A methodology for incorporating a photon-counting CT system into routine clinical use.

Photon-counting computed tomography (PCCT) systems are increasingly available in the U.S. following Food and Drug Administration (FDA) approval of the first clinical PCCT system in Fall 2021. Consequently, there will be a need to incorporate PCCTs into existing fleets of traditional CT systems. The commissioning process of a PCCT was devised by evaluating the degree of agreement between the performance of the PCCT and that of established clinical CT systems. A PCCT system (Siemens NAEOTOM Alpha) was evaluated using the American College of Radiology(ACR) CT phantom (Gammex 464). The phantom was scanned on the system and on a 3rd Generation EID CT system (Siemens Force) at three clinical dose levels. Images were reconstructed across the range of available reconstruction kernels and Iterative Reconstruction (IR) strengths. Two image quality metrics-spatial resolution and noise texture-were calculated using AAPM TG233 software (imQuest), as well as a dose metric to achieve target image noise magnitude of 10 HU. For each pair of EID-PCCT kernel/IR strengths, the difference in metrics were calculated, weighted, and multiplied over all metrics to determine the concordance between systems. IR performance was characterized by comparing relative noise texture and reference dose as a function of IR strength for each system. In general, as kernel "sharpness" increased for each system, spatial resolution, noise spatial frequency, and reference dose increased. For a given kernel, EID reconstruction showed higher spatial resolution compared to PCCT in standard resolution mode. PCCT implementation of IR better preserved noise texture across all strengths compared to the EID, demonstrated by respective 20 and 7% shifts in noise texture from IR "Off" to IR "Max." Overall, the closest match for a given EID reconstruction kernel/IR strength was identified as a PCCT kernel with "sharpness" increased by 1 step and IR strength increased by 1-2 steps. Substantial dose reduction potential of up to 70% was found when targeting a constant noise magnitude.

Improving image quality by optimizing beam width and helical pitch in CT.

BACKGROUND: Expanding computed tomography (CT) detector coverage broadens the beam width, but inaccurate tube current application can reduce image quality at the boundaries between body regions with different attenuation values along the z-axis. OBJECTIVE: This study aims to develop and validate a new CT scanning technique with a fixed pitch to achieve higher imaging quality. METHODS: A cylindrical water phantom and an anthropomorphic chest phantom with different diameters represent a human body with different attenuation values. By optimizing the beam width and helical pitch, the pitch is fixed during scanning. The mean noise of the images and the standard deviation were calculated, and the coefficient of variation (COV) was compared to evaluate the uniformity of image noise according to the beam width. RESULTS: At the boundaries between regions with different attenuation values, the 10 mm beam width (COV: 0.065) in the water phantom showed a 47.7% COV reduction of image noise compared with the 20 mm beam width (COV: 0.125). In addition, the 20 mm beam width (COV: 0.146) in the chest phantom showed a 29.3% COV reduction of image noise compared with the 40 mm beam width (COV: 0.206). Thus, as the beam was narrowed, the mean noise was similar, but the standard deviation was reduced. CONCLUSIONS: The proposed CT scanning technique with a fixed pitch, optimized beam width, and helical pitch demonstrates that image quality can be improved without increasing radiation dose at the boundary between regions with different attenuation values.

CT radiation dose and iterative reconstruction techniques.

1. CT radiation dose optimization is one of the major concerns for the scientific community. 2. CT image quality is dependent on the selected image reconstruction algorithm. 3. Iterative reconstruction algorithms have reemerged with the potential of radiation dose optimization by lowering image noise. 4. Tube current is the most common parameter used to reduce radiation dose along with iterative reconstruction. 5. Tube potential (kV) is also used for dose optimization with iterative reconstruction in CT angiography protocols and small patients.

CT chest and gantry rotation time: does the rotation time influence image quality?

BACKGROUND: Computed tomography (CT) gantry rotation time is one factor influencing image quality. Until now, there has been no report investigating the influence of gantry rotation time on chest CT image quality. PURPOSE: To investigate the influence of faster gantry rotation time on image quality and subjective and objective image parameters in chest CT imaging. MATERIAL AND METHODS: Chest CT scans from 160 patients were examined in this study. All scans were performed using a single-source mode (collimation, 128 × 0.6 mm; pitch, 1.2) on a dual-source CT scanner. Only gantry rotation time was modified, while other CT parameters were kept stable for each scan (120 kV/110 reference mAs). Patients were divided into four groups based on rotation time: group 1, 1 s/ rotation (rot); group 2, 0.5 s/rot; group 3, 0.33 s/rot; group 4, 0.28 s/rot. Two blinded radiologists subjectively compared CT image quality, noise, and artifacts, as well as radiation exposure, from all groups. For objective comparison, all image datasets were analyzed by a radiologist with 5 years of experience concerning objective measurements as well as signal-to-noise ratio (SNR). RESULTS: We found that faster gantry rotation times (0.28 s/rot and 0.33 s/rot) resulted in more streak artifacts, image noise, and decreased image quality. However, there was no significant difference in radiation exposure between faster and slower rotation times (P > 0.7). CONCLUSION: Faster CT gantry rotation reduces scan time and motion artifacts. However, accelerating rotation time increases image noise and streak artifacts. Therefore, a slower CT gantry rotation speed is still recommended for higher image quality in some cases.

Calibration phantom-based prediction of CT lung nodule volume measurement performance.

Background: A calibration phantom-based method has been developed for predicting small lung nodule volume measurement bias and precision that is specific to a particular computed tomography (CT) scanner and acquisition protocol. Methods: The approach involves CT scanning a simple reference object with a specific acquisition protocol, analyzing the scan to estimate the fundamental imaging properties of the CT acquisition system, generating numerous simulated images of a target geometry using the fundamental imaging properties, measuring the simulated images with a standard nodule volume segmentation algorithm, and calculating bias and precision performance statistics from the resulting volume measurements. We evaluated the ability of this approach to predict volume measurement bias and precision of Teflon spheres (diameters =4.76, 6.36, and 7.94 mm) placed within an anthropomorphic chest phantom when using 3M Scotch Magic™ tape as the reference object. CT scanning of the spheres was performed with 0.625, 1.25, and 2.5 mm slice thickness and spacing. Results: The study demonstrated good agreement between predicted volumetric performance and observed volume measurement performance for both volumetric measurement bias and precision. The predicted and observed volume mean for all slice thicknesses was found to be 28% and 13% lower on average than the manufactured sphere volume, respectively. When restricted to 0.625 and 1.25 mm slice thickness scans, which are recommended for small lung nodule volume measurement, we found that the difference between predicted and observed volume coefficient of variation was less than 1.0 %. The approach also showed a resilience to varying CT image acquisition protocols, a critical capability when deploying in a real-world clinical setting. Conclusions: This is the first report of a calibration phantom-based method's ability to predict both small lung nodule volume measurement bias and precision. Volume measurement bias and precision for small lung nodules can be predicted using simple low-cost reference objects to estimate fundamental CT image characteristics and modeling and simulation techniques. The approach demonstrates an improved method for predicting task specific, clinically relevant measurement performance using advanced and fully automated image analysis techniques and low-cost reference objects.

Clinical Low-Dose Photon-Counting CT for the Detection of Urolithiasis: Radiation Dose Reduction Is Possible without Compromising Image Quality.

Background: This study evaluated the feasibility of reducing the radiation dose in abdominal imaging of urolithiasis with a clinical photon-counting CT (PCCT) by gradually lowering the image quality level (IQL) without compromising the image quality and diagnostic value. Methods: Ninety-eight PCCT examinations using either IQL70 (n = 31), IQL60 (n = 31) or IQL50 (n = 36) were retrospectively included. Parameters for the radiation dose and the quantitative image quality were analyzed. Qualitative image quality, presence of urolithiasis and diagnostic confidence were rated. Results: Lowering the IQL from 70 to 50 led to a significant decrease (22.8%) in the size-specific dose estimate (SSDE, IQL70 4.57 ± 0.84 mGy, IQL50 3.53 ± 0.70 mGy, p < 0.001). Simultaneously, lowering the IQL led to a minimal deterioration of the quantitative quality, e.g., image noise increased from 9.13 ± 1.99 (IQL70) to 9.91 ± 1.77 (IQL50, p = 0.248). Radiologists did not notice major changes in the image quality throughout the IQLs. Detection rates of urolithiasis (91.3-100%) did not differ markedly. Diagnostic confidence was high and not influenced by the IQL. Conclusions: Adjusting the PCCT scan protocol by lowering the IQL can significantly reduce the radiation dose without significant impairment of the image quality. The detection rate and diagnostic confidence are not impaired by using an ultra-low-dose PCCT scan protocol.

Evaluating the effects of variation in CT scanning parameters on the image quality and Hounsfield units for optimization of dose in radiotherapy treatment planning: A semi-anthropomorphic thorax phantom study.

Aim: The diagnosis accuracy of computed tomography (CT) systems and the reliability of calculated Hounsfield Units (HUs) are critical in tumor detection and cancer patients' treatment planning. This study evaluated the effects of scan parameters (Kilovoltage peak or kVp, milli-Ampere-second or mAS reconstruction kernels and algorithms, reconstruction field of view, and slice thickness) on image quality, HUs, and the calculated dose in the treatment planning system (TPS). Materials and Methods: A quality dose verification phantom was scanned several times by a 16-slice Siemens CT scanner. The DOSIsoft ISO gray TPS was applied for dose calculations. The SPSS.24 software was used to analyze the results and the P-value <0.05 was considered significant. Results: Reconstruction kernels and algorithms significantly affected noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). The noise increased and CNR decreased by raising the sharpness of reconstruction kernels. SNR and CNR had considerable increments at iterative reconstruction compared with the filtered back-projection algorithm. The noise decreased by raising mAS in soft tissues. Also, KVp had a significant effect on HUs. TPS--calculated dose variations were less than 2% for mediastinum and backbone and less than 8% for rib. Conclusions: Although HU variation depends on image acquisition parameters across a clinically feasible range, its dosimetric impact on the calculated dose in TPS can be neglected. Hence, it can be concluded that the optimized values of scan parameters can be applied to obtain the maximum diagnostic accuracy and calculate HUs more precisely without affecting the calculated dose in the treatment planning of cancer patients.

A C-arm photon counting CT prototype with volumetric coverage using multi-sweep step-and-shoot acquisitions.

Objective.Existing clinical C-arm interventional systems use scintillator-based energy-integrating flat panel detectors (FPDs) to generate cone-beam CT (CBCT) images. Despite its volumetric coverage, FPD-CBCT does not provide sufficient low-contrast detectability desired for certain interventional procedures. The purpose of this work was to develop a C-arm photon counting detector (PCD) CT system with a step-and-shoot data acquisition method to further improve the tomographic imaging performance of interventional systems.Approach.As a proof-of-concept, a cadmium telluride-based 51 cm × 0.6 cm PCD was mounted in front of a FPD in an Artis Zee biplane system. A total of 10 C-arm sweeps (5 forward and 5 backward) were prescribed. A motorized patient table prototype was synchronized with the C-arm system such that it translates the object by a designated distance during the sub-second rest time in between gantry sweeps. To evaluate whether this multi-sweep step-and-shoot acquisition strategy can generate high-quality and volumetric PCD-CT images without geometric distortion artifacts, experiments were performed using physical phantoms, a human cadaver head, and anin vivoswine subject. Comparison with FPD-CT was made under matched narrow beam collimation and radiation dose conditions.Main results.Compared with FPD-CT images, PCD-CT images had lower noise and improved visualization of low-contrast lesion models, as well as improved visibility of small iodinated blood vessels. Fine structures were visualized more clearly by the PCD-CT than the highest-available resolution provided by FPD-CBCT and MDCT. No perceivable geometric distortion artifacts were observed in the multi-planar PCD-CT images.Significance.This work is the first demonstration of the feasibility of high-quality and multi-planar (volumetric) PCD-CT imaging with a rotating C-arm gantry.

Feasibility of a method for low contrast CT image quality assessment using difference detail curves for abdominal scans.

This work describes a measurement method for assessing dose-related image-quality of CT scans based on the difference detail curve (DDC) method, and showcases its use in a low contrast setting. The method is based on a phantom consisting of elliptical slices of different sizes into which contrast object modules can be inserted. These modules contain contrast objects based on (synthetic) resin mixtures with sucrose (native) or sodium iodine (contrast medium). Mixing ratios are provided to achieve a range of clinically relevant CT-numbers with these materials. The phantom is characterized in terms of contrast accuracy, energy dependency and long-term drift with satisfying results. Contrast accuracy and energy dependency are similar to that of water or soft tissue. Image quality of 655 scans of the phantom acquired at 30 different clinical institutions and with 16 different CT scanner models from 4 manufacturers was assessed by calculating a difference detail curve (DDC) from evaluation of up to 5 human observers using a custom-made software (RadiVates) described in this work. Based on these measurements, inter-observer variability was quantified using a bootstrap method and was shown to be a large contributor to the overall variability. This work demonstrates that assessment of CT image quality is feasible with the aforementioned phantom and DDC method.

Stereolithography (STL) measurement rubric for the evaluation of craniomaxillofacial STLs.

BACKGROUND: Facial deformities often demand reconstructive surgery and the placement of three-dimensional (3D) printed craniomaxillofacial prostheses. Prostheses manufacturing requires patients' computed tomography (CT) images. Poor quality images result in incorrectly sized prostheses, necessitating repeat imaging and refitting. The Centre for Rapid Prototyping and Manufacturing (CRPM) produces most facial prostheses in South Africa but does not have a prescribed optimised CT protocol. Therefore, this study was undertaken. METHODS: A collection of CRPM STLs used in the design and manufacturing of craniomaxillofacial prostheses is available. The image quality of stereolithography (STL) files of CRPM CT scans was evaluated to determine what constitutes good image quality. This collection was scrutinised for inclusion in the image quality evaluation. After scrutiny, 35 STLs of individuals ≥15 years of age were selected and included metadata attached to the DICOM file. Furthermore, only STLs created without manipulation by the same designer were included in the collection. Before the qualitative evaluation of the STLs, eight different critical anatomical reference points (CARPs) were identified with the assistance of an expert team. A visual acuity rating scale of three categories was devised for each CARP, where 1 was allocated to poor visual acuity, 2 to partial, and 3 to good visual acuity. Similarly, rating scales were devised for the presence of concentric rings and the overall impression score awarded by the two designers involved in the design and manufacturing of the prostheses. This stereolithography measurement rubric (SMR) was then applied to the 35 STLs by a team of three experts, including the two designers, during a structured evaluation session. The scores were used to calculate summary and inferential statistics. RESULTS: Scores grouped around the central rating of partial visual acuity. The three evaluators' mean total CARP scores ranged from 13.1 to 14.4 (maximum possible score 24), while the mean total CARP + ring scores ranged from 15.8 to 17.1 (maximum possible score 27). No significant differences were detected between the evaluators' scores. CONCLUSION: This SMR appears to be the first of its kind. This image quality assessment of STLs provides the groundwork for finer CT image quality evaluation to formulate a CT imaging protocol for the CRPM to design and manufacture accurate internal cranial prostheses.

Clinical Low Dose Photon Counting CT for the Detection of Urolithiasis: Evaluation of Image Quality and Radiation Dose.

The purpose of this study was the evaluation of image quality and radiation dose parameters of the novel photon counting CT (PCCT, Naeotom Alpha, Siemens Healthineers) using low-dose scan protocols for the detection of urolithiasis. Standard CT scans were used as a reference (S40, Somatom Sensation 40, Siemens Healthineers). Sixty-three patients, who underwent CT scans between August and December 2021, were retrospectively enrolled. Thirty-one patients were examined with the PCCT and 32 patients were examined with the S40. Radiation dose parameters, as well as quantitative and qualitative image parameters, were analyzed. The presence of urolithiasis, image quality, and diagnostic certainty were rated on a 5-point-scale by 3 blinded readers. Both patient groups (PCCT and S40) did not differ significantly in terms of body mass index. Radiation dose was significantly lower for examinations with the PCCT compared to the S40 (2.4 ± 1.0 mSv vs. 3.4 ± 1.0 mSv; p < 0.001). The SNR was significantly better on images acquired with the PCCT (13.3 ± 3.3 vs. 8.2 ± 1.9; p < 0.001). The image quality of the PCCT was rated significantly better (4.3 ± 0.7 vs. 2.8 ± 0.6; p < 0.001). The detection rate of kidney or ureter calculi was excellent with both CT scanners (PCCT 97.8% and S40 99%, p = 0.611). In high contrast imaging, such as the depiction of stones of the kidney and the ureter, PCCT allows a significant reduction of radiation dose, while maintaining excellent diagnostic confidence and image quality. Given this image quality with our current protocol, further adjustments towards ultra-low-dose CT scans appear feasible.

Multiresolution residual deep neural network for improving pelvic CBCT image quality.

PURPOSE: Cone-beam computed tomography (CBCT) is frequently used for accurate image-guided radiation therapy. However, the poor CBCT image quality prevents its further clinical use. Thus, it is important to improve the HU accuracy and structure preservation of CBCT images. METHODS: In this study, we proposed a novel method to generate synthetic CT (sCT) images from CBCT images. A multiresolution residual deep neural network (RDNN) was adopted for image regression from CBCT images to planning CT (pCT) images. At the coarse level, RDNN was first trained with a large amount of lower resolution images, which can make the network focus on coarse information and prevent overfitting problems. More fine information was obtained gradually by fine-tuning the coarse model using fewer number of higher resolution images. Our model was optimized by using aligned pCT and CBCT image pairs of a particular body region of 153 prostate cancer patients treated in our hospital (120 for training and 33 for testing). Five-fold cross-validation was used to tune the hyperparameters and the testing data were used to evaluate the performance of the final models. RESULTS: The mean absolute error (MAE) between CBCT and pCT on the testing data was 352.56 HU, while the MAE between the sCT and pCT images was 52.18 HU for our proposed multiresolution RDNN model, which reduced the MAE by 85.20% (p < 0.01). In addition, the average structural similarity index measure between the sCT and CBCT was 19.64% (p = 0.01) higher than that of pCT and CBCT. CONCLUSIONS: The sCT images generated using our proposed multiresolution RDNN have higher HU accuracy and structural fidelity, which may promote the further applications of CBCT images in the clinic for structure segmentation, dose calculation, and adaptive radiotherapy planning.

A signal detection model for quantifying overregularization in nonlinear image reconstruction.

Many useful image quality metrics for evaluating linear image reconstruction techniques do not apply to or are difficult to interpret for nonlinear image reconstruction. The vast majority of metrics employed for evaluating nonlinear image reconstruction are based on some form of global image fidelity, such as image root mean square error (RMSE). Use of such metrics can lead to overregularization in the sense that they can favor removal of subtle details in the image. To address this shortcoming, we develop an image quality metric based on signal detection that serves as a surrogate to the qualitative loss of fine image details. The metric is demonstrated in the context of a breast CT simulation, where different equal-dose configurations are considered. The configurations differ in the number of projections acquired. Image reconstruction is performed with a nonlinear algorithm based on total variation constrained least-squares (TV-LSQ). The resulting images are studied as a function of three parameters: number of views acquired, total variation constraint value, and number of iterations. The images are evaluated visually, with image RMSE, and with the proposed signal-detection-based metric. The latter uses a small signal, and computes detectability in the sinogram and in the reconstructed image. Loss of signal detectability through the image reconstruction process is taken as a quantitative measure of loss of fine details in the image. Loss of signal detectability is seen to correlate well with the blocky or patchy appearance due to overregularization with TV-LSQ, and this trend runs counter to the image RMSE metric, which tends to favor the over-regularized images. The proposed signal detection-based metric provides an image quality assessment that is complimentary to that of image RMSE. Using the two metrics in concert may yield a useful prescription for determining CT algorithm and configuration parameters when nonlinear image reconstruction is used.

An experimental method to correct low-frequency concentric artifacts in photon counting CT.

Large-area photon counting detectors (PCDs) are usually built by tiling multiple semiconductor panels that often have slightly different spectral responses to input x-rays. As a result of this spectral inconsistency, experimental PCD-CT images of large, human-sized objects may show high-frequency ring artifacts and low-frequency band artifacts. Due to the much larger width of the bands compared with the rings, the concentric artifact problem in PCD-CT images of human-sized objects cannot be adequately addressed by conventional CT ring correction methods. This work presents an experimental method to correct the concentric artifacts in PCD-CT. The method is applicable to not only energy-discriminating PCDs with multiple bins but also PCDs with only a single threshold controller. Its principle is similar to the two-step beam hardening correction method, except that the proposed method uses pixel-specific polynomial functions to address the spectral inconsistency problem across the detector plane. The pixel-specific polynomial coefficients were experimentally calibrated using 15 acrylic sheets and 6 aluminum sheets of known thicknesses. The pixel-specific polynomial functions were used to convert the measured PCD-CT projection data to acrylic- and aluminum-equivalent thicknesses that are energy-independent. The proposed method was experimentally evaluated using a human cadaver head and multiple physical phantoms: two of them contain iodine and one phantom contains dual K-edge contrast materials (gadolinium and iodine). The results show that the proposed method can effectively remove the low-frequency concentric artifacts in PCD-CT images while reducing beam hardening artifacts. In contrast, the conventional CT ring correction algorithm did not adequately address the low-frequency band artifacts. Compared with the direct material decomposition-based correction method, the proposed method not only relaxes the requirement of multi-energy bins but also generates images with lower noise and fewer concentric artifacts.

[CT image quality assessment based on prior information of pre-restored images].

OBJECTIVE: We propose a CT IQA strategy based on the prior information of pre-restored images (PR-IQA) to improve the performance of IQA models. OBJECTIVE: We propose a CNN-based no-reference CT IQA strategy using the prior information of image quality features in the image restoration algorithm, which is combined with the original distorted image information into the two CNNs through the pre-restored image and the residual image. Multi-information fusion was used to improve the feature extraction ability and prediction performance of CNN. We built a CT IQA dataset based on spiral CT data published by Mayo Clinic. The performance of PR- IQA was evaluated by calculating the quantitative metrics and statistical tests. The influence of different hyperparameter settings for PR-IQA was analyzed. We then compared PR-IQA with the BASELINE model based on the single CNN to evaluate the original distorted image without reference image and other eight IQA algorithms. OBJECTIVE: The comparative experiment results showed that the PR-IQA model based on the prior information of 3 different image restoration algorithms (BF, NLM and BM3D) was better than all the tested IQA algorithms. Compared with the BASELINE method, the proposed method showed significantly improved performance, and the mean PLCC was increased by 12.56% and SROCC by 19.95%, and RMSE was decreased by 22.77%. OBJECTIVE: The proposed PR-IQA method can make full use of the prior information of the image restoration algorithm to effectively predict the quality of CT images.

The effect of megavoltage field size on intrafraction cone-beam CT image quality.

To investigate the effects of scatter from a megavoltage treatment beam on intrafraction cone beam CT (CBCT) image quality. The effects of treatment beam field size and phantom geometry were investigated as well as the clinical success of IFI. Intrafraction imaging (IFI) was performed on four phantoms with four different MV field sizes using a 6 MV FFF source. The image quality of the intrafraction CBCT images was compared to that of a baseline CBCT (i.e. with no treatment beam on) and quantified using noise and low contrast visibility. Increasing the kV tube current was explored as a possible method to reduce noise induced by the MV photon scatter in the intrafraction-CBCTs. The clinical success of all IFI patients over a 2 month period was reviewed. Intrafraction-CBCT image quality and low-contrast visibility deteriorated as MV field size increased. The extent of image degradation was found to depend on the mass of the phantom resulting in a more pronounced effect for a pelvic phantom than a thoracic phantom. While increasing the tube current could reduce the noise in the intrafraction-CBCT images, increasing the current by a factor of 4 failed to reach baseline image quality. Anatomy was found to be the primary indication of clinical IFI failure with all observed failures occurring during abdominal treatments. Image quality was found to decrease with increasing MV field size and decrease with increasing treatment anatomy mass. When considering intrafraction imaging clinically, the primary indicator of IFI failure is treatment anatomy. IFI can be used during chest treatments with high success rates but care must be taken for abdominal treatments and failures should be expected.

Perturbation Response of Model-based Material Decomposition with Edge-Preserving Penalties.

Spectral CT permits material discrimination beyond the structural information in conventional single-energy CT. Model-based material decomposition facilitates direct estimation of material density from spectral measurements, incorporating a general forward model for arbitrary spectral CT system, a statistical model of spectral CT measurements, and flexible regularization schemes. Such one-step approaches are promising for superior image quality, but the relationship between regularization parameters, imaging conditions, and reconstructed image properties is complicated. More specifically, the estimator is inherently nonlinear and may include additional nonlinearities like edge-preserving regularization, making image quality metrics intended for linear system evaluation difficult to apply. In this work, we seek approaches to quantify the image properties of this inherently nonlinear process through an investigation of perturbation response - the generalized system response to a local perturbation of arbitrary shape, location, and contrast. Such responses include cross-talk between material density channels, and we investigate the application of this metric in a sample spectral CT system. Inspired by the prior work under assumptions of local linearity and shift-invariant we also propose a prediction framework for perturbation response using a perceptron neural network. The proposed prediction framework offers an alternative to exhaustive evaluation and is a potential tool that can be used to prospectively choose optimal regularization parameters based on imaging conditions and diagnostic task.

[Possibilities of dose reduction during CT imaging by application of iterative image reconstruction]. / A dóziscsökkentés lehetoségei CT-képalkotás során az iteratív képrekonstrukció alkalmazásával.

Introduction and aim: In case of imaging modalities using ionizing radiation, radiation exposure of the patients is a vital issue. It is important to survey the various dose-reducing techniques to achieve optimal radiation protection while keeping image quality on an optimal level. Method: We reprocessed 105 patients' data prospectively between February and April 2017. The determination of the radiation dose was based on the effective dose, calculated by multiplying the dose-length product (DLP) and dose-conversation coefficient. In case of image quality we used signal-to-noise ratio (SNR) based on manual segmentation of region of interest (ROI). For statistical analysis, one sample t-test and Wilcoxon signed rank test were used. Results: Using iterative reconstruction, the effective dose was significantly lower (p<0.001) in both native and contrast-enhanced abdominal, contrast-enhanced chest CT scans and in the case of the total effective dose. At native and contrast-enhanced abdominal CT scans, the noise content of the images showed significantly lower (p<0.001) values for iterative reconstruction images. At contrast-enhanced chest CT scans there was no significant difference between the noise content of the images (p>0.05). Conclusion: Using iterative reconstruction, it was possible to achieve significant dose reduction. Since the noise content of the images was not significantly higher using the iterative reconstruction compared to the filtered back projection, further dose reduction can be achievable while preserving the optimal quality of the images. Orv Hetil. 2019; 160(35): 1387-1394.

Validation of algorithmic CT image quality metrics with preferences of radiologists.

PURPOSE: Automated assessment of perceptual image quality on clinical Computed Tomography (CT) data by computer algorithms has the potential to greatly facilitate data-driven monitoring and optimization of CT image acquisition protocols. The application of these techniques in clinical operation requires the knowledge of how the output of the computer algorithms corresponds to clinical expectations. This study addressed the need to validate algorithmic image quality measurements on clinical CT images with preferences of radiologists and determine the clinically acceptable range of algorithmic measurements for abdominal CT examinations. MATERIALS AND METHODS: Algorithmic measurements of image quality metrics (organ HU, noise magnitude, and clarity) were performed on a clinical CT image dataset with supplemental measures of noise power spectrum from phantom images using techniques developed previously. The algorithmic measurements were compared to clinical expectations of image quality in an observer study with seven radiologists. Sets of CT liver images were selected from the dataset where images in the same set varied in terms of one metric at a time. These sets of images were shown via a web interface to one observer at a time. First, the observer rank ordered the CT images in a set according to his/her preference for the varying metric. The observer then selected his/her preferred acceptable range of the metric within the ranked images. The agreement between algorithmic and observer rankings of image quality were investigated and the clinically acceptable image quality in terms of algorithmic measurements were determined. RESULTS: The overall rank-order agreements between algorithmic and observer assessments were 0.90, 0.98, and 1.00 for noise magnitude, liver parenchyma HU, and clarity, respectively. The results indicate a strong agreement between the algorithmic and observer assessments of image quality. Clinically acceptable thresholds (median) of algorithmic metric values were (17.8, 32.6) HU for noise magnitude, (92.1, 131.9) for liver parenchyma HU, and (0.47, 0.52) for clarity. CONCLUSIONS: The observer study results indicated that these algorithms can robustly assess the perceptual quality of clinical CT images in an automated fashion. Clinically acceptable ranges of algorithmic measurements were determined. The correspondence of these image quality assessment algorithms to clinical expectations paves the way toward establishing diagnostic reference levels in terms of clinically acceptable perceptual image quality and data-driven optimization of CT image acquisition protocols.

Statistical properties of cerebral CT perfusion imaging systems. Part I. Cerebral blood volume maps generated from nondeconvolution-based systems.

PURPOSE: The development and clinical employment of a computed tomography (CT) imaging system benefit from a thorough understanding of the statistical properties of the output images; cerebral CT perfusion (CTP) imaging system is no exception. A series of articles will present statistical properties of CTP systems and the dependence of these properties on system parameters. This Part I paper focuses on the signal and noise properties of cerebral blood volume (CBV) maps calculated using a nondeconvolution-based method. METHODS: The CBV imaging chain was decomposed into a cascade of subimaging stages, which facilitated the derivation of analytical models for the probability density function, mean value, and noise variance of CBV. These models directly take CTP source image acquisition, reconstruction, and postprocessing parameters as inputs. Both numerical simulations and in vivo canine experiments were performed to validate these models. RESULTS: The noise variance of CBV is linearly related to the noise variance of source images and is strongly influenced by the noise variance of the baseline images. Uniformly partitioning the total radiation dose budget across all time frames was found to be suboptimal, and an optimal dose partition method was derived to minimize CBV noise. Results of the numerical simulation and animal studies validated the derived statistical properties of CBV. CONCLUSIONS: The statistical properties of CBV imaging systems can be accurately modeled by extending the linear CT systems theory. Based on the statistical model, several key signal and noise characteristics of CBV were identified and an optimal dose partition method was developed to improve the image quality of CBV.