State of the Art: Iterative CT Reconstruction Techniques

Utility of fast non-local means (FNLM) filter for detection of pulmonary nodules in chest CT for pediatric patient

PURPOSE

This study was aimed to evaluate the utility based on imaging quality of the fast non-local means (FNLM) filter in diagnosing lung nodules in pediatric chest computed tomography (CT).

METHODS

We retrospectively reviewed the chest CT reconstructed with both filtered back projection (FBP) and iterative reconstruction (IR) in pediatric patients with metastatic lung nodules. After applying FNLM filter with six h values (0.0001, 0.001, 0.01, 0.1, 1, and 10) to the FBP images, eight sets of images including FBP, IR, and FNLM were analyzed. The image quality of the lung nodules was evaluated objectively for coefficient of variation (COV), contrast to noise ratio (CNR), and point spread function (PSF), and subjectively for noise, sharpness, artifacts, and diagnostic acceptability.

RESULTS

The COV was lowest in IR images and decreased according to increasing h values and highest with FBP images (P < 0.001). The CNR was highest with IR images, increased according to increasing h values and lowest with FBP images (P < 0.001). The PSF was lower only in FNLM filter with h value of 0.0001 or 0.001 than in IR images (P < 0.001). In subjective analysis, only images of FNLM filter with h value of 0.0001 or 0.001 rarely showed unacceptable quality and had comparable results with IR images. There were less artifacts in FNLM images with h value of 0.0001 compared with IR images (p < 0.001).

CONCLUSION

FNLM filter with h values of 0.0001 allows comparable image quality with less artifacts compared with IR in diagnosing metastatic lung nodules in pediatric chest CT.

Technical Note: kV-independent coronary calcium scoring: A phantom evaluation of score accuracy and potential radiation dose reduction

PURPOSE

To determine the accuracy of CT number and calcium score of a kV-independent technique based on an artificial 120 kV reconstruction, and its potential to reduce radiation dose.

METHODS

Anthropomorphic chest phantoms were scanned on a third-generation dual-source CT system equipped with the artificial 120 kV reconstruction. First, a phantom module containing a 20-mm diameter hydroxyapatite (HA) insert was scanned inside the chest phantoms at different tube potentials (70-140 kV) to evaluate calcium CT number accuracy. Next, three small HA inserts (diameter/length = 5 mm) were inserted into a pork steak and scanned inside the phantoms to evaluate calcium score accuracy at different kVs. Finally, the same setup was scanned using automatic exposure control (AEC) at 120 kV, and then with automatic kV selection (auto-kV). Phantoms were also scanned at 120 kV using a size-dependent mA chart. CT numbers of soft tissue and calcium were measured from different kV images. Calcium score of each small HA insert was measured using commercial software.

RESULTS

The CT number difference from 120 kV was small with tube potentials from 90 to 140 kV for both soft tissue and calcium (maximal difference of 4/5 HU, respectively). Consistent calcium scores were obtained from images of different kVs compared to 120 kV, with a relative difference <8%. Auto-kV provided a 25-34% dose reduction compared to AEC alone.

CONCLUSION

A kV-independent calcium scoring technique can produce artificial 120 kV images with consistent soft tissue and calcium CT numbers compared to standard 120 kV examinations. When coupled with auto-kV, this technique can reduce radiation by 25-34% compared to that with AEC alone, while providing consistent calcium scores as that of standard 120 kV examinations.

Effect of CT reconstruction settings on the performance of a deep learning based lung nodule CAD system

PURPOSE

To study the effect of different reconstruction parameter settings on the performance of a commercially available deep learning based pulmonary nodule CAD system.

MATERIALS AND METHODS

We performed a retrospective analysis of 24 chest CT scans, reconstructed at 16 different reconstruction settings for two different iterative reconstruction algorithms (SAFIRE and ADMIRE) varying in slice thickness, kernel size and iterative reconstruction level strength using a commercially available deep learning pulmonary nodule CAD system. The DL-CAD software was evaluated at 25 different sensitivity threshold settings and nodules detected by the DL-CAD software were matched against a reference standard based on the consensus reading of three radiologists.

RESULTS

A total of 384 CT reconstructions was analysed from 24 patients, resulting in a total of 5786 found nodules. We matched the detected nodules against the reference standard, defined by a team of thoracic radiologists, and showed a gradual drop in recall, and an improvement in precision when the iterative strength levels were increased for a constant kernel size. The optimal DL-CAD threshold setting for use in our clinical workflow was found to be 0.88 with an F₂ of 0.73 ± 0.053.

CONCLUSIONS

The DL-CAD system behaves differently on IR data than on FBP data, there is a gradual drop in recall, and growth in precision when the iterative strength levels are increased. As a result, caution should be taken when implementing deep learning software in a hospital with multiple CT scanners and different reconstruction protocols. To the best of our knowledge, this is the first study that demonstrates this result from a DL-CAD system on clinical data.

Routine Chest X-Rays Are Inaccurate in Detecting Relevant Intrapulmonary Anomalies During Medical Assessments of Fitness to Dive

Introduction: Intrapulmonary pathology, such as bullae or blebs, can cause pulmonary barotrauma when diving. Many diving courses require chest X-rays (CXR) or high-resolution computed tomography (HRCT) to exclude asymptomatic healthy individuals with these lesions. The ability of routine CXRs and HRCT to assess fitness to dive has never been evaluated.

Methods: Military divers who underwent yearly medical assessments at the Royal Netherlands Navy Diving Medical Center, including CXR at initial assessment, and who received a HRCT between January and June 2018, were included. The correlations of CXR and HRCT results with fitness to dive assessments were analyzed using Fisher's exact tests.

Results: This study included 101 military divers. CXR identified bullae or blebs in seven divers, but HRCT found that these anomalies were not present in three subjects and were something else in four. CXR showed no anomalies in 94 subjects, but HRCT identified coincidental findings in 23 and bullae or blebs in seven. The differences between CXR and HRCT results were statistically significant (p = 0.023). Of the 34 subjects with anomalies on HRCT, 18 (53%) were disqualified for diving.

Discussion: Routine CXR in asymptomatic military divers does not contribute to the identification of relevant pathology in fitness to dive assessments and has a high false negative rate (32%). HRCT is more diagnostic than CXR but yields unclear results, leading to disqualification for diving. Fitness to dive tests should exclude routine CXR; rather, HRCT should be performed only in subjects with clinical indications.

Deep learning trained algorithm maintains the quality of half-dose contrast-enhanced liver computed tomography images: Comparison with hybrid iterative reconstruction: Study for the application of deep learning noise reduction technology in low dose

PURPOSE

This study compares the image and diagnostic qualities of a DEep Learning Trained Algorithm (DELTA) for half-dose contrast-enhanced liver computed tomography (CT) with those of a commercial hybrid iterative reconstruction (HIR) method used for standard-dose CT (SDCT).

METHODS

This study enrolled 207 adults, and they were divided into two groups: SDCT and low-dose CT (LDCT). SDCT was reconstructed using the HIR method (SDCT_HIR), and LDCT was reconstructed using both the HIR method (LDCT_HIR) and DELTA (LDCT_DL). Noise, Hounsfield unit (HU) values, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between three image series. Two radiologists assessed the noise, artefacts, overall image quality, visualisation of critical anatomical structures and lesion detection, characterisation and visualisation.

RESULTS

The mean effective doses were 5.64 ± 1.96 mSv for SDCT and 2.87 ± 0.87 mSv for LDCT. The noise of LDCT_DL was significantly lower than that of SDCT_HIR and LDCT_HIR. The SNR and CNR of LDCT_DL were significantly higher than those of the other two groups. The overall image quality, visualisation of anatomical structures and lesion visualisation between LDCT_DL and SDCT_HIR were not significantly different. For lesion detection, the sensitivities and specificities of SDCT_HIR vs. LDCT_DL were 81.9 % vs. 83.7 % and 89.1 % vs. 86.3 %, respectively, on a per-patient basis. SDCT_HIR showed 75.4 % sensitivity and 82.6 % specificity for lesion characterisation on a per-patient basis, whereas LDCT_DL showed 73.5 % sensitivity and 82.4 % specificity.

CONCLUSIONS

LDCT with DELTA had approximately 49 % dose reduction compared with SDCT with HIR while maintaining image quality on contrast-enhanced liver CT.

A preliminary evaluation study of applying a deep learning image reconstruction algorithm in low-kilovolt scanning of upper abdomen

OBJECTIVE

To investigate feasibility of applying deep learning image reconstruction (DLIR) algorithm in a low-kilovolt enhanced scan of the upper abdomen.

METHODS

A total of 64 patients (BMI<28) are selected for the enhanced upper abdomen scan and divided evenly into two groups. The tube voltages in Group A are 100kV in arterial phase and 80kV in venous phase, while tube voltages are 120kV during two phases in Group B. Image reconstruction algorithms used in Group A include the filtered back projection (FBP) algorithm, the adaptive statistical iterative reconstruction-Veo (ASIR-V 40% and 80%) algorithm, and the DLIR algorithm (DL-L, DL-M, DL-H). Image reconstruction algorithm used in Group B is ASIR-V40%. The different reconstruction algorithm images are used to measure the common hepatic artery, liver, renal cortex, erector spinae, and subcutaneous adipose in the arterial phase and the average CT value and standard deviation of the portal vein, liver, spleen, erector spinae, and subcutaneous adipose in the portal phase. The signal-to-noise ratio (SNR) is calculated, and the images are also scored subjectively.

RESULTS

In Group A, noise in the aorta, liver, portal vein (the portal phase), spleen (the portal phase), renal cortex, retroperitoneal adipose, and muscle is significantly lower in both the DL-H and ASIR-V80% images, and the SNR is significantly higher than those in the remaining groups (P<0.05). The SNR of each tissue and organ in Group B is not significantly different from that in DL-M, DL-L, and ASIR-V40% in Group A (P>0.05). The subjective image quality scores in the DL-H and B groups are higher than those in the other groups, and the FBP group has significantly lower image quality than the remaining groups (P<0.05).

CONCLUSION

For upper abdominal low-kilovolt enhanced scan data, the DLIR-H gear yields a more satisfactory image quality than the FBP and ASIR-V.

Deep learning reconstruction versus iterative reconstruction for cardiac CT angiography in a stroke imaging protocol: reduced radiation dose and improved image quality

Background

To assess the radiation dose and image quality of cardiac computed tomography angiography (CCTA) in an acute stroke imaging protocol using a deep learning reconstruction (DLR) method compared to a hybrid iterative reconstruction algorithm.

Methods

Retrospective analysis of 296 consecutive patients admitted to the emergency department for stroke suspicion. All patients underwent a stroke CT imaging protocol including a non-enhanced brain CT, a brain perfusion CT imaging if necessary, a CT angiography (CTA) of the supra-aortic vessels, a CCTA and a post-contrast brain CT. The CCTA was performed with a prospectively ECG-gated volume acquisition. Among all CT scans performed, 143 were reconstructed with an iterative reconstruction algorithm (AIDR 3D, adaptive iterative dose reduction three dimensional) and 146 with a DLR algorithm (AiCE, advanced intelligent clear-IQ engine). Image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective image quality (IQ) scored from 1 to 4 were assessed. Dose-length product (DLP), volume CT dose index (CTDIvol) and effective dose (ED) were obtained.

Results

The radiation dose was significantly lower with AiCE than with AIDR 3D (DLP =106.4±50.0 vs. 176.1±37.1 mGy·cm, CTDIvol =6.9±3.2 vs. 11.5±2.2 mGy, and ED =1.5±0.7 vs. 2.5±0.5 mSv) (P<0.001). The median SNR and CNR were higher [9.9 (IQR, 8.1-12.3); and 12.6 (IQR, 10.5-15.5), respectively], with AiCE than with AIDR 3D [6.5 (IQR, 5.2-8.5); and 8.4 (IQR, 6.7-11.0), respectively] (P<0.001). SNR and CNR were increased by 51% and 49%, respectively, with AiCE compared to AIDR 3D. The image quality was significantly better with AiCE (mean IQ score =3.4±0.7) than with AIDR 3D (mean IQ score =3±0.9) (P<0.001).

Conclusions

The use of a DLR algorithm for cardiac CTA in an acute stroke imaging protocol reduced the radiation dose by about 40% and improved the image quality by about 50% compared to an iterative reconstruction algorithm.

Effects of Patient Size and Radiation Dose on Iodine Quantification in Dual-Source Dual-Energy CT

RATIONALE AND OBJECTIVES

The purpose of this study was to investigate the potential effects of patient size and radiation dose on the accuracy of iodine quantification using dual-source dual-energy computed tomography (CT).

MATERIALS AND METHODS

Three phantoms representing different patient sizes were constructed, containing iodine inserts with concentrations from 0 to 20 mg/ml. Dual-energy CT scans were performed at six dose levels from 2 to 30 mGy. Iodine concentrations were measured using a three-material-decomposition algorithm and their accuracy was assessed.

RESULTS

In a small phantom, iodine quantification was accurate and consistent at all dose levels. In a medium phantom, minor underestimations were observed, and the results were consistent except at low dose. In the large phantom, more significant underestimation of iodine concentration was observed at higher doses (≥15 mGy), which was attributed to the beam-hardening effect. At lower doses, increasing upward bias was observed in the CT number, leading to significant overestimations of both iodine concentration and fat fraction, which was attributed to the photon-starvation effect. The severity of the latter effect was determined by mA instead of mAs, suggesting that the electronic noise, rather than the quantum noise, was responsible for the bias. Using higher kVp for the low-energy tube was found to alleviate these effects.

CONCLUSION

Reliable iodine quantification can be achieved using dual-source CT, but the result can be affected by patient size and dose rate. In large patients, biases may occur due to the beam-hardening and the photon-starvation effects, in which case higher dose rate and higher kVp are recommended to minimize these effects.

Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography

OBJECTIVES

Modern reconstruction and post-processing software aims at reducing image noise in CT images, potentially allowing for a reduction of the employed radiation exposure. This study aimed at assessing the influence of a novel deep-learning based software on the subjective and objective image quality compared to two traditional methods [filtered back-projection (FBP), iterative reconstruction (IR)].

METHODS

In this institutional review board-approved retrospective study, abdominal low-dose CT images of 27 patients (mean age 38 ± 12 years, volumetric CT dose index 2.9 ± 1.8 mGy) were reconstructed with IR, FBP and, furthermore, post-processed using a novel software. For the three reconstructions, qualitative and quantitative image quality was evaluated by means of CT numbers, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in six different ROIs. Additionally, the reconstructions were compared using SNR, peak SNR, root mean square error and mean absolute error to assess structural differences.

RESULTS

On average, CT numbers varied within 1 Hounsfield unit (HU) for the three assessed methods in the assessed ROIs. In soft tissue, image noise was up to 42% lower compared to FBP and up to 27% lower to IR when applying the novel software. Consequently, SNR and CNR were highest with the novel software. For both IR and the novel software, subjective image quality was equal but higher than the image quality of FBP-images.

CONCLUSION

The assessed software reduces image noise while maintaining image information, even in comparison to IR, allowing for a potential dose reduction of approximately 20% in abdominal CT imaging.

ADVANCES IN KNOWLEDGE

The assessed software reduces image noise by up to 27% compared to IR and 48% compared to FBP while maintaining the image information.The reduced image noise allows for a potential dose reduction of approximately 20% in abdominal imaging.

Cardiac Computed Tomography Perfusion: Contrast Agents, Challenges and Emerging Methodologies from Preclinical Research to the Clinics

Computed Tomography (CT) has long been regarded as a purely anatomical imaging modality. Recent advances on CT technology and Contrast Agents (CA) in both clinical and preclinical cardiac imaging offer opportunities for the use of CT in functional imaging. Combined with modern ECG-gating techniques, functional CT has now become a reality allowing a comprehensive evaluation of myocardial global and regional function, perfusion and coronary angiography. This article aims at reviewing the current status of cardiac CT perfusion and micro-CT perfusion with established and experimental scanners and contrast agents, from clinical practice to the experimental domain of investigations based on animal models of heart diseases.

Effect of an iterative reconstruction quantum noise reduction technique on computed tomography radiomic features

Purpose: The goal of this study was to quantify the effects of iterative reconstruction on radiomics features of normal anatomic structures on head and neck computed tomography (CT) scans. Methods: Regions of interest (ROI) containing five different tissue types and an ROI containing only air were extracted from CT scans of the head and neck from 108 patients. Each scan was reconstructed using three different iDose 4 reconstruction levels (2, 4, and 6) in addition to bone, thin slice (1-mm slice thickness), and thin-bone reconstructions. From each ROI in all reconstructions, 142 radiomic features were calculated. For each of the six ROIs, features were compared between combinations of iDose levels (2v4, 4v6, and 2v6) with a threshold of α = 0.05 after correcting for multiple comparisons ( p < 0.00006 ). Features from iDose 4 - 2 reconstructions were also compared to bone, thin slice, and thin-bone reconstructions. Spearman's rank correlation coefficient, ρ , quantified the relative feature value agreement across iDose 4 reconstructions. Results: When comparing radiomics features across the three iDose 4 reconstruction levels, over half of all features reflected significant differences for all tissue types, while no features demonstrated significant differences when extracted from air ROIs. When assessing feature value agreement, at least 97% of features reflected excellent agreement ( ρ > 0.9 ) when comparing the three iDose levels for all ROIs. When comparing iDose 4 - 2 to bone, thin slice, and thin-bone reconstructions, more than half of all features demonstrated significant differences for all ROIs and 89 % of features reflected excellent agreement for all ROIs. Conclusion: Many radiomics features are dependent on the iterative reconstruction level, and the magnitude of this dependency is affected by the tissue from which features are extracted. For studies using images reconstructed using varying iDose 4 reconstruction levels, features robust to these should be used.

Deep Learning CT Image Reconstruction in Clinical Practice

BACKGROUND

 Computed tomography (CT) is a central modality in modern radiology contributing to diagnostic medicine in almost every medical subspecialty, but particularly in emergency services. To solve the inverse problem of reconstructing anatomical slice images from the raw output the scanner measures, several methods have been developed, with filtered back projection (FBP) and iterative reconstruction (IR) subsequently providing criterion standards. Currently there are new approaches to reconstruction in the field of artificial intelligence utilizing the upcoming possibilities of machine learning (ML), or more specifically, deep learning (DL).

METHOD

 This review covers the principles of present CT image reconstruction as well as the basic concepts of DL and its implementation in reconstruction. Subsequently commercially available algorithms and current limitations are being discussed.

RESULTS AND CONCLUSION

 DL is an ML method that utilizes a trained artificial neural network to solve specific problems. Currently two vendors are providing DL image reconstruction algorithms for the clinical routine. For these algorithms, a decrease in image noise and an increase in overall image quality that could potentially facilitate the diagnostic confidence in lesion conspicuity or may translate to dose reduction for given clinical tasks have been shown. One study showed equal diagnostic accuracy in the detection of coronary artery stenosis for DL reconstructed images compared to IR at higher image quality levels. Consequently, a lot more research is necessary and should aim at diagnostic superiority in the clinical context covering a broadness of pathologies to demonstrate the reliability of such DL approaches.

KEY POINTS

  · Following iterative reconstruction, there is a new approach to CT image reconstruction in the clinical routine using deep learning (DL) as a method of artificial intelligence.. · DL image reconstruction algorithms decrease image noise, improve image quality, and have potential to reduce radiation dose.. · Diagnostic superiority in the clinical context should be demonstrated in future trials..

CITATION FORMAT

· Arndt C, Güttler F, Heinrich A et al. Deep Learning CT Image Reconstruction in Clinical Practice. Fortschr Röntgenstr 2021; 193: 252 - 261.

Iterative reconstruction and deep learning algorithms for enabling low-dose computed tomography in midfacial trauma

OBJECTIVES

The objective of this study was to quantitatively assess the image quality of Advanced Modeled Iterative Reconstruction (ADMIRE) and the PixelShine (PS) deep learning algorithm for the optimization of low-dose computed tomography protocols in midfacial trauma.

STUDY DESIGN

Six fresh frozen human cadaver head specimens were scanned by computed tomography using both standard and low-dose scan protocols. Three iterative reconstruction strengths were applied to reconstruct bone and soft tissue data sets and these were subsequently applied to the PS algorithm. Signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) were calculated for each data set by using the image noise measurements of 10 consecutive image slices from a standardized region of interest template.

RESULTS

The low-dose scan protocol resulted in a 61.7% decrease in the radiation dose. Radiation dose reduction significantly reduced, and iterative reconstruction and the deep learning algorithm significantly improved, the CNR for bone and soft tissue data sets. The algorithms improved image quality after substantial dose reduction. The greatest improvement in SNRs and CNRs was found using the iterative reconstruction algorithm.

CONCLUSION

Both the ADMIRE and PS algorithms significantly improved image quality after substantial radiation dose reduction.

Evaluation of Ultra-low-dose Paediatric Cone-beam Computed Tomography for Image-guided Radiotherapy

AIMS

In image-guided radiotherapy, daily cone-beam computed tomography (CBCT) is rarely applied to children due to concerns over imaging dose. Simulating low-dose CBCT can aid clinical protocol design by allowing visualisation of new scan protocols in patients without delivering additional dose. This work simulated ultra-low-dose CBCT and evaluated its use for paediatric image-guided radiotherapy by assessment of image registration accuracy and visual image quality.

MATERIALS AND METHODS

Ultra-low-dose CBCT was simulated by adding the appropriate amount of noise to projection images prior to reconstruction. This simulation was validated in phantoms before application to paediatric patient data. Scans from 20 patients acquired at our current clinical protocol (0.8 mGy) were simulated for a range of ultra-low doses (0.5, 0.4, 0.2 and 0.125 mGy) creating 100 scans in total. Automatic registration accuracy was assessed in all 100 scans. Inter-observer registration variation was next assessed for a subset of 40 scans (five scans at each simulated dose and 20 scans at the current clinical protocol). This subset was assessed for visual image quality by Likert scale grading of registration performance and visibility of target coverage, organs at risk, soft-tissue structures and bony anatomy.

RESULTS

Simulated and acquired phantom scans were in excellent agreement. For patient scans, bony atomy registration discrepancies for ultra-low-dose scans fell within 2 mm (translation) and 1° (rotation) compared with the current clinical protocol, with excellent inter-observer agreement. Soft-tissue registration showed large discrepancies. Bone visualisation and registration performance reached over 75% acceptability (rated 'well' or 'very well') down to the lowest doses. Soft-tissue visualisation did not reach this threshold for any dose.

CONCLUSION

Ultra-low-dose CBCT was accurately simulated and evaluated in patient data. Patient scans simulated down to 0.125 mGy were appropriate for bony anatomy set-up. The large dose reduction could allow for more frequent (e.g. daily) image guidance and, hence, more accurate set-up for paediatric radiotherapy.

Low Dose CT Perfusion With K-Space Weighted Image Average (KWIA)

CTP (Computed Tomography Perfusion) is widely used in clinical practice for the evaluation of cerebrovascular disorders. However, CTP involves high radiation dose (≥~200mGy) as the X-ray source remains continuously on during the passage of contrast media. The purpose of this study is to present a low dose CTP technique termed K-space Weighted Image Average (KWIA) using a novel projection view-shared averaging algorithm with reduced tube current. KWIA takes advantage of k-space signal property that the image contrast is primarily determined by the k-space center with low spatial frequencies and oversampled projections. KWIA divides each 2D Fourier transform (FT) or k-space CTP data into multiple rings. The outer rings are averaged with neighboring time frames to achieve adequate signal-to-noise ratio (SNR), while the center region of k-space remains unchanged to preserve high temporal resolution. Reduced dose sinogram data were simulated by adding Poisson distributed noise with zero mean on digital phantom and clinical CTP scans. A physical CTP phantom study was also performed with different X-ray tube currents. The sinogram data with simulated and real low doses were then reconstructed with KWIA, and compared with those reconstructed by standard filtered back projection (FBP) and simultaneous algebraic reconstruction with regularization of total variation (SART-TV). Evaluation of image quality and perfusion metrics using parameters including SNR, CNR (contrast-to-noise ratio), AUC (area-under-the-curve), and CBF (cerebral blood flow) demonstrated that KWIA is able to preserve the image quality, spatial and temporal resolution, as well as the accuracy of perfusion quantification of CTP scans with considerable (50-75%) dose-savings.

Reduced dose iterative reconstruction versus standard dose filtered back projection in detection of bladder tumors

Background

The purpose was to assess radiation dose, image quality, and diagnostic performance of reduced-dose scanning with iterative reconstruction (IR) compared with standard-dose with filtered back projection (FBP) with CT urography for detection of bladder tumor. This study was prospectively conducted on 21 patients with bladder masses. All patients were subjected to two scanning protocols: protocol A (standard dose with FBP) and protocol B (additional limited scan to the pelvis at delayed phase with low dose with IR). Based on body weight (< or > 80 kg), each protocol was subdivided into 2 protocols A1 (130 kVp) and A2 (130 kVp) and protocols B1 (80 kVp) and B2 (110 kVp). Radiation dose was assessed in terms of mean CT dose index (CTDI), Dose-length product (DLP) and effective dose (ED). Image quality and diagnostic accuracy were compared in both groups.

Results

Mean CTDI, DLP and ED were reduced by average 72.3 % in the 80 kVp protocol (B1) and by 36.3% in 110 kVp (B2) protocol compared to standard-dose protocols. There were significantly lower SNR (signal to noise ratio) between protocol A1 and B1 at aorta and psoas muscles. Subjective image quality analysis revealed no statistically significant differences between the protocol A2 and B2 whereas there were significant differences between protocol A1and B1 as regards to visual image noise and overall image quality. Diagnostic accuracy was identical among different protocols.

Conclusion

CT urography with IR scanning showed reduced radiation dose and no difference in detection of urothelial carcinomas from standard dose with FBP despite of degraded image quality in 80 kVp scanning.

Low-Dose Abdominal CT Using a Deep Learning-Based Denoising Algorithm: A Comparison with CT Reconstructed with Filtered Back Projection or Iterative Reconstruction Algorithm

Objective

To compare the image quality of low-dose (LD) computed tomography (CT) obtained using a deep learning-based denoising algorithm (DLA) with LD CT images reconstructed with a filtered back projection (FBP) and advanced modeled iterative reconstruction (ADMIRE).

Materials and Methods

One hundred routine-dose (RD) abdominal CT studies reconstructed using FBP were used to train the DLA. Simulated CT images were made at dose levels of 13%, 25%, and 50% of the RD (DLA-1, -2, and -3) and reconstructed using FBP. We trained DLAs using the simulated CT images as input data and the RD CT images as ground truth. To test the DLA, the American College of Radiology CT phantom was used together with 18 patients who underwent abdominal LD CT. LD CT images of the phantom and patients were processed using FBP, ADMIRE, and DLAs (LD-FBP, LD-ADMIRE, and LD-DLA images, respectively). To compare the image quality, we measured the noise power spectrum and modulation transfer function (MTF) of phantom images. For patient data, we measured the mean image noise and performed qualitative image analysis. We evaluated the presence of additional artifacts in the LD-DLA images.

Results

LD-DLAs achieved lower noise levels than LD-FBP and LD-ADMIRE for both phantom and patient data (all p < 0.001). LD-DLAs trained with a lower radiation dose showed less image noise. However, the MTFs of the LD-DLAs were lower than those of LD-ADMIRE and LD-FBP (all p < 0.001) and decreased with decreasing training image dose. In the qualitative image analysis, the overall image quality of LD-DLAs was best for DLA-3 (50% simulated radiation dose) and not significantly different from LD-ADMIRE. There were no additional artifacts in LD-DLA images.

Conclusion

DLAs achieved less noise than FBP and ADMIRE in LD CT images, but did not maintain spatial resolution. The DLA trained with 50% simulated radiation dose showed the best overall image quality.

CT iterative vs deep learning reconstruction: comparison of noise and sharpness

OBJECTIVES

To compare image noise and sharpness of vessels, liver, and muscle in lower extremity CT angiography between "adaptive statistical iterative reconstruction-V" (ASIR-V) and deep learning reconstruction "TrueFidelity" (TFI).

METHODS

Thirty-seven patients (mean age, 65.2 years; 32 men) with lower extremity CT angiography were enrolled between November and December 2019. Images were reconstructed with two ASIR-V (blending factor of 80% and 100% (AV-100)) and three TFI (low-, medium-, and high-strength-level (TF-H) settings). Two radiologists evaluated these images for vessels (aorta, femoral artery, and popliteal artery), liver, and psoas muscle. For quantitative analyses, conventional indicators (CT number, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)) and blur metric values (indicating the degree of image sharpness) of selected regions of interest were determined. For qualitative analyses, the degrees of quantum mottle and blurring were assessed.

RESULTS

The higher the blending factor in ASIR-V or the strength in TFI, the lower the noise, the higher the SNR and CNR values, and the higher the blur metric values in all structures. The SNR and CNR values of TF-H images were significantly higher than those of AV-80 images and similar to those of AV-100 images. The blur metric values in TFI images were significantly lower than those in ASIR-V images (p < 0.001), indicating increased sharpness. Among all the investigated image procedures, the overall qualitative image quality was best in TF-H images.

CONCLUSION

TF-H was the most balanced image in terms of image noise and sharpness among the examined image combinations.

KEY POINTS

• Deep learning image reconstruction "TrueFidelity" is superior to iterative reconstruction "ASIR-V" regarding image noise and sharpness. • The high-strength "TrueFidelity" approach generated the best image quality among the examined image reconstruction procedures. • In iterative and deep learning CT image reconstruction, the higher the blending and strength factors, the lower the image noise and the poorer the image sharpness.

Surveillance Imaging following Endovascular Aneurysm Repair: State of the Art

Endovascular aneurysmal repair (EVAR) has become a prominent modality for the treatment of abdominal aortic aneurysm. Surveillance imaging is important for the detection of device-related complications, which include endoleak, structural abnormalities, and infection. Currently used modalities include ultrasound, X-ray, computed tomography, magnetic resonance imaging, and angiography. Understanding the advantages and drawbacks of each modality, as well available guidelines, can guide selection of the appropriate technique for individual patients. We review complications following EVAR and advances in surveillance imaging modalities.

Deep learning-based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V)

PURPOSE

To compare the image quality of brain computed tomography (CT) images reconstructed with deep learning-based image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V).

METHODS

Sixty-two patients underwent routine noncontrast brain CT scans and datasets were reconstructed with 30% ASIR-V and DLIR with three selectable reconstruction strength levels (low, medium, high). Objective parameters including CT attenuation, noise, noise reduction rate, artifact index of the posterior cranial fossa, and contrast-to-noise ratio (CNR) were measured at the levels of the centrum semiovale and basal ganglia. Subjective parameters including gray matter-white matter differentiation, sharpness, and overall diagnostic quality were also assessed and compared with the interobserver agreement.

RESULTS

There was a gradual reduction in the image noise and artifact index of the posterior cranial fossa as the strength levels of DLIR increased (all P < 0.001) compared with that of ASIR-V. CNR in both the centrum semiovale and basal ganglia levels also improved from the low to high strength levels of DLIR compared with that of ASIR-V (all P < 0.001). DLIR images with medium and high strength levels demonstrated the best subjective image quality scores among the reconstruction datasets. There was moderate to good interobserver agreement for the subjective image quality assessments with ASIR-V and DLIR.

CONCLUSION

On routine brain CT scans, optimized protocols with DLIR allowed significant reduction of noise and artifacts with improved subjective image quality compared with ASIR-V.

Highly reduced-dose CT of the lumbar spine in a human cadaver model

Purpose

Feasibility of a highly reduced-dose lumbar spine CT protocol using iterative reconstruction (IR) in a human cadaver model.

Materials and methods

The lumbar spine of 20 human cadavers was repeatedly examined using three different reduced-dose protocols (RDCT) with decreasing reference tube current-exposure time products (RDCT-1: 50 mAs; RDCT-2: 30 mAs; RDCT-3: 10 mAs) at a constant tube voltage of 140 kV. A clinical standard-dose protocol (SDCT) served as the reference (reference tube current–exposure time product: 70 mAs; tube voltage: 140 kV). Images were reconstructed using filtered back projection (FBP) and two increasing levels of IR: IRL4 and IRL6. A five-point scale was used by two observers to assess the diagnostic quality of anatomical structures (cortical and trabecular bone, intervertebral foramina, pedicles and intervertebral joints, spinous and transverse processes). Objective image noise (OIN) was measured. Results were interpreted using a linear mixed-effects regression model.

Results

RDCT-2 with IRL6 (1.2 ± 0.5mSv) was the lowest reduced-dose protocol which provided diagnostically acceptable and equivalent image quality compared to the SDCT (2.3 ± 1.1mSV) with FBP (p > 0.05). All RDCT protocols achieved a significant reduction of the mean (±SD) effective radiation doses (RDCT-1: 1.7±0.9mSv; RDCT-2: 1.2±0.5mSv; RDCT-3: 0.4±0.2mSv; p < 0.05) compared to SDCT. OIN was lower in all RDCT protocols with the application of IRL4 and IRL6, compared to the SDCT with FBP (p < 0.05).

Conclusion

Highly reduced-dose lumbar spine CT providing diagnostically acceptable image quality is feasible using IR in this cadaver model and may be transferred into a clinical setting.

Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique

OBJECTIVES

To evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning-based noise reduction technique for pediatric abdominal CT, compared with standard iodine concentration single-energy polychromatic CT (SECT).

METHODS

From December 2016 to May 2017, DECT with 300 mg•I/mL contrast medium was performed in 29 pediatric patients (17 boys, 12 girls; age, 2-19 years). The DECT images were reconstructed using a noise-optimized virtual monoenergetic reconstruction image (VMI) with and without a deep learning method. SECT images with 350 mg•I/mL contrast medium, performed within the last 3 months before the DECT, served as reference images. The quantitative and qualitative parameters were compared using paired t tests and Wilcoxon signed-rank tests, and the differences in radiation dose and total iodine administration were assessed.

RESULTS

The linearly blended DECT showed lower attenuation and higher noise than SECT. The 60-keV VMI showed an increase in attenuation and higher noise than SECT. The combined 60-keV VMI plus deep learning images showed low noise, no difference in contrast-to-noise ratios, and overall image quality or diagnostic image quality, but showed a higher signal-to-noise ratio in the liver and lower enhancement of lesions than SECT. The overall image and diagnostic quality of lesions were maintained on the combined noise reduction approach. The CT dose index volume and total iodine administration in DECT were respectively 19.6% and 14.3% lower than those in SECT.

CONCLUSION

Low iodine concentration DECT, combined with deep learning in pediatric abdominal CT, can maintain image quality while reducing the radiation dose and iodine load, compared with standard SECT.

KEY POINTS

• An image noise reduction approach combining deep learning and noise-optimized virtual monoenergetic image reconstruction can maintain image quality while reducing radiation dose and iodine load. • The 60-keV virtual monoenergetic image reconstruction plus deep learning images showed low noise, no difference in contrast-to-noise ratio, and overall image quality, but showed a higher signal-to-noise ratio in the liver and a lower enhancement of lesion than single-energy polychromatic CT. • This combination could offer a 19.6% reduction in radiation dose and a 14.3% reduction in iodine load, in comparison with a control group that underwent single-energy polychromatic CT with the standard protocol.

3D reconstructions, 4D imaging and postprocessing with CT in musculoskeletal disorders: Past, present and future

Three-dimensional (3D) imaging and post processing are common tasks used daily in many disciplines. The purpose of this article is to review the new postprocessing tools available. Although 3D imaging can be applied to all anatomical regions and used with all imaging techniques, its most varied and relevant applications are found with computed tomography (CT) data in musculoskeletal imaging. These new applications include global illumination rendering (GIR), unfolded rib reformations, subtracted CT angiography for bone analysis, dynamic studies, temporal subtraction and image fusion. In all of these tasks, registration and segmentation are two basic processes that affect the quality of the results. GIR simulates the complete interaction of photons with the scanned object, providing photorealistic volume rendering. Reformations to unfold the rib cage allow more accurate and faster diagnosis of rib lesions. Dynamic CT can be applied to cinematic joint evaluations a well as to perfusion and angiographic studies. Finally, more traditional techniques, such as minimum intensity projection, might find new applications for bone evaluation with the advent of ultra-high-resolution CT scanners. These tools can be used synergistically to provide morphologic, topographic and functional information and increase the versatility of CT.

Evaluation of Impact of Factors Affecting CT Radiation Dose for Optimizing Patient Dose Levels

The dose metrics and factors influencing radiation exposure for patients undergoing head, chest, and abdominal computed tomography (CT) scans were investigated for optimization of patient dose levels. The local diagnostic reference levels (DRLs) of adult CT scans performed in our hospital were established based on 28,147 consecutive examinations, including 5510 head scans, 9091 chest scans, and 13,526 abdominal scans. Among the six CT scanners used in our hospital, four of them are 64-slice multi-detector CT units (MDCT64), and the other two have detector slices higher than 64 (MDCTH). Multivariate analysis was conducted to evaluate the effects of body size, kVp, mAs, and pitch on volume CT dose index (CTDIvol). The local DRLs expressed in terms of the 75th percentile of CTDIvol for the head, chest, and abdominal scans performed on MDCT64 were 59.32, 9.24, and 10.64 mGy, respectively. The corresponding results for MDCTH were 57.90, 7.67, and 9.86 mGy. In regard to multivariate analysis, CTDIvol showed various dependence on the predictors investigated in this study. All regression relationships have coefficient of determination (R2) larger than 0.75, indicating a good fit to the data. Overall, the research results obtained through our workflow could facilitate the modification of CT imaging procedures once the local DRLs are unusually high compared to the national DRLs.

Contrast-Medium Anisotropy-Aware Tensor Total Variation Model for Robust Cerebral Perfusion CT Reconstruction with Low-Dose Scans

Perfusion computed tomography (PCT) is critical in detecting cerebral ischemic lesions. PCT examination with low-dose scans can effectively reduce radiation exposure to patients at the cost of degraded images with severe noise and artifacts. Tensor total variation (TTV) models are powerful tools that can encode the regional continuous structures underlying a PCT object. In a TTV model, the sparsity structures of the contrast-medium concentration (CMC) across PCT frames are assumed to be isotropic with identical and independent distribution. However, this assumption is inconsistent with practical PCT tasks wherein the sparsity has evident variations and correlations. Such modeling deviation hampers the performance of TTV-based PCT reconstructions. To address this issue, we developed a novel contrast-medium anisotropy-aware tensor total variation (CMAA-TTV) model to describe the intrinsic anisotropy sparsity of the CMC in PCT imaging tasks. Instead of directly on the difference matrices, the CMAA-TTV model characterizes sparsity on a low-rank subspace of the difference matrices which are calculated from the input data adaptively, thus naturally encoding the intrinsic variant and correlated anisotropy sparsity structures of the CMC. We further proposed a robust and efficient PCT reconstruction algorithm to improve low-dose PCT reconstruction performance using the CMAA-TTV model. Experimental studies using a digital brain perfusion phantom, patient data with low-dose simulation and clinical patient data were performed to validate the effectiveness of the presented algorithm. The results demonstrate that the CMAA-TTV algorithm can achieve noticeable improvements over state-of-the-art methods in low-dose PCT reconstruction tasks.

Model-Based Reconstruction of Large Three-Dimensional Optoacoustic Datasets

Iterative model-based algorithms are known to enable more accurate and quantitative optoacoustic (photoacoustic) tomographic reconstructions than standard back-projection methods. However, three-dimensional (3D) model-based inversion is often hampered by high computational complexity and memory overhead. Parallel implementations on a graphics processing unit (GPU) have been shown to efficiently reduce the memory requirements by on-the-fly calculation of the actions of the optoacoustic model matrix, but the high complexity still makes these approaches impractical for large 3D optoacoustic datasets. Herein, we show that the computational complexity of 3D model-based iterative inversion can be significantly reduced by splitting the model matrix into two parts: one maximally sparse matrix containing only one entry per voxel-transducer pair and a second matrix corresponding to cyclic convolution. We further suggest reconstructing the images by multiplying the transpose of the model matrix calculated in this manner with the acquired signals, which is equivalent to using a very large regularization parameter in the iterative inversion method. The performance of these two approaches is compared to that of standard back-projection and a recently introduced GPU-based model-based method using datasets from in vivo experiments. The reconstruction time was accelerated by approximately an order of magnitude with the new iterative method, while multiplication with the transpose of the matrix is shown to be as fast as standard back-projection.

Multi-energy CT imaging for large patients using dual-source photon-counting detector CT

Multi-energy CT imaging of large patients with conventional dual-energy (DE)-CT using an energy-integrating-detector (EID) is challenging due to photon starvation-induced image artifacts, especially in lower tube potential (80-100 kV) images. Here, we performed phantom experiments to investigate the performance of DECT for morbidly obese patients, using an iodine and water material decomposition task as an example, on an emulated dual-source (DS)-photon-counting-detector (PCD)-CT, and compared its performance with a clinical DS-EID-CT. An abdominal CT phantom with iodine inserts of different concentrations was wrapped with tissue-equivalent gel layers to emulate a large patient (50 cm lateral size). The phantom was scanned on a research whole-body single-source (SS)-PCD-CT (140 kV tube potential), a DS-PCD-CT (100/Sn140 kV; Sn140 indicates 140 kV with Sn filter), and a clinical DS-EID-CT (100/Sn140 kV) with the same radiation dose. Phantom scans were repeated five times on each system. The DS-PCD-CT acquisition was emulated by scanning twice on the SS-PCD-CT using different tube potentials. The multi-energy CT images acquired on each system were then reconstructed, and iodine- and water-specific images were generated using material decomposition. The root-mean-square-error (RMSE) between true and measured iodine concentrations were calculated for each system and compared. The images acquired on the DS-EID-CT showed severe artifacts, including ringing, reduced uniformity, and photon starvation artifacts, especially for low-energy images. These were largely reduced in DS-PCD-CT images. The CT number difference that was measured using regions-of-interest across field-of-view were reduced from 20.3 ± 0.9 (DS-EID-CT) to 2.5 ± 0.4 HU on DS-PCD-CT, showing improved image uniformity using DS-PCD-CT. Iodine RMSE was reduced from 3.42 ± 0.03 mg ml^-1 (SS-PCD-CT) and 2.90 ± 0.03 mg ml^-1 (DS-EID-CT) to 2.39 ± 0.05 mg ml^-1 using DS-PCD-CT. DS-PCD-CT out-performed a clinical DS-EID-CT for iodine and water-based material decomposition on phantom emulating obese patients by reducing image artifacts and improving iodine quantification (RMSE reduced by 20%). With DS-PCD-CT, multi-energy CT can be performed on large patients that cannot be accommodated with current DECT.

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, 130 kV, tin filter and iterative reconstruction). Images and radiation doses were compared to 36 patients (group 2) acquired in a 16-MDCT (120 kV 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 ± 0.04 mSv for the 32-MDCT and 1.25 ± 0.30 mSv 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±0.04 mSv).

A comprehensive review of dual-energy and multi-spectral computed tomography

This review will provide a brief introduction to the development of the first Computed Tomography (CT) scan, from the beginnings of x-ray imaging to the first functional CT system introduced by Godfrey Houndsfield. The principles behind photon interactions and the methods by which they can be leveraged to generate dual-energy or multi-spectral CT images are discussed. The clinical applications of these methodologies are investigated, showing the immense potential for dual-energy or multi-spectral CT to change the fields of in-vivo and non-destructive imaging for quantitative analysis of tissues and materials. Lastly the current trends of research for dual-energy and multi-spectral CT are covered, showing that the majority of instrument development is focused on photon counting detectors for mutli-spectral CT and that clinical research is dominated by validation studies for the implementation of dual-energy and multi-spectral CT.

Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise

OBJECTIVE

Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%).

MATERIALS AND METHODS

This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated.

RESULTS

Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001).

CONCLUSION

DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.

Optimization of Pediatric Body CT Angiography: What Radiologists Need to Know

OBJECTIVE. Pediatric CT angiography (CTA) presents unique challenges compared with adult CTA. Because of the ionizing radiation exposure, CTA should be used judiciously in children. The pearls offered here are observations gleaned from the authors' experience in the use of pediatric CTA. We also present some potential follies to be avoided. CONCLUSION. Understanding the underlying principles and paying meticulous attention to detail can substantially optimize dose and improve the diagnostic quality of pediatric CTA.

Interior photon counting computed tomography for quantification of coronary artery calcium: pre-clinical phantom study

Computed tomography (CT) is the reference method for cardiac imaging, but concerns have been raised regarding the radiation dose of CT examinations. Recently, photon counting detectors (PCDs) and interior tomography, in which the radiation beam is limited to the organ-of-interest, have been suggested for patient dose reduction. In this study, we investigated interior PCD-CT (iPCD-CT) for non-enhanced quantification of coronary artery calcium (CAC) using an anthropomorphic torso phantom and ex vivo coronary artery samples. We reconstructed the iPCD-CT measurements with filtered back projection (FBP), iterative total variation (TV) regularization, padded FBP, and adaptively detruncated FBP and adaptively detruncated TV. We compared the organ doses between conventional CT and iPCD-CT geometries, assessed the truncation and cupping artifacts with iPCD-CT, and evaluated the CAC quantification performance of iPCD-CT. With approximately the same effective dose between conventional CT geometry (0.30 mSv) and interior PCD-CT with 10.2 cm field-of-view (0.27 mSv), the organ dose of the heart was increased by 52.3% with interior PCD-CT when compared to CT. Conversely, the organ doses to peripheral and radiosensitive organs, such as the stomach (55.0% reduction), were often reduced with interior PCD-CT. FBP and TV did not sufficiently reduce the truncation artifact, whereas padded FBP and adaptively detruncated FBP and TV yielded satisfactory truncation artifact reduction. Notably, the adaptive detruncation algorithm reduced truncation artifacts effectively when it was combined with reconstruction detrending. With this approach, the CAC quantification accuracy was good, and the coronary artery disease grade reclassification rate was particularly low (5.6%). Thus, our results confirm that CAC quantification can be performed with the interior CT geometry, that the artifacts are effectively reduced with suitable interior reconstruction methods, and that interior tomography provides efficient patient dose reduction.

CHARACTERIZATION OF TWO CT SYSTEMS USING A CHANNELIZED HOTELLING OBSERVER AND NPS METRIC

We investigated the performances of two computed tomography (CT) systems produced by the same manufacturers (Somatom Flash and Edge Siemens) with different detector technologies (Ultrafast Ceramic and Stellar) and different generation of iterative reconstruction (IR) algorithms (SAFIRE and ADMIRE). A homemade phantom was scanned and the images were reconstructed with filtered back-projection (FBP) and IR algorithms. In terms of image quality, the performances of the systems were checked using the low-contrast detectability, evaluated by a Channelized Hotelling Observer (CHO), and the noise power spectrum (NPS). The analysis with CHO showed the best performance of Edge respect to Flash system for both FBP and IR algorithms. This better behavior, which reaches 20%, has been ascribed to the Stellar detector. From the NPS analysis, the noise reduction due to Stellar detector was 57%, moreover ADMIRE algorithm preserves a more traditional CT image texture appearance versus SAFIRE due to a lower NPS peak shift.

Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm

PURPOSE

To characterize the noise and spatial resolution properties of a commercially available deep learning-based computed tomography (CT) reconstruction algorithm.

METHODS

Two phantom experiments were performed. The first used a multisized image quality phantom (Mercury v3.0, Duke University) imaged at five radiation dose levels (CTDI_vol : 0.9, 1.2, 3.6, 7.0, and 22.3 mGy) with a fixed tube current technique on a commercial CT scanner (GE Revolution CT). Images were reconstructed with conventional (FBP), iterative (GE ASiR-V), and deep learning-based (GE True Fidelity) reconstruction algorithms. Noise power spectrum (NPS), high-contrast (air-polyethylene interface), and intermediate-contrast (water-polyethylene interface) task transfer functions (TTF) were measured for each dose level and phantom size and summarized in terms of average noise frequency (f_av ) and frequency at which the TTF was reduced to 50% (f_50% ), respectively. The second experiment used a custom phantom with low-contrast rods and lung texture sections for the assessment of low-contrast TTF and noise spatial distribution. The phantom was imaged at five dose levels (CTDI_vol : 1.0, 2.1, 3.0, 6.0, and 10.0 mGy) with 20 repeated scans at each dose, and images reconstructed with the same reconstruction algorithms. The local noise stationarity was assessed by generating spatial noise maps from the ensemble of repeated images and computing a noise inhomogeneity index, η , following AAPM TG233 methods. All measurements were compared among the algorithms.

RESULTS

Compared to FBP, noise magnitude was reduced on average (± one standard deviation) by 74 ± 6% and 68 ± 4% for ASiR-V (at "100%" setting) and True Fidelity (at "High" setting), respectively. The noise texture from ASiR-V had substantially lower noise frequency content with 55 ± 4% lower NPS f_av compared to FBP while True Fidelity had only marginally different noise frequency content with 9 ± 5% lower NPS f_av compared to FBP. Both ASiR-V and True Fidelity demonstrated locally nonstationary noise in a lung texture background at all radiation dose levels, with higher noise near high-contrast edges of vessels and lower noise in uniform regions. At the 1.0 mGy dose level η values were 314% and 271% higher in ASiR-V and True Fidelity compared to FBP, respectively. High-contrast spatial resolution was similar between all algorithms for all dose levels and phantom sizes (<3% difference in TTF f_50% ). Compared to FBP, low-contrast spatial resolution was lower for ASiR-V and True Fidelity with a reduction of TTF f_50% of up to 42% and 36%, respectively.

CONCLUSIONS

The deep learning-based CT reconstruction demonstrated a strong noise magnitude reduction compared to FBP while maintaining similar noise texture and high-contrast spatial resolution. However, the algorithm resulted in images with a locally nonstationary noise in lung textured backgrounds and had somewhat degraded low-contrast spatial resolution similar to what has been observed in currently available iterative reconstruction techniques.

Additional CTA-Subtraction Technique in Detection of Pulmonary Embolism-a Benefit for Patients or Only an Increase in Dose?

Latest advantages in computed tomography (CT) come with enhanced diagnostic imaging and also sophisticated dose reduction techniques. However, overall exposure to ionizing radiation of patients in Germany rises slightly, which is mainly based on the growing number of performed CT scans. Furthermore, new possibilities in modern imaging, including 4D scans or perfusion protocols, offer new medical insights but require additional scans.In this study, we reevaluated data sets from patients undergoing CT examinations because of suspected pulmonary embolism and compared doses and diagnostic results of the standard protocol to the additional modern CT subtraction technique. Two groups of single-blinded radiologists were provided with CT data sets from 50 patients. One group (G1) had access to full datasets including CT subtraction with perfusion map. The other group (G2) only evaluated conventional CT angiography. Results were compared to final clinical diagnosis. Dose length product (DLP) of CT angiography was compared to CT subtraction technique, which consists of an additional non-contrast-enhanced scan and perfusion map. Effective dose was calculated using a Monte Carlo simulation-based software tool (ImpactDose). Inter-rater agreement of both groups was strong in G1 with κ = .896 and minimal in G2 (κ = .307). Agreement to final diagnosis was strong in both groups (G1, κ = .848; G2, κ = .767). Doses applied using the CT subtraction technique were 34.8% higher than for CT angiography alone (G1 DLP 337.6 ± 171.3 mGy x cm; G2 DLP 220.2 ± 192.8 mGy x cm; p < .001). Calculated effective dose was therefore significantly higher for G1 (G1 4.82 ± 2.20 mSv; G2 3.04 ± 1.33 mSv; p < .001). Our results indicate a benefit of the CT subtraction technique for the detection of pulmonary embolisms in clinical routine, accompanied by an increase in the dose administered. Although CT protocols should always be applied carefully to specific clinical indications in order to maximize the potential for dose reduction and keep the administered dose as low as reasonably achievable, one should never lose sight of the diagnostic benefit, especially in vital clinical indications.

Improving Low-contrast Detectability and Noise Texture Pattern for Computed Tomography Using Iterative Reconstruction Accelerated with Machine Learning Method: A Phantom Study

RATIONALE AND OBJECTIVES

To evaluate the performance of iterative reconstruction (IR) and filtered back projection (FBP) images in terms of low-contrast detectability at different radiation doses, IR levels, and slice thickness using the mathematical model observer with a focus on low-contrast detectability.

MATERIALS AND METHODS

The CCT189 MITA CT IQ Low-Contrast Phantom was used and helical scans were performed using a 64-detector CT scanner. Tube voltage was set at 120 kVp and tube current was adjusted from 45 to 600 mA. Images were reconstructed at slice thicknesses of 0.625 and 5.0 mm with FBP and five types of iterative progressive reconstruction with visual modeling (IPV) algorithms. The noise power spectrum (NPS) and normalized NPS were calculated. To evaluate low-contrast detectability, the model observer with the channelized Hotelling observer model was applied using low-contrast modules in the phantom.

RESULTS

The NPS and normalized NPS for IPV images had similar curves as that for FBP images. At a slice thickness of 0.625 mm and equivalent radiation dose level, the mean improvement of low-contrast detectability for IPV images was 1.19-2.15-fold greater than FBP images with corresponding noise reduction levels. At equivalent noise levels of 5.0-8.0 HU, low-contrast detectability of the IPVstd2 to IPVstr2 images as almost the same or better than that of the FBP images. However, the detectability of the IPVstr4 image was lower than that of the FBP image (p = 0.02).

CONCLUSION

Low-contrast detectability of the IPV images was improved with a similar normalized NPS as with FBP images. Furthermore, a radiation reduction of >50% was achieved for the IPV images, while maintaining similar low-contrast detectability.

A Task-dependent Investigation on Dose and Texture in CT Image Reconstruction

Localizing and characterizing clinically-significant lung nodules, a potential precursor to lung cancer, at the lowest achievable radiation dose is demanded to minimize the stochastic radiation effects from x-ray computed tomography (CT). A minimal dose level is heavily dependent on the image reconstruction algorithms and clinical task, in which the tissue texture always plays an important role. This study aims to investigate the dependence through a task-based evaluation at multiple dose levels and variable textures in reconstructions with prospective patient studies. 133 patients with a suspicious pulmonary nodule scheduled for biopsy were recruited and the data was acquired at120kVp with three different dose levels of 100, 40 and 20mAs. Three reconstruction algorithms were implemented: analytical filtered back-projection (FBP) with optimal noise filtering; statistical Markov random field (MRF) model with optimal Huber weighting (MRF-H) for piecewise smooth reconstruction; and tissue-specific texture model (MRF-T) for texture preserved statistical reconstruction. Experienced thoracic radiologists reviewed and scored all images at random, blind to the CT dose and reconstruction algorithms. The radiologists identified the nodules in each image including the 133 biopsy target nodules and 66 other non-target nodules. For target nodule characterization, only MRF-T at 40mAs showed no statistically significant difference from FBP at 100mAs. For localizing both the target nodules and the non-target nodules, some as small as 3mm, MRF-T at 40 and 20mAs levels showed no statistically significant difference from FBP at 100mAs, respectively. MRF-H and FBP at 40 and 20mAs levels performed statistically differently from FBP at 100mAs. This investigation concluded that (1) the textures in the MRF-T reconstructions improves both the tasks of localizing and characterizing nodules at low dose CT and (2) the task of characterizing nodules is more challenging than the task of localizing nodules and needs more dose or enhanced textures from reconstruction.

Impact of 80 kVp with iterative reconstruction algorithm and low-dose contrast medium on the image quality of craniocervical CT angiography

PURPOSE

To assess the image quality of 80-kVp craniocervical CT angiography (CCCTA) protocol combined with adaptive statistical iterative reconstruction-V (ASIR-V) and low-dose contrast medium (CM).

METHODS

A total of 119 patients were randomly divided into three groups. For group A, 120-kVp protocol was followed with 60 ml CM and filtered back projection; for group B, 80-kVp protocol with 60 ml CM and ASIR-V; and for group C, 80-kVp protocol with 45 ml CM and ASIR-V. Both subjective and objective image quality and radiation doses were evaluated.

RESULTS

Arterial attenuation, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the head, neck, and shoulder regions were significantly higher in groups B and C compared with group A. Group C yielded significantly better subjective image quality than that observed in groups A and B (both p < .05). As compared with group A, effective radiation dose and the iodine load of group C were reduced by 51.4% and 25%, respectively.

CONCLUSIONS

The CCCTA protocol with 80 kVp, ASIR-V, and 45 ml of CM injected at 3 ml/s significantly reduced the radiation dose, iodine load, and iodine delivery rate while providing better subjective and objective image quality, including higher arterial enhancement and a higher SNR and CNR compared with the 120-kVp protocol.

What scans we will read: imaging instrumentation trends in clinical oncology

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non-invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/CT), advanced MRI, optical or ultrasound imaging.This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and, then point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now.Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by advances in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as "data", and - through the wider adoption of advanced analysis, including machine learning approaches and a "big data" concept - move to the next stage of non-invasive tumour phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi-dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging.

Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges

Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic radiology, are under active investigation with deep learning technology, which has shown promising performance in various tasks, including detection, classification, segmentation, and image synthesis, outperforming conventional methods and suggesting its potential for clinical implementation. However, the implementation of deep learning in daily clinical practice is in its infancy and facing several challenges, such as its limited ability to explain the output results, uncertain benefits regarding patient outcomes, and incomplete integration in daily workflow. In this review article, we will introduce the potential clinical applications of deep learning technology in thoracic radiology and discuss several challenges for its implementation in daily clinical practice.