Quantum Iterative Reconstruction for Abdominal Photon-counting Detector CT Improves Image Quality.

Background An iterative reconstruction (IR) algorithm was introduced for clinical photon-counting detector (PCD) CT. Purpose To investigate the image quality and the optimal strength level of a quantum IR algorithm (QIR; Siemens Healthcare) for virtual monoenergetic images and polychromatic images (T3D) in a phantom and in patients undergoing portal venous abdominal PCD CT. Materials and Methods In this retrospective study, noise power spectrum (NPS) was measured in a water-filled phantom. Consecutive oncologic patients who underwent portal venous abdominal PCD CT between March and April 2021 were included. Virtual monoenergetic images at 60 keV and T3D were reconstructed without QIR (QIR-off; reference standard) and with QIR at four levels (QIR 1-4; index tests). Global noise index, contrast-to-noise ratio (CNR), and voxel-wise CT attenuation differences were measured. Noise and texture, artifacts, diagnostic confidence, and overall quality were assessed qualitatively. Conspicuity of hypodense liver lesions was rated by four readers. Parametric (analyses of variance, paired t tests) and nonparametric tests (Friedman, post hoc Wilcoxon signed-rank tests) were used to compare quantitative and qualitative image quality among reconstructions. Results In the phantom, NPS showed unchanged noise texture across reconstructions with maximum spatial frequency differences of 0.01 per millimeter. Fifty patients (mean age, 59 years ± 16 [standard deviation]; 31 women) were included. Global noise index was reduced from QIR-off to QIR-4 by 45% for 60 keV and by 44% for T3D (both, P < .001). CNR of the liver improved from QIR-off to QIR-4 by 74% for 60 keV and by 69% for T3D (both, P < .001). No evidence of difference was found in mean attenuation of fat and liver (P = .79-.84) and on a voxel-wise basis among reconstructions. Qualitatively, QIR-4 outperformed all reconstructions in every category for 60 keV and T3D (P value range, <.001 to .01). All four readers rated QIR-4 superior to other strengths for lesion conspicuity (P value range, <.001 to .04). Conclusion In portal venous abdominal photon-counting detector CT, an iterative reconstruction algorithm (QIR; Siemens Healthcare) at high strength levels improved image quality by reducing noise and improving contrast-to-noise ratio and lesion conspicuity without compromising image texture or CT attenuation values. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Sinitsyn in this issue.

Impact of iterative metal artifact reduction on diagnostic image quality in patients with dental hardware.

Background Metal artifacts often impair diagnostic accuracy in computed tomography (CT) imaging. Therefore, effective and workflow implemented metal artifact reduction algorithms are crucial to gain higher diagnostic image quality in patients with metallic hardware. Purpose To assess the clinical performance of a novel iterative metal artifact reduction (iMAR) algorithm for CT in patients with dental fillings. Material and Methods Thirty consecutive patients scheduled for CT imaging and dental fillings were included in the analysis. All patients underwent CT imaging using a second generation dual-source CT scanner (120 kV single-energy; 100/Sn140 kV in dual-energy, 219 mAs, gantry rotation time 0.28-1/s, collimation 0.6 mm) as part of their clinical work-up. Post-processing included standard kernel (B49) and an iterative MAR algorithm. Image quality and diagnostic value were assessed qualitatively (Likert scale) and quantitatively (HU ± SD) by two reviewers independently. Results All 30 patients were included in the analysis, with equal reconstruction times for iMAR and standard reconstruction (17 s ± 0.5 vs. 19 s ± 0.5; P > 0.05). Visual image quality was significantly higher for iMAR as compared with standard reconstruction (3.8 ± 0.5 vs. 2.6 ± 0.5; P < 0.0001, respectively) and showed improved evaluation of adjacent anatomical structures. Similarly, HU-based measurements of degree of artifacts were significantly lower in the iMAR reconstructions as compared with the standard reconstruction (0.9 ± 1.6 vs. -20 ± 47; P < 0.05, respectively). Conclusion The tested iterative, raw-data based reconstruction MAR algorithm allows for a significant reduction of metal artifacts and improved evaluation of adjacent anatomical structures in the head and neck area in patients with dental hardware.

Initial results of a new generation dual source CT system using only an in-plane comb filter for ultra-high resolution temporal bone imaging.

OBJECTIVES: To prospectively evaluate radiation dose and image quality of a third generation dual-source CT (DSCT) without z-axis filter behind the patient for temporal bone CT. METHODS: Forty-five patients were either examined on a first, second, or third generation DSCT in an ultra-high-resolution (UHR) temporal bone-imaging mode. On the third generation DSCT system, the tighter focal spot of 0.2 mm(2) removes the necessity for an additional z-axis-filter, leading to an improved z-axis radiation dose efficiency. Images of 0.4 mm were reconstructed using standard filtered-back-projection or iterative reconstruction (IR) technique for previous generations of DSCT and a novel IR algorithm for the third generation DSCT. Radiation dose and image quality were compared between the three DSCT systems. RESULTS: The statistically significantly highest subjective and objective image quality was evaluated for the third generation DSCT when compared to the first or second generation DSCT systems (all p < 0.05). Total effective dose was 63%/39% lower for the third generation examination as compared to the first and second generation DSCT. CONCLUSIONS: Temporal bone imaging without z-axis-UHR-filter and a novel third generation IR algorithm allows for significantly higher image quality while lowering effective dose when compared to the first two generations of DSCTs. KEY POINTS: • Omitting the z-axis-filter allows a reduction in radiation dose of 50% • A smaller focal spot of 0.2 mm (2) significantly improves spatial resolution • Ultra-high-resolution temporal-bone-CT helps to gain diagnostic information of the middle/inner ear.

Coronary stent imaging with dual-source CT: assessment of lumen visibility using different convolution kernels and postprocessing filters.

BACKGROUND: Assesment of the coronary arteries after stent placement using coronary computed tomography angiography (CCTA) currently requires reconstruction of images with soft kernels for the assessment of atherosclerotic plaques and dedicated edge enhancing kernels for the evaluation of the stent lumen. PURPOSE: To evaluate a two-dimensional filter tool that provides instant postprocessing of images reconstructed with soft kernels into edge-enhanced images and vice versa and thus may eliminate the need for two separate reconstrcutions for the assessment of coronary artery stents using CCTA. MATERIAL AND METHODS: Twenty stents with a diameter of 3.0 mm placed in a vascular phantom were scanned with a dual-source CT using standard parameters. Images were reconstructed with a soft B30f and an edge-enhancing B46f kernel and postprocessed with the corresponding filter algorithm (F30 for B30f images; F46 for B46f images). The resulting four data-sets were evaluated for lumen visibility, intraluminal attenuation, and image noise by two independent readers. Results were validated in vivo against invasive coronary angiography in data-sets from patients with coronary artery stents. RESULTS: Average intraluminal attenuation was 552.6 HU, 527.3 HU, 207.9 HU, and 267.5 HU for B30f, F30, B46f, and F46 images, respectively (P < 0.0001). Average image noise was 11.3, 10.6, 19.2, and 15.0 HU, respectively (P < 0.0001). The visible stent diameter was significantly higher in the B46f (59.6%) and F46 images (54%) compared to the B30f (48.3%) and F30 (51.5%) images (P < 0.0001). In the patient study, lumen assessability was significantly better in B46f images than in F46 images. Sensitivity for stenosis detection was best in the original B46f images with a sensitivity of 67% and a specificity of 94%. CONCLUSION: The postprocessing filter reduces image noise, however currently it does not offer an alternative to image reconstruction using the edge-enhancing kernels for the evaluation of the stent lumen.

Iterative image reconstruction techniques for CT coronary artery calcium quantification: comparison with traditional filtered back projection in vitro and in vivo.

PURPOSE: To investigate in vitro and in vivo the use of image-based and raw data-based iterative reconstruction algorithms for quantification of coronary artery calcium by using the Agatston score and subsequent cardiac risk stratification. MATERIALS AND METHODS: In vitro data were obtained by using a moving anthropomorphic cardiac phantom containing calcium inserts of different concentrations and sizes. With institutional review board approval and HIPAA compliance, coronary calcium imaging data of 110 consecutive patients (mean age ± standard deviation, 58.2 years ± 9.8; 48 men) were reconstructed with filtered back projection (FBP), iterative reconstruction in image space (IRIS), and sinogram-affirmed iterative reconstruction (SAFIRE). Image noise was measured and the Agatston score was obtained for all reconstructions. Assignment to Agatston scores and percentile-based cardiac risk categories was compared. Statistical analysis included the Cohen κ coefficient and Friedman and Wilcoxon testing. RESULTS: In vitro, mean Agatston scores ± standard deviation for FBP (638.9 ± 9.6), IRIS (622.7 ± 15.2), and SAFIRE (631.4 ± 17.6) were comparable (P = .30). The smallest phantom calcifications were more frequently detected when iterative reconstruction techniques were used. The Agatston scores in the patient cohort were not significantly different among FBP, IRIS, and SAFIRE in paired comparisons (median Agatston score [25th and 75th percentiles]: 76.0 [20.6, 243.9], 76.4 [22, 249.3], and 75.7 [21.5, 49.1], respectively; P = .20 each). Comparison of categorization based on Agatston score percentiles showed excellent agreement for both IRIS and SAFIRE with FBP (κ = 0.975 [0.942-1.00] and κ = 0.963 [0.922-1.00], respectively). The mean effective dose was 1.02 mSv ± 0.51. Mean image noise was significantly (P < .001) higher with FBP than that with iterative reconstructions. CONCLUSION: In comparison with FBP, iterative reconstruction techniques do not have a profound effect on the reproducible quantification of coronary calcium according to Agatston score and subsequent cardiac risk classification, although risk reclassification may occur in a small subset of subjects.

Reducing the risk of hallucinations with interpretable deep learning models for low-dose CT denoising: comparative performance analysis.

Objective.Reducing CT radiation dose is an often proposed measure to enhance patient safety, which, however results in increased image noise, translating into degradation of clinical image quality. Several deep learning methods have been proposed for low-dose CT (LDCT) denoising. The high risks posed by possible hallucinations in clinical images necessitate methods which aid the interpretation of deep learning networks. In this study, we aim to use qualitative reader studies and quantitative radiomics studies to assess the perceived quality, signal preservation and statistical feature preservation of LDCT volumes denoised by deep learning. We aim to compare interpretable deep learning methods with classical deep neural networks in clinical denoising performance.Approach.We conducted an image quality analysis study to assess the image quality of the denoised volumes based on four criteria to assess the perceived image quality. We subsequently conduct a lesion detection/segmentation study to assess the impact of denoising on signal detectability. Finally, a radiomic analysis study was performed to observe the quantitative and statistical similarity of the denoised images to standard dose CT (SDCT) images.Main results.The use of specific deep learning based algorithms generate denoised volumes which are qualitatively inferior to SDCT volumes(p< 0.05). Contrary to previous literature, denoising the volumes did not reduce the accuracy of the segmentation (p> 0.05). The denoised volumes, in most cases, generated radiomics features which were statistically similar to those generated from SDCT volumes (p> 0.05).Significance.Our results show that the denoised volumes have a lower perceived quality than SDCT volumes. Noise and denoising do not significantly affect detectability of the abdominal lesions. Denoised volumes also contain statistically identical features to SDCT volumes.

Performance evaluation of x-ray differential phase contrast computed tomography (PCT) with respect to medical imaging.

PURPOSE: To evaluate the maximum performance gain that can theoretically be achieved with differential phase contrast computed tomography (PCT) compared with absorption-based CT, applied to in vivo medical imaging. METHODS: We develop a mathematical framework for analyzing the performance of PCT relative to CT under ideal conditions. We validate our model by interpreting the results of published PCT experiments. Finally, we utilize our framework to evaluate the relative performance of PCT versus CT for in vivo medical imaging of the human body, investigating several clinically relevant material contrasts. RESULTS: We show that the performance of PCT relative to CT depends on the ratio of phase contrast and absorption contrast of the examined materials and increases with increasing effective coherence length and increasing spatial resolution. The introduced effective coherence length characterizes an experimental PCT setup; it comprises coherence of the beam as well as properties of the x-ray interferometer. Whole body medical CT will not benefit from phase-contrast imaging, because the higher phase contrast is overcompensated by the low coherence lengths of PCT setups with low-brilliance sources, and by limited spatial resolution. The relative performance of PCT, which is inferior to CT for all examined material contrasts at the resolution level of today's medical CT, can be improved by increasing spatial resolution at the expense of increased patient dose. At the break-even point of equal performance for PCT and CT, a radiation dose at least 1 order of magnitude higher than today is required. Mammographic CT already operates at higher spatial resolution and may benefit from PCT for some applications in terms of reduced patient dose at equal image quality. CONCLUSIONS: Phase-contrast imaging utilizing low-brilliance x-ray sources has limited potential for an application in routine whole body CT. Breast CT, however, may benefit from phase-contrast imaging. These conclusions are due to fundamental arguments and independent of whether technical issues (quality of gratings, etc.) can be solved. PCT will only be suitable for in vivo medical imaging if x-ray sources with much better spatial coherence are routinely available.

Frequency split metal artifact reduction (FSMAR) in computed tomography.

PURPOSE: The problem of metal artifact reduction (MAR) is almost as old as the clinical use of computed tomography itself. When metal implants are present in the field of measurement, severe artifacts degrade the image quality and the diagnostic value of CT images. Up to now, no generally accepted solution to this issue has been found. In this work, a method based on a new MAR concept is presented: frequency split metal artifact reduction (FSMAR). It ensures efficient reduction of metal artifacts at high image quality with enhanced preservation of details close to metal implants. METHODS: FSMAR combines a raw data inpainting-based MAR method with an image-based frequency split approach. Many typical methods for metal artifact reduction are inpainting-based MAR methods and simply replace unreliable parts of the projection data, for example, by linear interpolation. Frequency split approaches were used in CT, for example, by combining two reconstruction methods in order to reduce cone-beam artifacts. FSMAR combines the high frequencies of an uncorrected image, where all available data were used for the reconstruction with the more reliable low frequencies of an image which was corrected with an inpainting-based MAR method. The algorithm is tested in combination with normalized metal artifact reduction (NMAR) and with a standard inpainting-based MAR approach. NMAR is a more sophisticated inpainting-based MAR method, which introduces less new artifacts which may result from interpolation errors. A quantitative evaluation was performed using the examples of a simulation of the XCAT phantom and a scan of a spine phantom. Further evaluation includes patients with different types of metal implants: hip prostheses, dental fillings, neurocoil, and spine fixation, which were scanned with a modern clinical dual source CT scanner. RESULTS: FSMAR ensures sharp edges and a preservation of anatomical details which is in many cases better than after applying an inpainting-based MAR method only. In contrast to other MAR methods, FSMAR yields images without the usual blurring close to implants. CONCLUSIONS: FSMAR should be used together with NMAR, a combination which ensures an accurate correction of both high and low frequencies. The algorithm is computationally inexpensive compared to iterative methods and methods with complex inpainting schemes. No parameters were chosen manually; it is ready for an application in clinical routine.

Lowering kilovoltage to reduce radiation dose in contrast-enhanced abdominal CT: initial assessment of a prototype automated kilovoltage selection tool.

OBJECTIVE: The purpose of this study was to determine whether the use of an automated CT kilovoltage (kV) selection tool (Auto kV) can result in lower radiation dose without sacrificing image quality in contrast-enhanced abdominopelvic CT. MATERIALS AND METHODS: Tube potential, radiation dose, and iodine contrast-to-noise ratio (CNR) were retrospectively evaluated in 36 patients who underwent abdominopelvic CT with Auto kV, and compared with results from size-matched control patients using identical protocols. Two radiologists evaluated image quality (sharpness, noise, and diagnostic confidence) blinded to kV. Volume CT dose index (CTDI(vol)) was also compared with what each patient would have received from scanning at 120 kV. RESULTS: Mean (SD) CTDI(vol) was 16.0 (4.4) mGy after Auto kV versus 19.5 (4.0) mGy using standard 120-kV prescription and was 19.3 (6.0) mGy in control subjects (yielding dose reductions of 18.0% and 17.2%, respectively; p < 0.001 for both). Thirty of 36 patients were scanned at 100 kV (median dose reduction, 25%). Auto kV images were rated as very sharp in 33 (92%) and 36 (100%) cases versus 36 (100%) and 35 (97%) of the control cases, with all cases scored as having optimal noise. Readers had full diagnostic confidence in 34 (94%) and 36 (100%) of Auto kV cases; one reader scored "probably confident" in two cases (6%). Iodine CNRs for the aorta, liver, and portal vein were similar between Auto kV cases and control cases (p > 0.50, all comparisons). CONCLUSION: The use of an automated kV selection tool results in significant dose savings while maintaining diagnostic image quality and iodine CNR.

Limited parameter denoising for low-dose X-ray computed tomography using deep reinforcement learning.

BACKGROUND: The use of deep learning has successfully solved several problems in the field of medical imaging. Deep learning has been applied to the CT denoising problem successfully. However, the use of deep learning requires large amounts of data to train deep convolutional networks (CNNs). Moreover, due to the large parameter count, such deep CNNs may cause unexpected results. PURPOSE: In this study, we introduce a novel CT denoising framework, which has interpretable behavior and provides useful results with limited data. METHODS: We employ bilateral filtering in both the projection and volume domains to remove noise. To account for nonstationary noise, we tune the σ parameters of the volume for every projection view and every volume pixel. The tuning is carried out by two deep CNNs. Due to the impracticality of labeling, the two-deep CNNs are trained via a Deep-Q reinforcement learning task. The reward for the task is generated by using a custom reward function represented by a neural network. Our experiments were carried out on abdominal scans for the Mayo Clinic the cancer imaging archive (TCIA) dataset and the American association of physicists in medicine (AAPM) Low Dose CT Grand Challenge. RESULTS: Our denoising framework has excellent denoising performance increasing the peak signal to noise ratio (PSNR) from 28.53 to 28.93 and increasing the structural similarity index (SSIM) from 0.8952 to 0.9204. We outperform several state-of-the-art deep CNNs, which have several orders of magnitude higher number of parameters (p-value [PSNR] = 0.000, p-value [SSIM] = 0.000). Our method does not introduce any blurring, which is introduced by mean squared error (MSE) loss-based methods, or any deep learning artifacts, which are introduced by wasserstein generative adversarial network (WGAN)-based models. Our ablation studies show that parameter tuning and using our reward network results in the best possible results. CONCLUSIONS: We present a novel CT denoising framework, which focuses on interpretability to deliver good denoising performance, especially with limited data. Our method outperforms state-of-the-art deep neural networks. Future work will be focused on accelerating our method and generalizing it to different geometries and body parts.

Iterative reconstruction algorithm for abdominal multidetector CT at different tube voltages: assessment of diagnostic accuracy, image quality, and radiation dose in a phantom study.

PURPOSE: To assess the diagnostic accuracy, image quality, and radiation dose of an iterative reconstruction algorithm compared with a filtered back projection (FBP) algorithm for abdominal computed tomography (CT) at different tube voltages. MATERIALS AND METHODS: A custom liver phantom with 45 simulated hypovascular liver tumors (diameters of 5, 10, and 15 mm; tumor-to-liver contrast of 10, 25, and 50 HU) was placed in a cylindrical water container that mimicked an intermediate-sized patient. The phantom was scanned at 120, 100, and 80 kVp. The CT data sets were reconstructed with FBP and iterative reconstruction. The image noise was measured, and the contrast-to-noise ratio (CNR) of the tumors was calculated. The radiation dose was assessed with the volume CT dose index. Tumor detection was independently performed by three radiologists. Statistical analysis included analysis of variance. RESULTS: Compared with the FBP data set at 120 kVp, the iterative reconstruction data set collected at 100 kVp demonstrated significantly lower mean image noise (20.9 and 16.7 HU, respectively; P < .001) and greater mean CNRs for the simulated tumors (P < .001). The iterative reconstruction data set collected at 120 kVp yielded the highest sensitivity for tumor detection, while the FBP data set at 80 kVp yielded the lowest. The sensitivity for the iterative reconstruction data set at 100 kVp was comparable with that for the FBP data set at 120 kVp (79.3% and 74.9%, respectively; P > .99). The volume CT dose index decreased by 39.8% between the 120-kVp protocol and the 100-kVp protocol and by 70.3% between the 120-kVp protocol and the 80-kVp protocol. CONCLUSION: Results of this phantom study suggest that a 100-kVp abdominal CT protocol with an iterative reconstruction algorithm for simulated intermediate-sized patients increases the image quality and maintains the diagnostic accuracy at a reduced radiation dose when compared with a 120-kVp protocol with an FBP algorithm.

High-temporal resolution dual-energy computed tomography of the heart using a novel hybrid image reconstruction algorithm: initial experience.

OBJECTIVES: Dual-energy computed tomography (DECT) has been proposed for the comprehensive assessment of coronary artery stenosis and myocardial perfusion yet traditionally required reducing the temporal resolution of cardiac studies. We evaluated a reconstruction algorithm that preserves high temporal resolution at cardiac DECT. METHODS: Twelve consecutive patients (3 women; mean [SD] age, 64 [10] years) with an abnormal single photon emission CT result underwent invasive coronary angiography and cardiac DECT. Dual-energy CT studies were reconstructed using the standard algorithm with 165-millisecond temporal resolution and a hybrid algorithm providing 83-millisecond temporal resolution. These studies were rated for coronary image quality and motion artifacts and compared with invasive coronary angiographic studies. RESULTS: One hundred sixty-eight coronary artery segments (82%) were evaluated. The standard 165-millisecond reconstruction provided 95% diagnostic segments compared with 100% using the 83-millisecond hybrid reconstruction. Image quality was rated significantly (P < 0.05) better with hybrid reconstruction and had 91.4% sensitivity, 94.7% specificity, 82.1% positive predictive value, and 97.7% negative predictive value for detecting significant stenosis versus 85.7%, 93.2%, 76.9%, and 96.1% with standard reconstruction, respectively. CONCLUSIONS: Hybrid image reconstruction mitigates the former limitations in temporal resolution of cardiac DECT.

Effect of beam hardening on arterial enhancement in thoracoabdominal CT angiography with increasing patient size: an in vitro and in vivo study.

PURPOSE: To assess the effect of beam hardening on arterial enhancement in thoracoabdominal computed tomographic (CT) angiography in various body sizes in a phantom and in a clinical study. MATERIALS AND METHODS: An abdominal aortic phantom was placed in three cylindrical water containers simulating a small, an intermediate, and a large patient (diameters of 22, 30, and 40 cm, respectively). CT scanning was performed at 80, 100, and 120 kVp, and aortic attenuation was assessed. In a HIPAA-compliant institutional review board-approved study with waiver of informed consent, thoracoabdominal aortic attenuation was assessed in 100 consecutive patients (75 men, 25 women; mean body weight (BW), 79 kg) undergoing thoracoabdominal 64-section CT angiography at 80 kVp. Overall aortic attenuation in patients grouped according to BW (small patients [n = 26], <70 kg; intermediate patients [n = 41], 70-85 kg; large patients [n = 33], >85 kg) was compared. Overall aortic attenuation was correlated with BW, height, body mass index (BMI), body surface area (BSA), and lateral diameter by using linear regression. RESULTS: Comparison of the intermediate- and large-phantom groups relative to the small-phantom group indicated that mean aortic attenuation decreased by 9.7% and 13.4% at 80 kVp, 9.8% and 15.1% at 100 kVp, and 13.8% and 23.8% at 120 kVp, respectively (P < .001). In the clinical study, the overall aortic attenuation decreased by 11.3% and 20.0% in the intermediate- and large-patient groups relative to the small-patient group, respectively (P < .05). Overall aortic attenuation correlated well with BSA (R = -0.454), height (R = -0.421), and BW (R = -0.414), followed by lateral diameter (R = -0.291) and BMI (R = -0.211). CONCLUSION: Beam hardening may substantially contribute to reduced arterial enhancement in larger patients undergoing thoracoabdominal CT angiography. The beam-hardening effect on arterial enhancement is more pronounced at higher tube voltages.

Normalized metal artifact reduction (NMAR) in computed tomography.

PURPOSE: While modern clinical CT scanners under normal circumstances produce high quality images, severe artifacts degrade the image quality and the diagnostic value if metal prostheses or other metal objects are present in the field of measurement. Standard methods for metal artifact reduction (MAR) replace those parts of the projection data that are affected by metal (the so-called metal trace or metal shadow) by interpolation. However, while sinogram interpolation methods efficiently remove metal artifacts, new artifacts are often introduced, as interpolation cannot completely recover the information from the metal trace. The purpose of this work is to introduce a generalized normalization technique for MAR, allowing for efficient reduction of metal artifacts while adding almost no new ones. The method presented is compared to a standard MAR method, as well as MAR using simple length normalization. METHODS: In the first step, metal is segmented in the image domain by thresholding. A 3D forward projection identifies the metal trace in the original projections. Before interpolation, the projections are normalized based on a 3D forward projection of a prior image. This prior image is obtained, for example, by a multithreshold segmentation of the initial image. The original rawdata are divided by the projection data of the prior image and, after interpolation, denormalized again. Simulations and measurements are performed to compare normalized metal artifact reduction (NMAR) to standard MAR with linear interpolation and MAR based on simple length normalization. RESULTS: Promising results for clinical spiral cone-beam data are presented in this work. Included are patients with hip prostheses, dental fillings, and spine fixation, which were scanned at pitch values ranging from 0.9 to 3.2. Image quality is improved considerably, particularly for metal implants within bone structures or in their proximity. The improvements are evaluated by comparing profiles through images and sinograms for the different methods and by inspecting ROIs. NMAR outperforms both other methods in all cases. It reduces metal artifacts to a minimum, even close to metal regions. Even for patients with dental fillings, which cause most severe artifacts, satisfactory results are obtained with NMAR. In contrast to other methods, NMAR prevents the usual blurring of structures close to metal implants if the metal artifacts are moderate. CONCLUSIONS: NMAR clearly outperforms the other methods for both moderate and severe artifacts. The proposed method reliably reduces metal artifacts from simulated as well as from clinical CT data. Computationally efficient and inexpensive compared to iterative methods, NMAR can be used as an additional step in any conventional sinogram inpainting-based MAR method.

Effect of nonlinear three-dimensional optimized reconstruction algorithm filter on image quality and radiation dose: validation on phantoms.

A new technique called the nonlinear three-dimensional optimized reconstruction algorithm filter (3D ORA filter) is currently used to improve CT image quality and reduce radiation dose. This technical note describes the comparison of image noise, slice sensitivity profile (SSP), contrast-to-noise ratio, and modulation transfer function (MTF) on phantom images processed with and without the 3D ORA filter, and the effect of the 3D ORA filter on CT images at a reduced dose. For CT head scans the noise reduction was up to 54% with typical bone reconstruction algorithms (H70) and a 0.6 mm slice thickness; for liver CT scans the noise reduction was up to 30% with typical high-resolution reconstruction algorithms (B70) and a 0.6 mm slice thickness. MTF and SSP did not change significantly with the application of 3D ORA filtering (P > 0.05), whereas noise was reduced (P < 0.05). The low contrast detectability and MTF of images obtained at a reduced dose and filtered by the 3D ORA were equivalent to those of standard dose CT images; there was no significant difference in image noise of scans taken at a reduced dose, filtered using 3D ORA and standard dose CT (P > 0.05). The 3D ORA filter shows good potential for reducing image noise without affecting image quality attributes such as sharpness. By applying this approach, the same image quality can be achieved whilst gaining a marked dose reduction.

Noise Texture Deviation: A Measure for Quantifying Artifacts in Computed Tomography Images With Iterative Reconstructions.

OBJECTIVES: The aims of this study were to introduce the measure noise texture deviation as quantitative parameter for evaluating iterative reconstruction (IR)-specific artifacts in computed tomography (CT) images and to test whether IR-specific artifacts, quantified through this measure, are reduced in advanced modeled IR (ADMIRE) as compared with sinogram-affirmed IR (SAFIRE) images of the liver ex vivo and in patients with hypodense liver lesions. MATERIALS AND METHODS: In the ex vivo study part, an abdominal phantom was used. In the institutional review board-approved in vivo study part, 40 consecutive patients (mean age, 63 years) with hypodense liver lesions undergoing abdominal CT in the portal-venous phase were included. Images were reconstructed with filtered back projection, with the second-generation IR algorithm SAFIRE and with the third-generation IR algorithm ADMIRE. Noise power spectra and noise texture deviation were calculated in the phantom; image noise was measured in the phantom and in patients. Two blinded readers evaluated all image data regarding IR-specific artifacts (plastic-like, blotchy appearance); patient data were evaluated regarding conspicuity and confidence for detecting hypodense liver lesions. RESULTS: Image noise was significantly reduced at increasing IR levels (P < 0.001) with both algorithms, with no significant differences between corresponding strength levels of SAFIRE and ADMIRE (all, P > 0.05). Noise power spectra were similar at corresponding strength levels of SAFIRE and ADMIRE (all, P > 0.05). Noise texture deviation in ADMIRE was reduced compared with corresponding strength levels of SAFIRE (all, P < 0.001) and strongly correlated with subjective IR-specific artifacts (r = 0.88, P < 0.001). Iterative reconstruction-specific artifacts were significantly reduced in ADMIRE compared with that in SAFIRE images at strength levels 3 or greater, both ex vivo and in vivo (all, P < 0.001). There were no significant differences in the readers' ratings of lesion conspicuity and lesion confidence in detecting hypodense liver lesions between SAFIRE and ADMIRE (P > 0.05). Only lesion conspicuity was superior with SAFIRE and ADMIRE compared with filtered back projection (all, P < 0.001). CONCLUSIONS: Noise texture deviation is a quantitative measure reflecting IR-specific artifacts and is reduced in CT images with ADMIRE compared with SAFIRE.

64- Versus 16-slice CT angiography for coronary artery stent assessment: in vitro experience.

OBJECTIVES: We sought to assess the visualization of different coronary artery stents and the delineation of in-stent stenoses using 64- and 16-slice multidector computed tomography (MDCT). MATERIALS AND METHODS: A total of 15 different coronary stents with a simulated in-stent stenosis were placed in a vascular phantom and scanned with a 16-slice and a 64-slice MDCT at orientations of 0 degree, 45 degrees, and 90 degrees relative to the scanner's z-axis. Visible lumen diameter and attenuation in the stented and the unstented segment of the phantom were measured. Three readers assessed stenosis delineation and visualization of the residual lumen using a 5-point scale. RESULTS: Artificial lumen narrowing (ALN) was significantly reduced with 64-slice CT compared with 16-slice CT. At an angle of 0 degree, 45 degrees, and 90 degrees relative to the scanner's z-axis, the ALN for 16-slice CT was 42.2%, 39.8%, and 44.0% using a slice-thickness of 1.0 mm and 40.9%, 40.4%, and 41.6% using a slice thickness of 0.75 mm, respectively. With 64-slice CT, the ALN was 39.1%, 37.3%, and 36.0% at the respective angles. The differences between attenuation values in the stented and unstented segment of the tube were significantly lower for 64-slice CT. Mean visibility scores were significantly higher for 64-slice CT. CONCLUSION: Use of the 64-slice CT results in superior visualization of the stent lumen and in-stent stenosis compared with 16-slice CT, especially when the stent is orientated parallel to the x-ray beam.

Noise Reduction in Abdominal Computed Tomography Applying Iterative Reconstruction (ADMIRE).

RATIONALE AND OBJECTIVES: The study aimed to compare image quality of filtered back projection (FBP) and iterative reconstruction (advanced modeled iterative reconstruction, ADMIRE) in contrast-enhanced computed tomography (CT) of the abdomen, and to assess the differences of reconstructions according to these methods. It also aimed to investigate the potential for noise reduction of ADMIRE for different reconstructed slice thicknesses. MATERIALS AND METHODS: CT data of the abdomen and pelvis were acquired using a 128-slice single-source CT system using automated kV selection and tube current adaption based on patients' anatomy. Raw data sets from patients scanned at 100 kV were selected, and images were reconstructed with slice thicknesses of 1 mm, 3 mm, and 5 mm, both with FBP and ADMIRE. Filter strength F1, F3, and F5 of the ADMIRE algorithm and the corresponding reconstruction kernels were used. In total, 58 raw data sets from 17 patients were used to reconstruct from the same raw data FBP and ADMIRE images, representing identical body regions. Identical regions of interest were placed at the same position of up to four images and image noise was measured. Differences of reconstructed images and detail preservation were tested using an image subtraction technique, and subjective image quality was assessed using a 5-point Likert scale. RESULTS: On average, for 1-mm slice thickness, noise reduction was 9.15% ± 2.4% with filter strength level F1, 30.2% ± 3.4% with F3, and 54.4% ± 7.0% with F5 as compared to FBP. For a slice thickness of 3 mm, noise reduction was 8.5% ± 3.7% with F1, 28.6% ± 3.9% with F3, and 52.2% ± 9.1% with F5. For 5 mm, the corresponding values are 8.9% ± 2.7%, 31.4% ± 2.8%, and 52.7% ± 7.7%. On subtraction images, edge information of tissue classes with a high attenuation gradient was found, but structures with small differences in attenuation were not detectable on subtraction images, confirming that no relevant details were lost in the iterative reconstruction process. CONCLUSIONS: ADMIRE is able to reduce image noise considerably (up to 50%) without any obvious negative impact on lesion depiction as assessed visually. Noise reduction of ADMIRE seems to be independent of slice thickness.

Comparison and Combination of Dual-Energy- and Iterative-Based Metal Artefact Reduction on Hip Prosthesis and Dental Implants.

PURPOSE: To compare and combine dual-energy based and iterative metal artefact reduction on hip prosthesis and dental implants in CT. MATERIAL AND METHODS: A total of 46 patients (women:50%,mean age:63±15years) with dental implants or hip prostheses (n = 30/20) were included and examined with a second-generation Dual Source Scanner. 120kV equivalent mixed-images were derived from reconstructions of the 100/Sn140kV source images using no metal artefact reduction (NOMAR) and iterative metal artefact reduction (IMAR). We then generated monoenergetic extrapolations at 130keV from source images without IMAR (DEMAR) or from source images with IMAR, (IMAR+DEMAR). The degree of metal artefact was quantified for NOMAR, IMAR, DEMAR and IMAR+DEMAR using a Fourier-based method and subjectively rated on a five point Likert scale by two independent readers. RESULTS: In subjects with hip prosthesis, DEMAR and IMAR resulted in significantly reduced artefacts compared to standard reconstructions (33% vs. 56%; for DEMAR and IMAR; respectively, p<0.005), but the degree of artefact reduction was significantly higher for IMAR (all p<0.005). In contrast, in subjects with dental implants only IMAR showed a significant reduction of artefacts whereas DEMAR did not (71%, vs. 8% p<0.01 and p = 0.1; respectively). Furthermore, the combination of IMAR with DEMAR resulted in additionally reduced artefacts (Hip prosthesis: 47%, dental implants 18%; both p<0.0001). CONCLUSION: IMAR allows for significantly higher reduction of metal artefacts caused by hip prostheses and dental implants, compared to a dual energy based method. The combination of DE-source images with IMAR and subsequent monoenergetic extrapolation provides an incremental benefit compared to both single methods.