reduction to below the level of a typical 2-view plain x-ray of the kidneys, ureters and bladder. Slight differences were seen among the reconstruction algorithms. There was mild improvement with model based iterative reconstruction over filtered back projection and adaptive statistical iterative reconstruction. Coronal images improved detection and diagnostic confidence over axial images alone.

Grating based x-ray differential phase contrast imaging without mechanical phase stepping.

Grating-based x-ray differential phase contrast imaging (DPCI) often uses a phase stepping procedure to acquire data that enables the extraction of phase information. This method prolongs the time needed for data acquisition by several times compared with conventional x-ray absorption image acquisitions. A novel analyzer grating design was developed in this work to eliminate the additional data acquisition time needed to perform phase stepping in DPCI. The new analyzer grating was fabricated such that the linear grating structures are shifted from one detector row to the next; the amount of the lateral shift was equal to a fraction of the x-ray diffraction fringe pattern. The x-ray data from several neighboring detector rows were then combined to extract differential phase information. Initial experimental results have demonstrated that the new analyzer grating enables accurate DPCI signal acquisition from a single x-ray exposure like conventional x-ray absorption imaging.

Deep-Interior: A new pathway to interior tomographic image reconstruction via a weighted backprojection and deep learning.

BACKGROUND: In recent years, deep learning strategies have been combined with either the filtered backprojection or iterative methods or the direct projection-to-image by deep learning only to reconstruct images. Some of these methods can be applied to address the interior reconstruction problems for centered regions of interest (ROIs) with fixed sizes. Developing a method to enable interior tomography with arbitrarily located ROIs with nearly arbitrary ROI sizes inside a scanning field of view (FOV) remains an open question. PURPOSE: To develop a new pathway to enable interior tomographic reconstruction for arbitrarily located ROIs with arbitrary sizes using a single trained deep neural network model. METHODS: The method consists of two steps. First, an analytical weighted backprojection reconstruction algorithm was developed to perform domain transform from divergent fan-beam projection data to an intermediate image feature space, B ( x ⃗ ) $B(\vec{x})$ , for an arbitrary size ROI at an arbitrary location inside the FOV. Second, a supervised learning technique was developed to train a deep neural network architecture to perform deconvolution to obtain the true image f ( x ⃗ ) $f(\vec{x})$ from the new feature space B ( x ⃗ ) $B(\vec{x})$ . This two-step method is referred to as Deep-Interior for convenience. Both numerical simulations and experimental studies were performed to validate the proposed Deep-Interior method. RESULTS: The results showed that ROIs as small as a diameter of 5 cm could be accurately reconstructed (similarity index 0.985 ± 0.018 on internal testing data and 0.940 ± 0.025 on external testing data) at arbitrary locations within an imaging object covering a wide variety of anatomical structures of different body parts. Besides, ROIs of arbitrary size can be reconstructed by stitching small ROIs without additional training. CONCLUSION: The developed Deep-Interior framework can enable interior tomographic reconstruction from divergent fan-beam projections for short-scan and super-short-scan acquisitions for small ROIs (with a diameter larger than 5 cm) at an arbitrary location inside the scanning FOV with high quantitative reconstruction accuracy.

Experimental measurement of local noise power spectrum (NPS) in photon counting detector-CT (PCD-CT) using a single data acquisition.

BACKGROUND: Accurate noise power spectra (NPS) measurement in clinical X-ray CT exams is challenging due to the need for repeated scans, which expose patients to high radiation risks. A reliable method for single CT acquisition NPS estimation is thus highly desirable. PURPOSE: To develop a method for estimating local NPS from a single photon counting detector-CT (PCD-CT) acquisition. METHODS: A novel nearly statistical bias-free estimator was constructed from the raw counts data of PCD-CT scan to estimate the variance of sinogram projection data. An analytical algorithm is employed to reconstruct point-wise covariance cov ( x i , x j ) $\text{cov}({\bf x}_i,{\bf x}_j)$ between any two image pixel/voxel locations x i ${\bf x}_i$ and x j ${\bf x_j}$ . A Fourier transform is applied to obtain the desired point-wise NPS for any chosen location x i ${\bf x}_i$ . The method was validated using experimental data acquired from a benchtop PCD-CT system with various physical phantoms, and the results were compared with the conventional local NPS measurement method using repeated scans and statistical ensemble averaging. RESULTS: The experimental results demonstrate that (1) the proposed method can achieve pointwise/local NPS measurement for a region of interest (ROI) located at any chosen position, accurately characterizing the NPS with spatial structures resulting from image content heterogeneity; (2) the local NPS measured using the proposed method show a higher precision in the measured NPS compared to the conventional measurement method; (3) spatial averaging of the local NPS yields the conventional NPS for a given local ROI. CONCLUSION: A new method was developed to enable local NPS from a single PCD-CT acquisition.

Noise power spectrum (NPS) in computed tomography: Enabling local NPS measurement without stationarity and ergodicity assumptions.

BACKGROUND: Conventional methods for estimating the noise power spectrum (NPS) often necessitate multiple computed tomography (CT) data acquisitions and are required to satisfy stringent stationarity and ergodicity conditions, which prove challenging in CT imaging systems. PURPOSE: The aim was to revisit the conventional NPS estimation method, leading to a new framework that estimates local NPS without relying on stationarity or ergodicity, thus facilitating experimental NPS estimations. METHODS: The scientific foundation of the conventional CT NPS measurement method, based on the Wiener-Khintchine theorem, was reexamined, emphasizing the critical conditions of stationarity and ergodicity. This work proposes an alternative framework, characterized by its independence from stationarity and ergodicity, and its ability to facilitate local NPS estimations. A spatial average of local NPS over a Region of Interest (ROI) yields the conventional NPS for that ROI. The connections and differences between the proposed alternative method and the conventional method are discussed. Experimental studies were conducted to validate the new method. RESULTS: (1) The NPS estimated using the conventional method was demonstrated to correspond to the spatial average of pointwise NPS from the proposed NPS estimation framework. (2) The NPS estimated over an ROI with the conventional method was shown to be the sum of the NPS estimated from the proposed method and a contribution from measurement uncertainty. (3) Local NPS estimations from the proposed method in this work elucidate the impact of surrounding image content on local NPS variations. CONCLUSION: The NPS estimation method proposed in this work allows for the estimation of local NPS without relying on stationarity and ergodicity conditions, offering local NPS estimations with significantly improved precision.

Approaches, advantages, and challenges to photon counting detector and multi-energy CT.

Photon counting detector CT (PCD-CT) is the newest major development in CT technology and has been commercially available since 2021. It offers major technological advantages over current standard-of-care energy integrating detector CT (EID-CT) including improved spatial resolution, improved iodine contrast to noise ratio, multi-energy imaging, and reduced noise. This article serves as a foundational basis to the technical approaches and concepts of PCD-CT technology with primary emphasis on detector technology in direct comparison to EID-CT. The article also addresses current technological challenges to PCD-CT with particular attention to cross talk and its causes (e.g., Compton scattering, fluorescence, charge sharing, K-escape) as well as pile-up.

Correcting statistical CT number biases without access to raw detector counts: Applications to high spatial resolution photon counting CT imaging.

BACKGROUND: Due to the nonlinear nature of the logarithmic operation and the stochastic nature of photon counts (N), sinogram data of photon counting detector CT (PCD-CT) are intrinsically biased, which leads to statistical CT number biases. When raw counts are available, nearly unbiased statistical estimators for projection data were developed recently to address the CT number bias issue. However, for most clinical PCD-CT systems, users' access to raw detector counts is limited. Therefore, it remains a challenge for end users to address the CT number bias issue in clinical applications. PURPOSE: To develop methods to correct statistical biases in PCD-CT without requiring access to raw PCD counts. METHODS: (1) The sample variance of air-only post-log sinograms was used to estimate air-only detector counts, N ¯ 0 $\bar{N}_0$ . (2) If the post-log sinogram data, y, is available, then N of each detector pixel was estimated using N = N ¯ 0 e - y $N = \bar{N}_0 \, \mathrm{e}^{-y}$ . Once N was estimated, a closed-form analytical bias correction was applied to the sinogram. (3) If a patient's post-log sinogram data are not archived, a forward projection of the bias-contaminated CT image was used to perform a first-order bias correction. Both the proposed sinogram domain- and image domain-based bias correction methods were validated using experimental PCD-CT data. RESULTS: Experimental results demonstrated that both sinogram domain- and image domain-based bias correction methods enabled reduced-dose PCD-CT images to match the CT numbers of reference-standard images within [-5, 5] HU. In contrast, uncorrected reduced-dose PCD-CT images demonstrated biases ranging from -25 to 55 HU, depending on the material. No increase in image noise or spatial resolution degradation was observed using the proposed methods. CONCLUSIONS: CT number bias issues can be effectively addressed using the proposed sinogram or image domain method in PCD-CT, allowing PCD-CT acquired at different radiation dose levels to have consistent CT numbers desired for quantitative imaging.

Unbiased zero-count correction method in low-dose high-resolution photon counting detector CT.

Objective.To address the zero-count problem in low-dose, high-spatial-resolution photon counting detector CT (PCD-CT) without introducing statistical biases or degrading spatial resolution.Approach.The classical approach to generate the sinogram projection data for estimating the line integrals of the linear attenuation coefficients of the image object is to take a log transform of detector counts, which requires zero counts to be replaced by positive numbers. Both the log transform and the zero-count replacement introduce biases. After analyzing the statistical properties of the zero-count replaced pre-log and post-log data, a formula for the statistical sinogram bias was derived, based on which a new sinogram estimator was empirically constructed to cancel the statistical biases. Dose- and object-independent free parameters in the proposed estimator were learned from simulated data, and then the estimator was applied to experimental low-dose PCD-CT data of physical phantoms for validation and generalizability testing. Both bias and noise performances of the proposed method were evaluated and compared with those of previous zero-count correction methods, including zero-weighting, zero-replacement, and adaptive filtration-based methods. The impact of these correction methods on spatial resolution was also quantified using line-pair patterns.Main Results.For all objects and reduced-dose levels, the proposed method reduces the statistical CT number biases to be within ± 10 HU, which is significantly lower than the biases given by the classical zero-count correction methods. The Bland-Altman analysis demonstrated that the proposed correction led to negligible sinogram biases at all attenuation levels, whereas the other correction methods did not. Additionally, the proposed method was found to have no discernible impact on image noise and spatial resolution.Significance.The proposed zero-count correction scheme allows the CT numbers of low-dose, high-spatial-resolution PCD-CT images to match those of standard-dose and standard-resolution PCD-CT images.

Training certified detectives to track down the intrinsic shortcuts in COVID-19 chest x-ray data sets.

Deep learning faces a significant challenge wherein the trained models often underperform when used with external test data sets. This issue has been attributed to spurious correlations between irrelevant features in the input data and corresponding labels. This study uses the classification of COVID-19 from chest x-ray radiographs as an example to demonstrate that the image contrast and sharpness, which are characteristics of a chest radiograph dependent on data acquisition systems and imaging parameters, can be intrinsic shortcuts that impair the model's generalizability. The study proposes training certified shortcut detective models that meet a set of qualification criteria which can then identify these intrinsic shortcuts in a curated data set.

Training certified detectives to track down the intrinsic shortcuts in COVID-19 chest x-ray data sets.

Deep learning faces a significant challenge wherein the trained models often underperform when used with external test data sets. This issue has been attributed to spurious correlations between irrelevant features in the input data and corresponding labels. This study uses the classification of COVID-19 from chest x-ray radiographs as an example to demonstrate that the image contrast and sharpness, which are characteristics of a chest radiograph dependent on data acquisition systems and imaging parameters, can be intrinsic shortcuts that impair the model's generalizability. The study proposes training certified shortcut detective models that meet a set of qualification criteria which can then identify these intrinsic shortcuts in a curated data set.