Abdominal image quality and dose reduction with energy-integrating or photon-counting detectors dual-source CT: A phantom study

PURPOSE

The purpose of this study was to assess image-quality and dose reduction potential using a photon-counting computed tomography (PCCT) system by comparison with two different dual-source CT (DSCT) systems using two phantoms.

MATERIALS AND METHODS

Acquisitions on phantoms were performed using two DSCT systems (DSCT1 [Somatom Force] and DSCT2 [Somatom Pro.Pulse]) and one PCCT system (Naeotom Alpha) at four dose levels (13/6/3.4/1.8 mGy). Noise power spectrum (NPS) and task-based transfer function (TTF) were computed to assess noise magnitude and noise texture and spatial resolution (f50), respectively. Detectability indexes (d') were computed to model the detection of abdominal lesions: one unenhanced high-contrast task, one contrast-enhanced high-contrast task and one unenhanced low-contrast task. Image quality was subjectively assessed on an anthropomorphic phantom by two radiologists.

RESULTS

For all dose levels, noise magnitude values were lower with PCCT than with DSCTs. For all CT systems, similar noise texture values were found at 13 and 6 mGy, but the greatest noise texture values were found for DSCT2 and the lowest for PCCT at 3.4 and 1.8 mGy. For high-contrast inserts, similar or lower f50 values were found with PCCT than with DSCT1 and the opposite pattern was found for the low-contrast insert. For the three simulated lesions, d' values were greater with PCCT than with DSCTs. Abdominal images were rated satisfactory for clinical use by the radiologists for all dose levels with PCCT and for 13 and 6 mGy with DSCTs.

CONCLUSION

By comparison with DSCTs, PCCT reduces image-noise and improves detectability of simulated abdominal lesions without altering the spatial resolution and image texture. Image-quality obtained with PCCT seem to indicate greater potential for dose optimization than those obtained with DSCTs.

Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT

BACKGROUND

This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduced radiation doses. This is essential in the context of low-dose CT lung cancer screening where accurate volumetry and characterization of pulmonary nodules in repeated CT scanning are indispensable.

MATERIALS AND METHODS

A standardized CT dataset was established using an anthropomorphic chest phantom (Lungman, Kyoto Kaguku Inc., Kyoto, Japan) containing a set of 3D-printed lung nodules including six diameters (4 to 9 mm) and three morphology classes (lobular, spiculated, smooth), with an established ground truth. Images were acquired at varying radiation doses (6.04, 3.03, 1.54, 0.77, 0.41 and 0.20 mGy) and reconstructed with combinations of reconstruction kernels (soft and hard kernel) and reconstruction algorithms (ASIR-V and DLIR at low, medium and high strength). Semi-automatic volumetry measurements and subjective image quality scores recorded by five radiologists were analyzed with multiple linear regression and mixed-effect ordinal logistic regression models.

RESULTS

Volumetric errors of nodules imaged with DLIR are up to 50% lower compared to ASIR-V, especially at radiation doses below 1 mGy and when reconstructed with a hard kernel. Also, across all nodule diameters and morphologies, volumetric errors are commonly lower with DLIR. Furthermore, DLIR renders higher subjective IQ, especially at the sub-mGy doses. Radiologists were up to nine times more likely to score the highest IQ-score to these images compared to those reconstructed with ASIR-V. Lung nodules with irregular margins and small diameters also had an increased likelihood (up to five times more likely) to be ascribed the best IQ scores when reconstructed with DLIR.

CONCLUSION

We observed that DLIR performs as good as or even outperforms conventionally used reconstruction algorithms in terms of volumetric accuracy and subjective IQ of nodules in an anthropomorphic chest phantom. As such, DLIR potentially allows to lower the radiation dose to participants of lung cancer screening without compromising accurate measurement and characterization of lung nodules.

Comparison of iterative reconstruction implementations for multislice helical CT

The most mature image reconstruction algorithms in multislice helical computed tomography are based on analytical and iterative methods. Over the past decades, several methods have been developed for iterative reconstructions that improve image quality by reducing noise and artifacts. In the regularization step of iterative reconstruction, noise can be significantly reduced, thereby making low-dose CT. The quality of the reconstructed image can be further improved by using model-based reconstructions. In these reconstructions, the main focus is on modeling the data acquisition process, including the behavior of the photon beams, the geometry of the system, etc. In this article, we propose two model-based reconstruction algorithms using a virtual detector for multislice helical CT. The aim of this study is to compare the effect of using a virtual detector on image quality for the two proposed algorithms with a model-based iterative reconstruction using the original detector model. Since the algorithms are implemented using multiple GPUs, the merging of separately reconstructed volumes can significantly affect image quality. This issue is often referred to as the "long object" problem, for which we also present a solution that plays an important role in the proposed reconstruction processes. The algorithms were evaluated using mathematical and physical phantoms, as well as patient cases. The SSIM, MS-SSIM and L1 metrics were utilized to evaluate the image quality of the mathematical phantom case. To demonstrate the effectiveness of the algorithms, we used the CatPhan 600 phantom. Additionally, anonymized patient scans were used to showcase the improvements in image quality on real scan data.

Low-dose liver CT: image quality and diagnostic accuracy of deep learning image reconstruction algorithm

OBJECTIVES

To perform a comprehensive within-subject image quality analysis of abdominal CT examinations reconstructed with DLIR and to evaluate diagnostic accuracy compared to the routinely applied adaptive statistical iterative reconstruction (ASiR-V) algorithm.

MATERIALS AND METHODS

Oncologic patients were prospectively enrolled and underwent contrast-enhanced CT. Images were reconstructed with DLIR with three intensity levels of reconstruction (high, medium, and low) and ASiR-V at strength levels from 10 to 100% with a 10% interval. Three radiologists characterized the lesions and two readers assessed diagnostic accuracy and calculated signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), figure of merit (FOM), and subjective image quality, the latter with a 5-point Likert scale.

RESULTS

Fifty patients (mean age: 70 ± 10 years, 23 men) were enrolled and 130 liver lesions (105 benign lesions, 25 metastases) were identified. DLIR_H achieved the highest SNR and CNR, comparable to ASiR-V 100% (p ≥ .051). DLIR_M returned the highest subjective image quality (score: 5; IQR: 4-5; p ≤ .001) and significant median increase (29%) in FOM (p < .001). Differences in detection were identified only for lesions ≤ 0.5 cm: 32/33 lesions were detected with DLIR_M and 26 lesions were detected with ASiR-V 50% (p = .031). Lesion accuracy of was 93.8% (95% CI: 88.1, 97.3; 122 of 130 lesions) for DLIR and 87.7% (95% CI: 80.8, 92.8; 114 of 130 lesions) for ASiR-V 50%.

CONCLUSIONS

DLIR yields superior image quality and provides higher diagnostic accuracy compared to ASiR-V in the assessment of hypovascular liver lesions, in particular for lesions ≤ 0.5 cm.

CLINICAL RELEVANCE STATEMENT

Deep learning image reconstruction algorithm demonstrates higher diagnostic accuracy compared to iterative reconstruction in the identification of hypovascular liver lesions, especially for lesions ≤ 0.5 cm.

KEY POINTS

• Iterative reconstruction algorithm impacts image texture, with negative effects on diagnostic capabilities. • Medium-strength deep learning image reconstruction algorithm outperforms iterative reconstruction in the diagnostic accuracy of ≤ 0.5 cm hypovascular liver lesions (93.9% vs 78.8%), also granting higher objective and subjective image quality. • Deep learning image reconstruction algorithm can be safely implemented in routine abdominal CT protocols in place of iterative reconstruction.

Nonenhanced Photon Counting CT of the Head : Impact of the keV Level, Iterative Reconstruction and Calvaria on Image Quality in Monoenergetic Images

PURPOSE

Nonenhanced computed tomography (CT) of the head is among the most commonly performed CT examinations. The spectral information acquired by photon counting CT (PCCT) allows generation of virtual monoenergetic images (VMI). At the same time, image noise can be reduced using quantum iterative reconstruction (QIR). In this study, the image quality of VMI was evaluated depending on the keV level and the QIR level. Furthermore, the influence of the cranial calvaria was investigated to determine the optimal reconstruction for clinical application.

METHODS

A total of 51 PCCT (NAEOTOM Alpha, Siemens Healthineers, Erlangen, Germany) of the head were retrospectively analyzed. In a quantitative analysis, gray and white matter ROIs were evaluated in different brain areas at all available keV levels and QIR levels with respect to signal, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). The distance to the cranial calvaria of the ROIs was included in the analysis. This was followed by a qualitative reading by five radiologists including experienced neuroradiologists.

RESULTS

In most ROIs, signal and noise varied significantly between keV levels (p < 0.0001). The CNR had a focal maximum at 66 keV and an absolute maximum at higher keV, slightly differently located depending on ROI and QIR level. With increasing QIR level, a significant reduction in noise was achieved (p < 0.0001) except just beneath the cranial calvaria. The cranial calvaria had a strong effect on the signal (p < 0.0001) but not on gray and white matter noise. In the qualitative reading, the 60 keV VMI was rated best.

CONCLUSION

In nonenhanced PCCT of the head the selected keV level of the VMI and the QIR level have a crucial influence on image quality in VMI. The 60 keV and 66 keV VMI with high QIR level provided optimal subjective and objective image quality for clinical use. The cranial calvaria has a significant influence on the visualization of the adjacent brain matter; currently, this substantially limits the use of low keV VMIs (< 60 keV).

Comparison of low-energy virtual monoenergetic images between photon-counting CT and energy-integrating detectors CT: A phantom study

PURPOSE

The purpose of this study was to assess image quality and dose level using a photon-counting CT (PCCT) scanner by comparison with a dual-source CT (DSCT) scanner on virtual monoenergetic images (VMIs) at low energy levels.

MATERIALS AND METHODS

A phantom was scanned using a DSCT and a PCCT with a volume CT dose index of 11 mGy, and additionally at 6 mGy and 1.8 mGy for PCCT. Noise power spectrum and task-based transfer function were evaluated from 40 to 70 keV on VMIs to assess noise magnitude and noise texture (fav) and spatial resolution on two iodine inserts (f50), respectively. A detectability index (d') was computed to assess the detection of two contrast-enhanced lesions according to the energy level used.

RESULTS

For all energy levels, noise magnitude values were lower with PCCT than with DSCT at 11 and 6 mGy, but greater at 1.8 mGy. fav values were higher with PCCT than with DSCT at 11 mGy (8.6 ± 1.5 [standard deviation [SD]%), similar at 6 mGy (1.6 ± 1.5 [SD]%) and lower at 1.8 mGy (-17.8 ± 2.2 [SD]%). For both inserts, f50 values were higher with PCCT than DSCT at 11- and 6 mGy for all keV levels, except at 6 mGy and 40 keV. d' values were higher with PCCT than with DSCT at 11- and 6 mGy for all keV and both simulated lesions. Similar d' values to those of the DSCT at 11 mGy, were obtained at 2.25 mGy for iodine insert at 2 mg/mL and at 0.96 mGy for iodine insert at 4 mg/mL at 40 keV.

CONCLUSION

Compared to DSCT, PCCT reduces noise magnitude and improves noise texture, spatial resolution and detectability on VMIs for all low-keV levels.

Dual-energy CT Portal Venography: Clinical Application Values and Future Opportunities

Standard multidetector computed tomography (MDCT) uses a single X-ray tube to emit a mixed energy X-ray beam, which is received by a single detector. The difference is that dual-energy CT (DECT), a new equipment in recent years, employs a single X-ray tube or two X-ray tubes to emit two single-energy X-ray beams, which are received by a single or two detectors. The application of dual-energy technology to portal venography has become one of the research hotspots. This paper will elaborate on the clinical application values of DECT portal venography in improving portal vein image quality, distinguishing the nature of portal vein thrombus, reducing contrast agent dose and radiation dose, and will discuss the possibility of its movement from research to routine practice and future development opportunities.

Artifact suppression for breast specimen imaging in micro CBCT using deep learning

BACKGROUND

Cone-beam computed tomography (CBCT) has been introduced for breast-specimen imaging to identify a free resection margin of abnormal tissues in breast conservation. As well-known, typical micro CT consumes long acquisition and computation times. One simple solution to reduce the acquisition scan time is to decrease of the number of projections, but this method generates streak artifacts on breast specimen images. Furthermore, the presence of a metallic-needle marker on a breast specimen causes metal artifacts that are prominently visible in the images. In this work, we propose a deep learning-based approach for suppressing both streak and metal artifacts in CBCT.

METHODS

In this work, sinogram datasets acquired from CBCT and a small number of projections containing metal objects were used. The sinogram was first modified by removing metal objects and up sampling in the angular direction. Then, the modified sinogram was initialized by linear interpolation and synthesized by a modified neural network model based on a U-Net structure. To obtain the reconstructed images, the synthesized sinogram was reconstructed using the traditional filtered backprojection (FBP) approach. The remaining residual artifacts on the images were further handled by another neural network model, ResU-Net. The corresponding denoised image was combined with the extracted metal objects in the same data positions to produce the final results.

RESULTS

The image quality of the reconstructed images from the proposed method was improved better than the images from the conventional FBP, iterative reconstruction (IR), sinogram with linear interpolation, denoise with ResU-Net, sinogram with U-Net. The proposed method yielded 3.6 times higher contrast-to-noise ratio, 1.3 times higher peak signal-to-noise ratio, and 1.4 times higher structural similarity index (SSIM) than the traditional technique. Soft tissues around the marker on the images showed good improvement, and the mainly severe artifacts on the images were significantly reduced and regulated by the proposed.

METHOD

CONCLUSIONS

Our proposed method performs well reducing streak and metal artifacts in the CBCT reconstructed images, thus improving the overall breast specimen images. This would be beneficial for clinical use.

An Evaluation Analysis for Computed Tomography Image Quality of Primary Liver Cancer Lesions Based on Deep Learning Image Reconstruction

BACKGROUND

Abdominal multi-slice helical computed tomography (CT) and contrast-enhanced scanning have been widely recognized clinically.

OBJECTIVE

The impact of the deep learning image reconstruction (DLIR) on the quality of dynamic contrast-enhanced CT imaging of primary liver cancer lesions was evaluated through comparison with the filtered back projection (FBP) and the new generation of adaptive statistical iterative reconstruction-V (ASIR-V).

METHODS

We evaluated the image noise of the lesion, fine structures inside the lesion, and diagnostic confidence in 48 liver cancer subjects. The CT values of the solid part of the lesion and the adjacent normal liver tissue and the systolic and diastolic blood pressure (SD) values of the right paravertebral muscle were measured. The muscle SD value was considered as the background noise of the image, and the signal noise ratio (SNR) and contrast signal-to-noise ratio (CNR) of the lesion and normal liver parenchyma were calculated.

RESULTS

High consistency in the evaluation of image noise (Kappa = 0.717). The Kappa values for margin/pseudocapsule, fine structure within the lesion, and diagnostic confidence were 0.463, 0.527, and 0.625, respectively. Besides, the differences in SD, SNR and CNR data of reconstructed lesion images among the six groups were statistically significant.

CONCLUSION

The contrast-enhanced CT image noise of DLIR-H in the portal venous phase is much lower than that of ASIR-V and FBP in primary liver cancer patients. In terms of the lesion structure display, the new reconstruction algorithm DLIR is superior.

Implementing a coronary CT angiography protocol based on the body mass index: Radiation dose reduction, image quality, and diagnostic performance

OBJECTIVES

To evaluate the relation between the coronary calcium score and the posterior choice of kilovoltage according to radiologists' criteria in a standard coronary CT angiography protocol to rule out coronary disease. To quantify the reduction in ionizing radiation after linking kilovoltage to patients' body mass index in a low-dose protocol with iterative model reconstruction. To evaluate the image quality and diagnostic performance of the low-dose protocol.

MATERIAL AND METHODS

We compared anthropometric characteristics, calcium score, kilovoltage levels, size-specific dose estimates (SSDE), and the dose-length product (DLP) between a group of 50 patients who were prospectively recruited to undergo coronary CT angiography with a low-dose protocol and a historical group of 50 patients who underwent coronary CT angiography with the standard protocol. We correlated these parameters, the number of coronary segments that could not be evaluated with and without temporal padding, the attenuation, and the signal-to-noise ratio in the ascending aorta in the low-dose protocol with excellent imaging quality according to a semiquantitative scale. To calculate the diagnostic performance per patient, we used 24-month clinical follow-up including all tests as the gold standard.

RESULTS

In the standard protocol, the presence of coronary calcium correlated with the selection of high kilovoltage (p = 0.02); this correlation was not found in the low-dose protocol (p = 0.47). Median values of SSDE and DLP were significantly (p < 0.001) lower and less dispersed in the low-dose protocol [9.22 mGy (IQR 7.84-12.1 mGy) vs. 26.5 mGy (IQR 21.3-36.3 mGy) in the standard protocol] and [97 mGy cm (IQR 78-134 mGy cm) vs. 253 mGy cm (IQR 216-404 mGy cm) in the standard protocol], respectively. The overall quality of the images obtained with the low-dose protocol was considered good or excellent in 96% of the studies. The parameters associated with image quality in a multivariable model (C statistic = 0.792) were heart rate (estimated coefficient, -0,12 [95% confidence interval: -0.2, -0.04]; p < 0.01) and the SSDE (estimated coefficient, -0,26 [95% confidence interval: -0.51, -0.01]; p < 0.05). The CAD-RADS modifier for a not fully evaluable or diagnostic study was used on two occasions (4%); the final measures for the diagnosis of coronary disease were sensitivity 100%, specificity 94%, and efficacy 94%.

CONCLUSIONS

In the standard protocol, the radiologist selects higher kilovoltage for CT angiography studies for patients whose previous calcium score indicates the presence of coronary calcium. In the low-dose protocol, linking kilovoltage with body mass index enables the dose of radiation to be reduced by 65% while obtaining excellent or good image quality in 96% of studies and excellent diagnostic performance.

Accuracy and Precision of Iodine Quantification in Subtracted Micro-Computed Tomography: Effect of Reconstruction and Noise Removal Algorithms

PURPOSE

To evaluate the effect of reconstruction and noise removal algorithms on the accuracy and precision of iodine concentration (CI) quantified with subtracted micro-computed tomography (micro-CT).

PROCEDURES

Two reconstruction algorithms were evaluated: a filtered backprojection (FBP) algorithm and a simultaneous iterative reconstruction technique (SIRT) algorithm. A 3D bilateral filter (BF) was used for noise removal. A phantom study evaluated and compared the image quality, and the accuracy and precision of CI in four scenarios: filtered FBP, filtered SIRT, non-filtered FBP, and non-filtered SIRT. In vivo experiments were performed in an animal model of chemically-induced mammary cancer.

RESULTS

Linear relationships between the measured and nominal CI values were found for all the scenarios in the phantom study (R2 > 0.95). SIRT significantly improved the accuracy and precision of CI compared to FBP, as given by their lower bias (adj. p-value = 0.0308) and repeatability coefficient (adj. p-value < 0.0001). Noise removal enabled a significant decrease in bias in filtered SIRT images only; non-significant differences were found for the repeatability coefficient. The phantom and in vivo studies showed that CI is a reproducible imaging parameter for all the scenarios (Pearson r > 0.99, p-value < 0.001). The contrast-to-noise ratio showed non-significant differences among the evaluated scenarios in the phantom study, while a significant improvement was found in the in vivo study when SIRT and BF algorithms were used.

CONCLUSIONS

SIRT and BF algorithms improved the accuracy and precision of CI compared to FBP and non-filtered images, which encourages their use in subtracted micro-CT imaging.

The influence of image reconstruction methods on the diagnosis of pulmonary emphysema with convolutional neural network

This study investigated the influence of iterative reconstruction (IR) methods on computed tomography (CT) images when training convolutional neural network (CNN) models to diagnose pulmonary emphysema. To evaluate the influence of the IR algorithm on CNN, the present study comprised two steps: the comparison of noise reduction by IR algorithms using phantom examinations and the change in performance of CNN with IR algorithms using patient data. We retrospectively analyzed 97 patients. Raw CT data were reconstructed using the filtered back-projection (FBP) and adaptive statistical iterative reconstruction V (ASIR-V) algorithms with blending levels of 30%, 50%, and 70%. The models were trained using reconstructed CT images and were named the FBP, ASIR-V30, ASIR-V50, and ASIR-V70 models. The mean and the standard deviation of the CT values were 11.3 ± 21.2 at FBP, 11.0 ± 17.3 at ASIR-V30, 11.0 ± 14.4 at ASIR-V50, and 11.0 ± 11.8 at ASIR-V70. For all the evaluation metrics, the best values were obtained with the FBP model applied to the ASIR-V70 test images. The worst values were obtained with the ASIR-V70 model applied to the FBP test images. The model trained with FBP images exhibited significantly better performance than the models trained using IR images. The reduction in image noise with the IR algorithm on the test images contributed to improving the accuracy of the classification of emphysema subtypes using CNN.

Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images

PURPOSE

Deep learning reconstruction (DLR) has been introduced by major vendors, tested for CT examinations of a variety of organs, and compared with other reconstruction methods. The purpose of this study was to compare the capabilities of DLR for image quality improvement and lung texture evaluation with those of hybrid-type iterative reconstruction (IR) for standard-, reduced- and ultra-low-dose CTs (SDCT, RDCT and ULDCT) obtained with high-definition CT (HDCT) and reconstructed at 0.25-mm, 0.5-mm and 1-mm section thicknesses with 512 × 512 or 1024 × 1024 matrixes for patients with various pulmonary diseases.

MATERIALS AND METHODS

Forty age-, gender- and body mass index-matched patients with various pulmonary diseases underwent SDCT (CT dose index volume : mean ± standard deviation, 9.0 ± 1.8 mGy), RDCT (CTDIvol: 1.7 ± 0.2 mGy) and ULDCT (CTDIvol: 0.8 ± 0.1 mGy) at a HDCT. All CT data set were then reconstructed with 512 × 512 or 1024 × 1024 matrixes by means of hybrid-type IR and DLR. SNR of lung parenchyma and probabilities of all lung textures were assessed for each CT data set. SNR and detection performance of each lung texture reconstructed with DLR and hybrid-type IR were then compared by means of paired t tests and ROC analyses for all CT data at each section thickness.

RESULTS

Data for each radiation dose showed DLR attained significantly higher SNR than hybrid-type IR for each of the CT data (p < 0.0001). On assessments of all findings except consolidation and nodules or masses, areas under the curve (AUCs) for ULDCT with hybrid-type IR for each section thickness (0.91 ≤ AUC ≤ 0.97) were significantly smaller than those with DLR (0.97 ≤ AUC ≤ 1, p < 0.05) and the standard protocol (0.98 ≤ AUC ≤ 1, p < 0.05).

CONCLUSION

DLR is potentially more effective for image quality improvement and lung texture evaluation than hybrid-type IR on all radiation dose CTs obtained at HDCT and reconstructed with each section thickness with both matrixes for patients with a variety of pulmonary diseases.

Evaluation of three-dimensional dual-energy CT cholangiopancreatography image quality in patients with pancreatobiliary dilatation: Comparison with conventional single-energy CT

Objective

This study aimed to evaluate three-dimensional (3D) negative-contrast CT cholangiopancreatography (nCTCP) image quality using dual-energy CT (DECT) with iterative reconstruction (IR) technique in patients with pancreatobiliary dilatation compared with single-energy CT (SECT).

Methods

Of the patients, 67 and 56 underwent conventional SECT (SECT set) and DECT with IR technique (DECT set), respectively. All patients were retrospectively analyzed during the portal phase to compare objective image quality and other data including patient demographics, hepatic and pancreatic parenchymal enhancement, noise, and attenuation difference (AD) between dilated ducts and enhanced hepatic parenchyma, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and CT volume dose index (CTDIvol). Two radiologists used the five-point Likert scale to evaluate the subjective image quality of 3D nCTCP regarding image noise, sharpness of dilated ducts, and overall image quality. Statistical analyses used the Mann-Whitney U test.

Results

No significant difference in patient demographics in either CT set was showed during objective evaluation (p > 0.05). However, higher hepatic and pancreatic parenchymal enhancement, AD, SNR, and CNR and lower hepatic and pancreatic noise (p < 0.005) as well as CTDIvol (p = 0.005) on DECT than on SECT were observed. Higher mean grades on DECT than on SECT were showed for image noise (4.65 vs 3.92), sharpness of dilated ducts (4.52 vs 3.94), and overall image quality (4.45 vs 3.91; p < 0.001), respectively during subjective evaluation.

Conclusion

A higher overall image quality and lower radiation dose on 3D nCTCP can be obtained by DECT with IR technique than with conventional SECT in patients with pancreatobiliary dilatation.

Deep learning imaging reconstruction of reduced-dose 40 keV virtual monoenergetic imaging for early detection of colorectal cancer liver metastases

OBJECTIVE

To explore whether reduced-dose (RD) gemstone spectral imaging (GSI) and deep learning image reconstruction (DLIR) of 40 keV virtual monoenergetic image (VMI) enhanced the early detection and diagnosis of colorectal cancer liver metastases (CRLM).

METHODS

Thirty-five participants with pathologically confirmed colorectal cancer were prospectively enrolled from March to August 2022 after routine care abdominal computed tomography (CT). GSI mode was used for contrast-enhanced CT, and two portal venous phase CT images were obtained [standard-dose (SD) CT dose index (CTDIvol) = 15.51 mGy, RD CTDIvol = 7.95 mGy]. The 40 keV-VMI were reconstructed via filtered back projection (FBP) and iterative reconstruction (ASIR-V 60 %, AV60) of both SD and RD images. RD medium-strength deep learning image reconstruction (DLIR-M) and RD high-strength deep learning image reconstruction (DLIR-H) were used to reconstruct the 40 keV-VMI. The contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the liver and the lesions were objectively evaluated. The overall image quality, lesion conspicuity, and diagnostic confidence were subjectively evaluated, to compare the differences in evaluation results among the different images.

RESULTS

All 35 participants (mean age: 59.51 ± 11.01 years; 14 females) underwent SD and RD GSI portal venous-phase CT scans. The dose-length product of the RD GSI scan was reduced by 49-53 % lower than that of the SD GSI scan (420.22 ± 31.95) vs (817.58 ± 60.56). A total of 219 lesions were identified, including 55 benign lesions and 164 metastases, with an average size of 7.37 ± 4.14 mm. SD-FBP detected 207 lesions, SD-AV60 detected 201 lesions, and DLIR-M and DLIR-H detected 199 and 190 lesions, respectively. For lesions ≤ 5 mm, there was no statistical difference between SD-FBP vs DLIR-M (χ2McNemar = 1.00, P = 0.32) and SD-AV60 vs DLIR-M (χ2McNemar = 0.33, P = 0.56) in the detection rate. The CNR, SNR, and noise of DLIR-M and DLIR-H 40 keV-VMI images were better than those of SD-FBP images (P < 0.01) but did not differ significantly from those of SD-AV60 images (P > 0.05). When the lesions ≤ 5 mm, there were statistical differences in the overall diagnostic sensitivity of lesions compared with SD-FBP, SD-AV60, DLIR-M and DLIR-H (P＜0.01). There were no statistical differences in the sensitivity of lesions diagnosis between SD-FBP, SD-AV60 and DLIR-M (both P＞0.05). However, the DLIR-M subjective image quality and lesion diagnostic confidence were higher for SD-FBP (both P < 0.01).

CONCLUSION

Reduced dose DLIR-M of 40 keV-VMI can be used for routine follow-up care of colorectal cancer patients, to optimize evaluations and ensure CT image quality. Meanwhile, the detection rate and diagnostic sensitivity and specificity of small lesions, early liver metastases is not obviously reduced.

Pediatric low-dose head CT: Image quality improvement using iterative model reconstruction

PURPOSE

To evaluate the differences in pediatric non-contrast low-dose head computed tomography (CT) between filtered-back projection and iterative model reconstruction using objective and subjective image quality evaluation.

METHODS

A retrospective study evaluated children undergoing low-dose non-contrast head CT. All CT scans were reconstructed using both filtered-back projection and iterative model reconstruction. Objective image quality analysis was performed using contrast and signal-to-noise ratios for the supra- and infratentorial brain regions of identical regions of interest on the two reconstruction methods. Two experienced pediatric neuroradiologists evaluated subjective image quality, visibility of structures, and artifacts.

RESULTS

We evaluated 233 low-dose brain CT scans of 148 pediatric patients. There was a ∼2-fold improvement in the contrast-to-noise ratio between gray and white matter in the infra- and supratentorial regions (p < 0.001) using iterative model reconstruction compared to filtered-back projection. The white and gray matter signal-to-noise ratio improved more than 2-fold using iterative model reconstruction (p < 0.001). Furthermore, radiologists graded anatomical details, gray-white matter differentiation, beam hardening artifacts, and image quality using iterative model reconstructions as superior to filtered-back projection reconstructions.

CONCLUSION

Iterative model reconstructions had better contrast-to-noise and signal-to-noise ratios with fewer artifacts in pediatric CT brain scans using low-dose radiation protocols. This image quality improvement was demonstrated in the supra- and infratentorial regions. This method thus comprises an important tool for reducing children's exposure while maintaining diagnostic capability.

Computed Tomography of the Head : A Systematic Review on Acquisition and Reconstruction Techniques to Reduce Radiation Dose

In 1971, the first computed tomography (CT) scan was performed on a patient's brain. Clinical CT systems were introduced in 1974 and dedicated to head imaging only. New technological developments, broader availability, and the clinical success of CT led to a steady growth in examination numbers. Most frequent indications for non-contrast CT (NCCT) of the head include the assessment of ischemia and stroke, intracranial hemorrhage and trauma, while CT angiography (CTA) has become the standard for first-line cerebrovascular evaluation; however, resulting improvements in patient management and clinical outcomes come at the cost of radiation exposure, increasing the risk for secondary morbidity. Therefore, radiation dose optimization should always be part of technical advancements in CT imaging but how can the dose be optimized? What dose reduction can be achieved without compromising diagnostic value, and what is the potential of the upcoming technologies artificial intelligence and photon counting CT? In this article, we look for answers to these questions by reviewing dose reduction techniques with respect to the major clinical indications of NCCT and CTA of the head, including a brief perspective on what to expect from current and future developments in CT technology with respect to radiation dose optimization.

Improvement of diagnostic performance of hyperacute ischemic stroke in head CT using an image-based noise reduction technique with non-black-boxed process

PURPOSE

This study aims to investigate whether an image-based noise reduction (INR) technique with a conventional rule-based algorithm involving no black-boxed processes can outperform an existing hybrid-type iterative reconstruction (HIR) technique, when applied to brain CT images for diagnosis of early CT signs, which generally exhibit low-contrast lesions that are difficult to detect.

METHODS

The subjects comprised 27 patients having infarctions within 4.5 h of onset and 27 patients with no change in brain parenchyma. Images with thicknesses of 5 mm and 0.625 mm were reconstructed by HIR. Images with a thickness of 0.625 mm reconstructed by filter back projection (FBP) were processed by INR. The contrast-to-noise ratios (CNRs) were calculated between gray and white matters; lentiform nucleus and internal capsule; infarcted and non-infarcted areas. Two radiologists subjectively evaluated the presence of hyperdense artery signs (HASs) and infarctions and visually scored three properties regarding image quality (0.625-mm HIR images were excluded because of their notably worse noise appearances).

RESULTS

The CNRs of INR were significantly better than those of HIR with P < 0.001 for all the indicators. INR yielded significantly higher areas under the curve for both infarction and HAS detections than HIR (P < 0.001). Also, INR significantly improved the visual scores of all the three indicators.

CONCLUSION

The INR incorporating a simple and reproducible algorithm was more effective than HIR in detecting early CT signs and can be potentially applied to CT images from a large variety of CT systems.

Low dose CTPA using a low kV technique combined with high IR: A clinical study

INTRODUCTION

To investigate optimising a computerised tomography pulmonary angiogram (CTPA) scan protocol in terms of radiation dose and image quality using a low kV technique combined with high iterative reconstruction (IR) parameters (>50%) and apply the optimised protocol in clinical practice on patients irrespective of their body weight.

METHODS

CTPA examinations were performed on 64 patients equally divided into control and experimental groups. Patients in the control group were scanned using the current protocol (100 kV with 50% IR) while patients in the experimental group were scanned using an optimised protocol (80 kV with 60%IR). The radiation dose indices volume computerised tomography dose index (CTDIvol), dose length product (DLP), size specific dose estimates (SSDE) and effective dose (ED) were recorded. Subjective image quality was evaluated by 3 radiologists through absolute visual grading analysis (VGA) using an image quality scoring tool. The resultant image quality scores were analysed using Visual Grading Characteristics (VGC). Objective image quality was recorded in terms of contrast-to-noise-ratio (CNR) and signal-to-noise-ratio (SNR).

RESULTS

The application of the optimised protocol resulted in a statistically significant (p < 0.05) reduction in mean CTDIvol (-49%), DLP (-48%), SSDE (-52%) and ED (-49%). Objective image quality was significantly (p < 0.05) improved both in CNR (32%) and SNR (13%). Subjective image quality scores were higher for the current protocol but variation between the two protocols was not significant (p = 0.650).

CONCLUSIONS

When applying the low kV technique combined with high IR parameters, a significant dose reduction may be achieved while still maintaining diagnostic image quality.

IMPLICATIONS FOR PRACTICE

The low kV technique combined with high IR parameters is an effective optimisation technique which can be easily implemented for the CTPA protocol.

Triple modality image reconstruction of PET data using SPECT, PET, CT information increases lesion uptake in images of patients treated with radioembolization with [Formula: see text] micro-spheres

PURPOSE

Nuclear medicine imaging modalities like computed tomography (CT), single photon emission CT (SPECT) and positron emission tomography (PET) are employed in the field of theranostics to estimate and plan the dose delivered to tumors and the surrounding tissues and to monitor the effect of the therapy. However, therapeutic radionuclides often provide poor images, which translate to inaccurate treatment planning and inadequate monitoring images. Multimodality information can be exploited in the reconstruction to enhance image quality. Triple modality PET/SPECT/CT scanners are particularly useful in this context due to the easier registration process between images. In this study, we propose to include PET, SPECT and CT information in the reconstruction of PET data. The method is applied to Yttrium-90 ([Formula: see text]Y) data.

METHODS

Data from a NEMA phantom filled with [Formula: see text]Y were used for validation. PET, SPECT and CT data from 10 patients treated with Selective Internal Radiation Therapy (SIRT) were used. Different combinations of prior images using the Hybrid kernelized expectation maximization were investigated in terms of VOI activity and noise suppression.

RESULTS

Our results show that triple modality PET reconstruction provides significantly higher uptake when compared to the method used as standard in the hospital and OSEM. In particular, using CT-guided SPECT images, as guiding information in the PET reconstruction significantly increases uptake quantification on tumoral lesions.

CONCLUSION

This work proposes the first triple modality reconstruction method and demonstrates up to 69% lesion uptake increase over standard methods with SIRT [Formula: see text]Y patient data. Promising results are expected for other radionuclide combination used in theranostic applications using PET and SPECT.

Physical and visual evaluations of CT image quality of large low-contrast objects with visual model-based iterative reconstruction technique: a phantom study

We aimed to verify whether the image quality of large low-contrast objects can be improved using visual model-based iterative reconstruction (VMR) while maintaining the visibility of conventional filtered back projection (FBP) and reducing radiation dose through physical and visual evaluation. A 64-row multi-slice CT system with SCENARIA View (FUJIFILM healthcare Corp. Tokyo, Japan) was used. The noise power spectrum (NPS), task-based transfer function (TTF), and signal-to-noise ratio (SNR) were physically evaluated. A low contrast object as a substitute for a liver mass was visually evaluated. In the noise measurement, STD1 showed an 18% lower noise compared to FBP. STR4 was able to reduce noise by 58% compared to FBP. The NPS of VMR was similar to those of FBP from low to high spatial frequency. The NPS of VMR reconstructions showed a similar variation with frequency as FBP reconstructions. STD1 showed the highest 10% TTF, and higher 10% TTF was observed with lower VMR level. The SNR of VMR was close to that of FBP, and higher SNR was observed with higher VMR level. In the results of the visual evaluation, there was no significant difference in visual evaluation between STD1 and FBP (p = 0.99) and between STD2 and FBP (p = 0.56). We found that the NPS of VMR images was similar to that of FBP images, and it can reduce noise and radiation dose by 25% and 50%, respectively, without decreasing the visual image quality compared to FBP.

Implications of computed tomography reconstruction algorithms on coronary atheroma quantification: Comparison with intravascular ultrasound

BACKGROUND

Advances in coronary computed tomography angiography (CCTA) reconstruction algorithms are expected to enhance the accuracy of CCTA plaque quantification. We aim to evaluate different CCTA reconstruction approaches in assessing vessel characteristics in coronary atheroma using intravascular ultrasound (IVUS) as the reference standard.

METHODS

Matched cross-sections (n ​= ​7241) from 50 vessels in 15 participants with chronic coronary syndrome who prospectively underwent CCTA and 3-vessel near-infrared spectroscopy-IVUS were included. Twelve CCTA datasets per patient were reconstructed using two different kernels, two slice thicknesses (0.75 ​mm and 0.50 ​mm) and three different strengths of advanced model-based iterative reconstruction (IR) algorithms. Lumen and vessel wall borders were manually annotated in every IVUS and CCTA cross-section which were co-registered using dedicated software. Image quality was sub-optimal in the reconstructions with a sharper kernel, so these were excluded. Intraclass correlation coefficient (ICC) and repeatability coefficient (RC) were used to compare the estimations of the 6 CT reconstruction approaches with those derived by IVUS.

RESULTS

Segment-level analysis showed good agreement between CCTA and IVUS for assessing atheroma volume with approach 0.50/5 (slice thickness 0.50 ​mm and highest strength 5 ADMIRE IR) being the best (total atheroma volume ICC: 0.91, RC: 0.67, p ​< ​0.001 and percentage atheroma volume ICC: 0.64, RC: 14.06, p ​< ​0.001). At lesion-level, there was no difference between the CCTA reconstructions for detecting plaques (accuracy range: 0.64-0.67; p ​= ​0.23); however, approach 0.50/5 was superior in assessing IVUS-derived lesion characteristics associated with plaque vulnerability (minimum lumen area ICC: 0.64, RC: 1.31, p ​< ​0.001 and plaque burden ICC: 0.45, RC: 32.0, p ​< ​0.001).

CONCLUSION

CCTA reconstruction with thinner slice thickness, smooth kernel and highest strength advanced IR enabled more accurate quantification of the lumen and plaque at a segment-, and lesion-level analysis in coronary atheroma when validated against intravascular ultrasound.

CLINICALTRIALS

gov (NCT03556644).

Potential of employing a quantum iterative reconstruction algorithm for ultra-high-resolution photon-counting detector CT of the hip

INTRODUCTION

This study investigated the image quality of a new quantum iterative reconstruction algorithm (QIR) for high resolution photon-counting CT of the hip.

METHODS

Using a first-generation photon-counting CT scanner, five cadaveric specimens were examined with ultra-high-resolution protocols matched for radiation dose. Images were post-processed with a sharp convolution kernel and five different strength levels of iterative reconstruction (QIR 0 - QIR 4). Subjective image quality was rated independently by three radiologists on a five-point scale. Intraclass correlation coefficients (ICC) were computed for assessing interrater agreement. Objective image quality was evaluated by means of contrast-to-noise-ratios (CNR) in bone and muscle tissue.

RESULTS

For osseous tissue, subjective image quality was rated best for QIR 2 reformatting (median 5 [interquartile range 5-5]). Contrarily, for soft tissue, QIR 4 received the highest ratings among compared strength levels (3 [3-4]). Both ICC_bone (0.805; 95% confidence interval 0.711-0.877; p < 0.001) and ICC_muscle (0.885; 0.824-0.929; p < 0.001) suggested good interrater agreement. CNR in bone and muscle tissue increased with ascending strength levels of iterative reconstruction with the highest results recorded for QIR 4 (CNR_bone 29.43 ± 2.61; CNR_muscle 8.09 ± 0.77) and lowest results without QIR (CNR_bone 3.90 ± 0.29; CNR_muscle 1.07 ± 0.07) (all p < 0.001).

CONCLUSION

Reconstructing photon-counting CT data with an intermediate QIR strength level appears optimal for assessment of osseous tissue, whereas soft tissue analysis benefitted from applying the highest strength level available.

IMPLICATIONS FOR PRACTICE

Quantum iterative reconstruction technique can enhance image quality by significantly reducing noise and improving CNR in ultra-high resolution CT imaging of the hip.

Sparse-view and limited-angle CT reconstruction with untrained networks and deep image prior

BACKGROUND AND OBJECTIVE

Neural network based image reconstruction methods are becoming increasingly popular. However, limited training data and the lack of theoretical guarantees for generalizability raised concerns, especially in biomedical imaging applications. These challenges are known to lead to an unstable reconstruction process that poses significant problems in biomedical image reconstruction. In this paper, we present a new framework that uses untrained generator networks to tackle this challenge, leveraging the structure of deep networks for regularizing solutions based on a technique known as Deep Image Prior (DIP).

METHODS

To achieve a high reconstruction accuracy, we propose a framework optimizing both the latent vector and the weights of a generator network during the reconstruction process. We also propose the corresponding reconstruction strategies to improve the stability and convergent performance of the proposed framework. Furthermore, instead of calculating forward projection in each iteration, we propose implementing its normal operator as a convolutional kernel under parallel beam geometry, thus greatly accelerating the calculation.

RESULTS

Our experiments show that the proposed framework has significant improvements over other state-of-the-art conventional, pre-trained, and untrained methods under sparse-view, limited-angle, and low-dose conditions.

CONCLUSIONS

Applying to parallel beam X-ray imaging, our framework shows advantages in speed, accuracy, and stability of the reconstruction process. We also show that the proposed framework is compatible with all differentiable regularizations that are commonly used in biomedical image reconstruction literature. Our framework can also be used as a post-processing technique to further improve the reconstruction generated by any other reconstruction methods. Furthermore, the proposed framework requires no training data and can be adjusted on-demand to adapt to different conditions (e.g. noise level, geometry, and imaged object).

Semi-automated volumetry of pulmonary nodules: Intra-individual comparison of standard dose and chest X-ray equivalent ultralow dose chest CT scans

PURPOSE

To assess the performance of semi-automated volumetry of solid pulmonary nodules on single-energy tin-filtered ultralow dose (ULD) chest CT scans at a radiation dose equivalent to chest X-ray relative to standard dose (SD) chest CT scans and assess the impact of kernel and iterative reconstruction selection.

METHODS

Ninety-four consecutive patients from a prospective single-center study were included and underwent clinically indicated SD chest CT (1.9 ± 0.8 mSv) and additional ULD chest CT (0.13 ± 0.01 mSv) in the same session. All scans were reconstructed with a soft tissue (Br40) and lung (Bl64) kernel as well as with Filtered Back Projection (FBP) and Iterative Reconstruction (ADMIRE-3 and ADMIRE-5). One hundred and forty-eight solid pulmonary nodules were identified and analysed by semi-automated volumetry on all reconstructions. Nodule volumes were compared amongst all reconstructions thereby focusing on the agreement between SD and ULD scans.

RESULTS

Nodule volumes ranged from 58.5 (28.8-126) mm3 for ADMIRE-5 Br40 ULD reconstructions to 72.5 (39-134) mm3 for FBP Bl64 SD reconstructions with significant differences between reconstructions (p < 0.001). Interscan agreement of volumes between two given reconstructions ranged from ICC = 0.605 to ICC = 0.999. Between SD and ULD scans, agreement of nodule volumes was highest for FBP Br40 (ICC = 0.995), FBP Bl64 (ICC = 0.939) and ADMIRE-5 Bl64 (ICC = 0.994) reconstructions. ADMIRE-3 reconstructions exhibited reduced interscan agreement of nodule volumes (ICCs from 0.788 - 0.882).

CONCLUSIONS

The interscan agreement of node volumes between SD and ULD is high depending on the choice of kernel and reconstruction algorithm. However, caution should be exercised when comparing two image series that were not identically reconstructed.

Truncation effect reduction for fast iterative reconstruction in cone-beam CT

Background

Iterative reconstruction for cone-beam computed tomography (CBCT) has been applied to improve image quality and reduce radiation dose. In a case where an object’s actual projection is larger than a flat panel detector, CBCT images contain truncated data or incomplete projections, which degrade image quality inside the field of view (FOV). In this work, we propose truncation effect reduction for fast iterative reconstruction in CBCT imaging.

Methods

The volume matrix size of the FOV and the height of projection images were extrapolated to a suitable size. These extended projections were reconstructed by fast iterative reconstruction. Moreover, a smoothing parameter for noise regularization in iterative reconstruction was modified to reduce the accumulated error while processing. The proposed work was evaluated by image quality measurements and compared with conventional filtered backprojection (FBP). To validate the proposed method, we used a head phantom for evaluation and preliminarily tested on a human dataset.

Results

In the experimental results, the reconstructed images from the head phantom showed enhanced image quality. In addition, fast iterative reconstruction can be run continuously while maintaining a consistent mean-percentage-error value for many iterations. The contrast-to-noise ratio of the soft-tissue images was improved. Visualization of low contrast in the ventricle and soft-tissue images was much improved compared to those from FBP using the same dose index of 5 mGy.

Conclusions

Our proposed method showed satisfactory performance to reduce the truncation effect, especially inside the FOV with better image quality for soft-tissue imaging. The convergence of fast iterative reconstruction tends to be stable for many iterations.

Visualization of thrombus using iterative reconstruction and maximum intensity projection of thin-slice CT images

OBJECTIVE

Iterative reconstruction (IR) is a noise reduction method that facilitates the synthesis of maximum intensity projection (MIP) from a larger number of slices while maintaining resolution. The present study aimed to analyze whether CT evaluation using IR and MIP is ideal for thrombus evaluation of large vessel occlusions in patients with acute ischemic stroke.

METHODS

Three types of images for each patient were reconstructed and categorized into three groups: the "conventional group," evaluated using 0.5-mm slice CT, the "MIP group," evaluated using 0.5-mm slice CT processed with MIP, and the "IR + MIP group," evaluated with 0.5-mm slice CT processed with IR and MIP. Noise and image quality were evaluated with noise standard deviation (Noise SD) and contrast-to-noise ratio (CNR). Three experts evaluated the thrombus edge coordinates, made a visual assessment, and compared the data with the digital subtraction angiography (DSA) of the mechanical thrombectomy.

RESULTS

Twenty-nine patients with cerebral infarction having large vessel occlusion were included in this study. The IR + MIP group had a lower Noise SD and a statistically higher CNR, leading to more favorable image evaluations. The thrombus assessment showed no inter-rater variability in thrombus edge identification, and the visual assessment and comparison with DSA were statistically better in the IR + MIP group.

CONCLUSIONS

IR reduces noise and improves resolution. MIP in combination with IR facilitates visualization of thrombus.

Ultralow-dose CT of the petrous bone using iterative reconstruction technique, tin filter and high resolution detectors allows an adequate assessment of the petrous bone structures

OBJECTIVES

To assess image quality and radiation dose in computed tomography (CT) studies of the petrous bone done with a scanner using a tin filter, high-resolution detectors, and iterative reconstruction, and to compare versus in studies done with another scanner without a tin filter using filtered back projection reconstruction.

MATERIAL AND METHODS

Thirty two patients (group 1) were acquired with an ultra-low dose CT (32-MDCT, 130kV, tin filter and iterative reconstruction). Images and radiation doses were compared to 36 patients (group 2) acquired in a 16-MDCT (120kV and filtered back-projection). Muscle density, bone density, and background noise were measured. Signal-to-noise ratio (SNR) was calculated. To assess image quality, two independent radiologists subjectively evaluated the visualization of the different structures of the middle and inner ear (0=not visualized, 3=perfectly identified and delimited). Interobserver agreement was calculated. Effective dose at different anatomical levels with the dose-length product was recorded.

RESULTS

In the quantitative analysis, there were no significant differences in image noise between the two groups. In the qualitative analysis, a similar or slightly lower subjective score was obtained in the delimitation of different structures of the ossicular chain and cochlea in the 32-MDCT, compared to 16-MDCT, with statistically significant differences. Mean effective dose (±standard deviation) was 0.16±0.04mSv for the 32-MDCT and 1.25±0.30mSv for the 16-MDCT.

CONCLUSIONS

The use of scanners with tin filters, high-resolution detectors, and iterative reconstruction allows to obtain images with adequate quality for the evaluation of the petrous bone structures with ultralow doses of radiation (0.16±0.04mSv).

Performance evaluation of a micro-CT system for laboratory animal imaging with iterative reconstruction capabilities

BACKGROUND

In recent years, there has been a rapid proliferation in micro-computed tomography (micro-CT) systems becoming more available for routine preclinical research, with applications in many areas including bone, lung, cancer and cardiac imaging. Micro-CT provides the means to non-invasively acquire detailed anatomical information, but high-resolution imaging comes at the cost of longer scan times and higher doses, which is not desirable given the potential risks related to x-ray radiation. To achieve dose reduction and higher throughputs without compromising image quality (noise management), fewer projections can be acquired. This is where iterative reconstruction methods can have the potential to reduce noise since these algorithms can better handle sparse projection data, compared to filtered backprojection PURPOSE: We evaluate the performance characteristics of a compact benchtop micro-CT scanner that provides iterative reconstruction capabilities with GPU-based acceleration. More specifically, we thereby investigate the potential benefit of iterative reconstruction methods for dose reduction.

METHODS

Based on a series of phantom experiments, the benchtop micro-CT system was characterized in terms of image uniformity, noise, low contrast detectability, linearity and spatial resolution. Whole-body images of a plasticized ex vivo mouse phantom were also acquired. Different acquisition protocols (general-purpose versus high-resolution, including low dose scans) and different reconstruction strategies (analytic versus iterative algorithms: FDK, ISRA, ISRA-TV) were compared.

RESULTS

Signal uniformity was maintained across the radial and axial field-of-view (no cupping effect) with an average difference in Hounsfield units (HU) between peripheral and central regions below 50. For low contrast detectability, regions with at least ∆HU of 40 to surrounding material could be discriminated (for rods of 2.5 mm diameter). A high linear correlation (R² = 0.997) was found between measured CT values and iodine concentrations (0-40 mg/ml). Modulation transfer function (MTF) calculations on a wire phantom evaluated a resolution of 10.2 lp/mm at 10% MTF that was consistent with the 8.3% MTF measured on the 50 μm bars (10 lp/mm) of a bar-pattern phantom. Noteworthy changes in signal-to-noise and contrast-to-noise values were found for different acquisition and reconstruction protocols. Our results further showed the potential of iterative reconstruction methods to deliver images with less noise and artefacts.

CONCLUSIONS

In summary, the micro-CT system for laboratory animal imaging that was evaluated in the present work was shown to provide a good combination of performance characteristics between image uniformity, low contrast detectability and resolution in short scan times. With the iterative reconstruction capabilities of this micro-CT system in mind (ISRA and ISRA-TV), the adoption of such algorithms by GPU-based acceleration enables the integration of noise reduction methods which here demonstrated potential for high quality imaging at reduced doses. This article is protected by copyright. All rights reserved.

Task-specific spatial resolution properties of iterative and deep learning-based reconstructions in computed tomography: Comparison using tasks assuming small and large enhanced vessels

PURPOSE

The present study aims to evaluate TTFs of deep-learning-based image reconstruction (DLIR) and iterative reconstruction (IR) in computed tomography (CT) using a conventional task with a rod object with a diameter of 30 mm and a newly-proposed task with a wire of 1 mm in diameter, simulating large and small enhanced vessels, respectively.

METHODS

The rod or wire phantom made of a material equivalent to diluted iodine that exhibits about 270 Hounsfield unit (HU) was placed inside a 30-cm water phantom. In-plane and z-directional TTFs were measured for the rod using the circular edge (CE) and plane edge (PE) methods, respectively. By using the wire (iodine wire: IW), in-plane and z-directional TTFs were measured using Fourier transform (IW method). TTFs of filtered back projection (FBP), IR, and DLIR of a 256-row CT system and FBP and IR of a 64-row CT system were evaluated with CT dose indices of 10 and 5 mGy.

RESULTS

For DLIR and IR, TTFs measured using the IW method were notably lower than those using the CE (or PE) method; moreover, they were also lower than those of corresponding FBP, indicating that the small enhanced vessels with a diameter of about 1 mm would be blurred with both DLIR and IR.

CONCLUSIONS

The proposed IW method has turned out to be effective to evaluate TTFs for small enhanced vessels, which have not been properly evaluated by the CE or PE method conventionally recommended.

Image quality and radiologists' subjective acceptance using model-based iterative and deep learning reconstructions as adjuncts to ultrahigh-resolution CT in low-dose contrast-enhanced abdominopelvic CT: phantom and clinical pilot studies

Purpose

In contrast-enhanced abdominopelvic CT (CE-APCT) for oncologic follow-up, ultrahigh-resolution CT (UHRCT) may improve depiction of fine lesions and low-dose scans are desirable for minimizing the potential adverse effects by ionizing radiation. We compared image quality and radiologists’ acceptance of model-based iterative (MBIR) and deep learning (DLR) reconstructions of low-dose CE-APCT by UHRCT.

Methods

Using our high-resolution (matrix size: 1024) and low-dose (tube voltage 100 kV; noise index: 20–40 HU) protocol, we scanned phantoms to compare the modulation transfer function and noise power spectrum between MBIR and DLR and assessed findings in 36 consecutive patients who underwent CE-APCT (noise index: 35 HU; mean CTDI_vol: 4.2 ± 1.6 mGy) by UHRCT. We used paired t-test to compare objective noise and contrast-to-noise ratio (CNR) and Wilcoxon signed-rank test to compare radiologists’ subjective acceptance regarding noise, image texture and appearance, and diagnostic confidence between MBIR and DLR using our routine protocol (matrix size: 512; tube voltage: 120 kV; noise index: 15 HU) for reference.

Results

Phantom studies demonstrated higher spatial resolution and lower low-frequency noise by DLR than MBIR at equal doses. Clinical studies indicated significantly worse objective noise, CNR, and subjective noise by DLR than MBIR, but other subjective characteristics were better (P < 0.001 for all). Compared with the routine protocol, subjective noise was similar or better by DLR, and other subjective characteristics were similar or worse by MBIR.

Conclusion

Image quality, except regarding noise characteristics, and acceptance by radiologists were better by DLR than MBIR in low-dose CE-APCT by UHRCT.

Graphical abstract

Multispectral interlaced sparse sampling photoacoustic tomography based on directional total variation

BACKGROUND AND OBJECTIVE

Photoacoustic tomography (PAT) is capable of obtaining cross-sectional images of small animals that represent the optical absorption of biological tissues. The multispectral Interlaced Sparse Sampling PAT, or ISS-PAT, is a previously proposed PAT imaging method that offered high quality images with much sparser transducer angular coverage. Although it provides superior imaging performance, the original ISS-PAT method suffered from a heavy computation burden, which hinders its practical application.

METHODS

Here, we propose a new regularization scheme based on the directional total variation (dTV) for ISS-PAT. This method efficiently imposes the structural information by considering both the edge position and direction information of the anatomical prior image in ISS-PAT. It does not require image segmentation, and can be conveniently solved by a modified alternating direction of multipliers (ADMM) algorithm.

RESULTS

We perform simulation, tissue mimicking phantom and in vivo small animal experiments to evaluate the proposed scheme. The reconstructed PAT images showed image quality and spectral un-mixing accuracy close to those obtained by non-local means based ISS-PAT, but with much shorter image reconstruction time. For a 1/6 sparse sampling rate, the average efficiency improvement is nearly 16-folds.

CONCLUSIONS

The experimental results demonstrate the feasibility of the dTV regularization scheme for ISS-PAT. Its efficient image reconstruction performance facilitates the potential of the hardware realization and practical applications of the ISS-PAT.

Evaluation of Apparent Noise on CT Images Using Moving Average Filters

This study aims to devise a simple method for evaluating the magnitude of texture noise (apparent noise) observed on computed tomography (CT) images scanned at a low radiation dose and reconstructed using iterative reconstruction (IR) and deep learning reconstruction (DLR) algorithms, and to evaluate the apparent noise in CT images reconstructed using the filtered back projection (FBP), IR, and two types of DLR (AiCE Body and AiCE Body Sharp) algorithms. We set a square region of interest (ROI) on CT images of standard- and obese-sized low-contrast phantoms, slid different-sized moving average filters in the ROI vertically and horizontally in steps of 1 pixel, and calculated the standard deviation (SD) of the mean CT values for each filter size. The SD of the mean CT values was fitted with a curve inversely proportional to the filter size, and an apparent noise index was determined from the curve-fitting formula. The apparent noise index of AiCE Body Sharp images for a given mAs value was approximately 58, 23, and 18% lower than that of the FBP, AIDR 3D, and AiCE Body images, respectively. The apparent noise index was considered to reflect noise power spectrum values at lower spatial frequency. Moreover, the apparent noise index was inversely proportional to the square roots of the mAs values. Thus, the apparent noise index could be a useful indicator to quantify and compare texture noise on CT images obtained with different scan parameters and reconstruction algorithms.

Pediatric craniosynostosis computed tomography: an institutional experience in reducing radiation dose while maintaining diagnostic image quality

BACKGROUND

Children with craniosynostosis may undergo multiple computed tomography (CT) examinations for diagnosis and post-treatment follow-up, resulting in cumulative radiation exposure.

OBJECTIVE

To reduce the risks associated with radiation exposure, we evaluated the compliance, radiation dose reduction and clinical image quality of a lower-dose CT protocol for pediatric craniosynostosis implemented at our institution.

MATERIALS AND METHODS

The standard of care at our institution was modified to replace pediatric head CT protocols with a lower-dose CT protocol utilizing 100 kV, 5 mAs and iterative reconstruction. Study-ordered, protocol-utilized and radiation-dose indices were collected for studies performed with routine pediatric brain protocols (n=22) and with the lower-dose CT protocol (n=135). Two pediatric neuroradiologists evaluated image quality in a subset (n=50) of the lower-dose CT studies by scoring visualization of cranial structures, confidence of diagnosis and the need for more radiation dose.

RESULTS

During the 30-month period, the lower-dose CT protocol had high compliance, with 2/137 studies performed with routine brain protocols. With the lower-dose CT protocol, volume CT dose index (CTDI_vol) was 1.1 mGy for all patients (0-9 years old) and effective dose ranged from 0.06 to 0.22 mSv, comparable to a 4-view skull radiography examination. CTDI_vol was reduced by 98% and effective dose was reduced up to 67-fold. Confidence in diagnosing craniosynostosis was high and more radiation dose was considered unnecessary in all studies (n=50) by both radiologists.

CONCLUSION

Replacing the routine pediatric brain CT protocol with a lower-dose CT craniosynostosis protocol substantially reduced radiation exposure without compromising image quality or diagnostic confidence.

Assessment of gastric wall structure using ultra-high-resolution computed tomography

PURPOSE

To evaluate the image quality of ultra-high-resolution CT (U-HRCT) in the comparison among four different reconstruction methods, focusing on the gastric wall structure, and to compare the conspicuity of a three-layered structure of the gastric wall between conventional HRCT (C-HRCT) and U-HRCT.

METHOD

Our retrospective study included 48 patients who underwent contrast-enhanced U-HRCT. Quantitative analyses were performed to compare image noise of U-HRCT between deep-learning reconstruction (DLR) and other three methods (filtered back projection: FBP, hybrid iterative reconstruction: Hybrid-IR, and Model-based iterative reconstruction: MBIR). The mean overall image quality scores were also compared between the DLR and other three methods. In addition, the mean conspicuity scores for the three-layered structure of the gastric wall at five regions were compared between C-HRCT and U-HRCT.

RESULTS

The mean noise of U-HRCT with DLR was significantly lower than that with the other three methods (P < 0.001). The mean overall image quality scores with DLR images were significantly higher than those with the other three methods (P < 0.001). Regarding the comparison between C-HRCT and U-HRCT, the mean conspicuity scores for the three-layered structure of the gastric wall on U-HRCT were significantly better than those on C-HRCT in the fornix (5 [5-5] vs. 3.5 [3-4], P < 0.001), body (4 [3.25-5] vs. 4 [3-4], P = 0.039), angle (5 [4-5] vs. 3 [2-4], P < 0.001), and antral posterior (4 [3.25-5] vs. 2 [2-4], P < 0.001), except for antral anterior (4 [3-5] vs. 3 [3-4], P = 0.230) CONCLUSION: U-HRCT using DLR improved the image noise and overall image quality of the gastric wall as well as the conspicuity of the three-layered structure, suggesting its utility for the evaluation of the anatomical details of the gastric wall structure.

An alternative approach to tracing the volumic proliferation development of an entire tumor spheroid in 3D through a mini-Opto tomography platform

Microscopy, which is listed among the major in-situ imaging applications, allows to derive information from a biological sample on the existing architectural structures of cells and tissues and their changes over time. Large biological samples such as tumor spheroids cannot be imaged within one field of view, regional imaging in different areas and subsequent stitching are required to attain the full picture. Microscopy is not typically used to produce full-size visualization of tumor spheroids measuring a few millimeters in size. In this study, we propose a 3D volume imaging technique for tracing the growth of an entire tumor spheroid measuring up to 10 mm using a miniaturized optical (mini-Opto) tomography platform. We performed a primary analysis of the 3D imaging for the MIA PaCa-2 pancreatic tumoroid employing its 2D images produced with the mini-Opto tomography from different angles ranging from -25 ° to +25 ° at six different three-day-apart time points of consecutive image acquisition. These 2D images were reconstructed by using a 3D image reconstruction algorithm that we developed based on the algebraic reconstruction technique (ART). We were able to reconstruct the 3D images of the tumoroid to achieve 800 × 800-pixel 50-layer images at resolutions of 5-25 μm. We also created its 3D visuals to understand more clearly how its volume changed and how it looked over weeks. The volume of the tumor was calculated to be 6.761 mm³ at the first imaging time point and 46.899 mm³ 15 days after the first (at the sixth time point), which is 6.94 times larger in volume. The mini-Opto tomography can be considered more advantageous than commercial microscopy because it is portable, more cost-effective, and easier to use, and enables full-size visualization of biological samples measuring a few millimeters in size.

Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction

BACKGROUND

Efforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT image reconstruction to overcome the drawbacks of iterative reconstruction (IR). The purpose of our study was to compare the objective and subjective image quality of DLR and IR on pediatric abdomen and chest CT images.

METHODS

This retrospective study included pediatric body CT images from February 2020 to October 2020, performed on 51 patients (34 boys and 17 girls; age 1-18 years). Non-contrast chest CT (n = 16), contrast-enhanced chest CT (n = 12), and contrast-enhanced abdomen CT (n = 23) images were included. Standard 50% adaptive statistical iterative reconstruction V (ASIR-V) images were compared to images with 100% ASIR-V and DLR at medium and high strengths. Attenuation, noise, contrast to noise ratio (CNR), and signal to noise (SNR) measurements were performed. Overall image quality, artifacts, and noise were subjectively assessed by two radiologists using a four-point scale (superior, average, suboptimal, and unacceptable). A phantom scan was performed including the dose range of the clinical images used in our study, and the noise power spectrum (NPS) was calculated. Quantitative and qualitative parameters were compared using repeated-measures analysis of variance (ANOVA) with Bonferroni correction and Wilcoxon signed-rank tests.

RESULTS

DLR had better CNR and SNR than 50% ASIR-V in both pediatric chest and abdomen CT images. When compared with 50% ASIR-V, high strength DLR was associated with noise reduction in non-contrast chest CT (33.0%), contrast-enhanced chest CT (39.6%), and contrast-enhanced abdomen CT (38.7%) with increases in CNR at 149.1%, 105.8%, and 53.1% respectively. The subjective assessment of overall image quality and the noise was also better on DLR images (p < 0.001). However, there was no significant difference in artifacts between reconstruction methods. From NPS analysis, DLR methods showed a pattern of reducing the magnitude of noise while maintaining the texture.

CONCLUSION

Compared with 50% ASIR-V, DLR improved pediatric body CT images with significant noise reduction. However, artifacts were not improved by DLR, regardless of strength.

Optimization of image quality and accuracy of low iodine concentration quantification as function of dose level and reconstruction algorithm for abdominal imaging using dual-source CT: A phantom study

PURPOSE

The purpose of this study was to assess the impact of advanced modeled iterative reconstruction (ADMIRE) algorithm and dose levels on the accuracy of Hounsfield unit (HU) measurement, image noise and contrast-to-noise ratio (CNR) in virtual monochromatic images (VMIs) with low iodine concentrations, and the accuracy of iodine quantification.

MATERIALS AND METHODS

A CT phantom was scanned with dual-source CT using abdomen-pelvis examination parameters at four dose levels: 5, 8, 11 and 20 mGy. Images were reconstructed using filtered-back projection (FBP) and ADMIRE levels 3 and 5 (A3-A5). HU accuracy was assessed calculating the root-mean-square deviation (RMSD_HU). Image noise and CNR were computed on VMIs at 40/50/60/70 keV for 4 iodine inserts with 0.5, 1, 2 and 5 mg/mL concentrations. Accuracy of iodine quantification was assessed by the RMSD_iodine and iodine bias (IB).

RESULTS

The RMSD_HU decreased significantly as the dose levels increased compared to 5 mGy, except for 8 mGy with A3 (P = 0.380) and with A5 level (P = 0.945). Noise increased by 63.0 ± 3.0 (standard deviation [SD])% from 20 mGy to 5 mGy. Noise decreased significantly by -53.8 ± 0.9 (SD) % with A5 compared to FBP. The CNR decreased by -43.1 ± 6.5 (SD)% from 20 mGy to 5 mGy. It increased using ADMIRE, and as the ADMIRE levels increased. The RMSD_iodine and IB decreased as the dose level increased, and this was similar for all reconstruction types.

CONCLUSION

ADMIRE strongly improves image quality in VMIs and slightly improves HU accuracy but does not affect the accuracy of iodine quantification.

Design of a 3D printed coronary artery model for CT optimization

INTRODUCTION

To design a custom phantom of the coronary arteries to optimize CT coronary angiography (CCTA) protocols.

METHODS

Characteristics of the left and right coronary arteries (mean Hounsfield Unit (HU) values and diameters) were collected from consecutive CCTA examinations (n = 43). Four different materials (two mixtures of glycerine, gelatine and water, pig hearts, Ecoflex™ silicone) were scanned inside a Lungman phantom using the CCTA protocol to find the closest model to in vivo data. A 3D printed model of the coronary artery tree was created using CCTA data by exporting a CT volume rendering into Autodesk Meshmixer™ software. The model was placed in an acid bath for 5 h, then covered in Ecoflex™, which was removed after drying. Both the Ecoflex™ and pig heart were later filled with a mixture of contrast (Visipaque 320 mg I/ml), NaCl and gelatin and scanned with different levels of tube current and iterative reconstruction (ASiR-V). Objective (HU, noise and size (vessel diameter) and subjective analysis were performed on all scans.

RESULTS

The gelatine mixtures had HU values of 130 and 129, Ecoflex™ 65 and the pig heart 56. At the different mA/ASiR-V levels the contrast filled Ecoflex™ had a mean HU 318 ± 4, noise 47±7HU and diameter of 4.4 mm. The pig heart had a mean HU of 209 ± 5, noise 38±4HU and a diameter of 4.4 mm. With increasing iterative reconstruction level the visualisation of the pig heart arteries decreased so no measurements could be performed.

CONCLUSION

The use of a 3D printed model of the arteries and casting with the Ecoflex™ silicone is the most suitable solution for a custom-designed phantom.

IMPLICATIONS FOR PRACTICE

Custom designed phantoms using 3D printing technology enable cost effective optimisation of CT protocols.

Advances in deep learning for computed tomography denoising

Computed tomography (CT) has seen a rapid increase in use in recent years. Radiation from CT accounts for a significant proportion of total medical radiation. However, given the known harmful impact of radiation exposure to the human body, the excessive use of CT in medical environments raises concerns. Concerns over increasing CT use and its associated radiation burden have prompted efforts to reduce radiation dose during the procedure. Therefore, low-dose CT has attracted major attention in the radiology, since CT-associated x-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Therefore, several denoising methods have been developed and applied to image processing technologies with the goal of reducing image noise. Recently, deep learning applications that improve image quality by reducing the noise and artifacts have become commercially available for diagnostic imaging. Deep learning image reconstruction shows great potential as an advanced reconstruction method to improve the quality of clinical CT images. These improvements can provide significant benefit to patients regardless of their disease, and further advances are expected in the near future.

Improvement of Spatial Resolution with Iterative PET Reconstruction using UltraFast TOF

The impact of Time-of-Flight (TOF) on positron emission tomography (PET) spatial resolution is generally considered negligible. In this work, a two-step approach based on simulations of two-dimensional scanner configurations is taken to show that ultra-fast TOF has the potential to overcome the limitation induced by the physical size of detectors on spatial resolution. An estimation of the lower bound on spatial resolution using point sources is provided, followed by a qualitative assessment of the resolution obtained using a Hot Spot phantom. The impact of detector width, TOF resolution and TOF binning on the achieved spatial resolution is also studied. While gain beyond the expected blur due to detector size is demonstrated, the detector size remains one limiting factor albeit less prominent. The dependence on acquisition statistics to reach the full potential of TOF-induced gain in spatial resolution is demonstrated. A simulated brain phantom acquired with a fictive three-dimensional PET scanner was qualitatively analyzed and structures smaller than the typical limit are clearly made visible by reconstructing the images with a ∼13-ps TOF resolution. A potential application of this feature of ultra-fast TOF would be the design of clinical PET scanners achieving spatial resolution beyond the current state-of-the-art.

Optimisation of CT protocols in PET-CT across different scanner models using different automatic exposure control methods and iterative reconstruction algorithms

Background

A significant proportion of the radiation dose from a PET-CT examination is dependent on the CT protocol, which should be optimised for clinical purposes. Matching protocols on different scanners within an imaging centre is important for the consistency of image quality and dose. This paper describes our experience translating low-dose CT protocols between scanner models utilising different automatic exposure control (AEC) methods and reconstruction algorithms.

Methods

The scanners investigated were a newly installed Siemens Biograph mCT PET with 64-slice SOMATOM Definition AS CT using sinogram affirmed iterative reconstruction (SAFIRE) and two GE Discovery 710 PET scanners with 128-slice Optima 660 CT using adaptive statistical reconstruction (ASiR). Following exploratory phantom work, 33 adult patients of various sizes were scanned using the Siemens scanner and matched to patients scanned using our established GE protocol to give 33 patient pairs. A comparison of volumetric CT dose index (CTDI_vol) and image noise within these patient pairs informed optimisation, specifically for obese patients. Another matched patient study containing 27 patient pairs was used to confirm protocol matching. Size-specific dose estimates (SSDEs) were calculated for patients in the second cohort. With the acquisition protocol for the Siemens scanner determined, clinicians visually graded the images to identify optimal reconstruction parameters.

Results

In the first matched patient study, the mean percentage difference in CTDI_vol for Siemens compared to GE was − 10.7% (range − 41.7 to 50.1%), and the mean percentage difference in noise measured in the patients’ liver was 7.6% (range − 31.0 to 76.8%). In the second matched patient study, the mean percentage difference in CTDI_vol for Siemens compared to GE was − 20.5% (range − 43.1 to 1.9%), and the mean percentage difference in noise was 19.8% (range − 27.0 to 146.8%). For these patients, the mean SSDEs for patients scanned on the Siemens and GE scanners were 3.27 (range 2.83 to 4.22) mGy and 4.09 (range 2.81 to 4.82) mGy, respectively. The analysis of the visual grading study indicated no preference for any of the SAFIRE strengths.

Conclusions

Given the different implementations of acquisition parameters and reconstruction algorithms between vendors, careful consideration is required to ensure optimisation and standardisation of protocols.

Accuracy of thin-slice model-based iterative reconstruction designed for brain CT to diagnose acute ischemic stroke in the middle cerebral artery territory: a multicenter study

PURPOSE

Model-based iterative reconstruction (MBIR) yields higher spatial resolution and a lower image noise than conventional reconstruction methods. We hypothesized that thin-slice MBIR designed for brain CT could improve the detectability of acute ischemic stroke in the middle cerebral artery (MCA) territory.

METHODS

Included were 41 patients with acute ischemic stroke in the MCA territory; they were seen at 4 medical centers. The controls were 39 subjects without acute stroke. Images were reconstructed with hybrid IR and with MBIR designed for brain CT at slice thickness of 2 mm. We measured the image noise in the ventricle and compared the contrast-to-noise ratio (CNR) in the ischemic lesion. We analyzed the ability of reconstructed images to detect ischemic lesions using receiver operating characteristics (ROC) analysis; 8 observers read the routine clinical hybrid IR with 5 mm-thick images, while referring to 2 mm-thick hybrid IR images or MBIR images.

RESULTS

The image noise was significantly lower on MBIR- than hybrid IR images (1.2 vs. 3.4, p < 0.001). The CNR was significantly higher with MBIR than hybrid IR (6.3 vs. 1.6, p < 0.001). The mean area under the ROC curve was also significantly higher on hybrid IR plus MBIR than hybrid IR (0.55 vs. 0.48, p < 0.036). Sensitivity, specificity, and accuracy were 41.2%, 88.8%, and 65.7%, respectively, for hybrid IR; they were 58.8%, 86.1%, and 72.9%, respectively, for hybrid IR plus MBIR.

CONCLUSION

The additional thin-slice MBIR designed for brain CT may improve the detection of acute MCA stroke.

Accelerated model-based quantitative diffusion MRI: A feasibility study for musculoskeletal application

PURPOSE

To develop a model-based reconstruction technique for diffusion quantification based on accelerated two-dimensional echo planar data, obtained with multiple b-weightings. In combination with a dedicated undersampling pattern, acceleration factors above three were proven feasible in a clinical setting.

METHODS

The proposed model-based method minimizes a cost function considering the l₂-norm of the difference between the Fourier transformation of a synthetic diffusion-model-generated k-space and the measured k-space data. Further regularization is performed by introduction of a total variation (TV) constraint to the cost function. Acceleration is achieved by a non-random undersampling pattern using acceleration factors that correspond to the total number of b-values. A rectangular region of variable size, centered in k-space, remains fully sampled for correction of phase variations, introduced by the different diffusion-encoding strengths.

RESULTS

Qualitative analysis of the resulting images (S₀ and ADC) demonstrates the potential of the suggested undersampling pattern in combination with a model-based iterative reconstruction. An edge analysis highlights the preservation of high-frequency information for all investigated undersampling factors. In comparison to a conventional SENSE-accelerated reconstruction, the quantitative analysis of the ADC maps revealed a significantly (P<0.05) superior performance of the suggested technique, enabling acceleration factors of R=3.65 without compromising diffusion data fidelity.

CONCLUSION

The presented work shows the potential of model-based ADC quantification, which, in combination with a suited undersampling pattern for multiple b-values, enables more than three-fold acceleration using two-dimensional EPI without sacrificing ADC fidelity.

A parallel spatial and Bloch manifold regularized iterative reconstruction method for MR Fingerprinting

Magnetic Resonance Fingerprinting (MRF) reconstructs tissue maps based on a sequence of very highly undersampled images. In order to be able to perform MRF reconstruction, state-of-the-art MRF methods rely on priors such as the MR physics (Bloch equations) and might also use some additional low-rank or spatial regularization. However to our knowledge these three regularizations are not applied together in a joint reconstruction. The reason is that it is indeed challenging to incorporate effectively multiple regularizations in a single MRF optimization algorithm. As a result most of these methods are not robust to noise especially when the sequence length is short. In this paper, we propose a family of new methods where spatial and low-rank regularizations, in addition to the Bloch manifold regularization, are applied on the images. We show on digital phantom and NIST phantom scans, as well as volunteer scans that the proposed methods bring significant improvement in the quality of the estimated tissue maps.

Detectability of pancreatic lesions by low-dose unenhanced computed tomography using iterative reconstruction

OBJECTIVES

To investigate the detectability of pancreatic cystic lesions and main pancreatic duct dilation by low-dose unenhanced computed tomography (CT).

MATERIAL AND METHODS

This study included 2684 patients who underwent low-dose unenhanced CT using iterative reconstruction and magnetic resonance imaging (MRI) as a part of a health-screening program between February 1, 2019 and December 31, 2019. Patients diagnosed with pancreatic cystic lesions and/or dilatations of the main pancreatic duct on MRI were identified. Detection rates by low dose CT in terms of lesion size were tested for significance by Fisher's exact test.

RESULTS

Of the 2684 patients, 558 (20.8 %) had pancreatic cystic lesions and 22 (0.8 %) had main pancreatic duct dilatation on MRI. The low-dose CT detection rates among the pancreatic cystic lesions were as follows: 1-9-mm cysts, three (0.65 %) of 461; 10-19-mm cysts, 17 (21.25 %) of 80, and ≥20-mm cysts, eight (47.06 %) of 17. The detection rates were significantly higher in the 10-19-mm and the ≥20-mm cyst group than in the 1-9-mm cyst group (p <  0.001). The detection rates among the main pancreatic duct dilatations were as follows: 3-5-mm dilatations, two (11.76 %) of 17 and ≥6-mm dilatations, four (80 %) of five, which were significantly higher rates than that for the 3-5-mm dilatations (p =  0.009).

CONCLUSION

Small pancreatic cysts and slight main pancreatic duct dilatation were practically undetectable by low-dose unenhanced CT. The application of a low-dose CT protocol as a screening tool in the detection of pancreatic abnormalities is not recommended.

Impact of dose reduction and the use of an advanced model-based iterative reconstruction algorithm on spectral performance of a dual-source CT system: A task-based image quality assessment

PURPOSE

To assess the impact of dose reduction and the use of an advanced modeled iterative reconstruction algorithm (ADMIRE) on image quality in low-energy monochromatic images from a dual-source dual energy computed tomography CT (DSCT) platform.

MATERIALS AND METHODS

Acquisitions on an image-quality phantom were performed using DSCT equipment with 100/Sn150 kVp for four dose levels (CTDI_vol: 20/11/8/5mGy). Raw data were reconstructed for six energy levels (40/50/60/70/80/100 keV) using filtered back projection and two levels of ADMIRE (A3/A5). Noise power spectrum (NPS) and task-based transfer function (TTF) were calculated on virtual monoenergetic images (VMIs). Detectability index (d') was computed to model the detection task of two enhanced iodine lesions as function of keV.

RESULTS

Noise-magnitude was significantly reduced between 40 to 70 keV by -56±0% (SD) (range: -56%--55%) with FBP; -56±0% (SD) (-56%--56%) with A3; and -57±1% (SD) (range: -57%--56%) with A5. The average spatial frequency of the NPS peaked at 70 keV and decreased as ADMIRE level increased. TTF values at 50% were greatest at 40 keV and shifted towards lower frequencies as the keV increased. The detectability of both lesions increased with increasing dose level and ADMIRE level. For the simulated lesion with iodine at 2mg/mL, d' values peaked at 70 keV for all reconstruction types, except for A3 at 20 mGy and A5 at 11 and 20 mGy, where d' peaked at 60 keV. For the other simulated lesion, d' values were highest at 40 keV and decreased beyond.

CONCLUSION

At low keV on VMIs, this study confirms that iterative reconstruction reduces the noise magnitude, improves the spatial resolution and increases the detectability of enhanced iodine lesions.

Low Tube Voltage Computed Tomography Venography for Patients With Deep Vein Thrombosis of the Lower Extremities - A Comparison With Venous Ultrasonography

BACKGROUND

Low tube voltage computed tomography venography (CTV) can be expected to increase imaging contrast and decrease radiation exposure by using iterative reconstruction (IR). This study evaluated the diagnostic ability of low tube voltage CTV with IR for deep vein thrombosis (DVT), compared to ultrasonography (US).Methods and Results:Two experienced radiologists retrospectively reevaluated the CTV data of 55 of 318 consecutive patients suspected of having DVT or pulmonary embolism between December 2015 and April 2017. The 55 patients had undergone both low tube voltage CTV and US (within 1 day before or after CTV). The lower extremity veins were divided into 10 segments. The DVT forms were categorized into 3 types: complete, concentric, and eccentric. We analyzed the 534 overall segments (16 segments excluded in US) measured using both CTV and US. The sensitivity-specificity was overall 73.3-90.0%, for femoropopliteal, it was 90.0-93.2%, and for the calf, it was 71.1-87.2%. The diagnostic accuracy between the 'eccentric only' and 'others' groups focusing on DVT forms was compared, and significant differences were revealed, especially in the muscular vein.

CONCLUSIONS

The DVT diagnostic ability above the knee was comparable between low tube voltage CTV with IR and conventional CTV, and the radiation dose was reduced. It was suggested that eccentric DVT measured by CTV tend to be a false-positive, especially in the calf muscular vein.

Computed tomography colonography and radiation risk: How low can we go?

Computed tomography colonography (CTC) has become a key examination in detecting colonic polyps and colorectal carcinoma (CRC). It is particularly useful after incomplete optical colonoscopy (OC) for patients with sedation risks and patients anxious about the risks or potential discomfort associated with OC. CTC's main advantages compared with OC are its non-invasive nature, better patient compliance, and the ability to assess the extracolonic disease. Despite these advantages, ionizing radiation remains the most significant burden of CTC. This opinion review comprehensively addresses the radiation risk of CTC, incorporating imaging technology refinements such as automatic tube current modulation, filtered back projections, lowering the tube voltage, and iterative reconstructions as tools for optimizing low and ultra-low dose protocols of CTC. Future perspectives arise from integrating artificial intelligence in computed tomography machines for the screening of CRC.