Renovascular CT: comparison between adaptive statistical iterative reconstruction and model-based iterative reconstruction

Incoherent nonlinear deconvolution using an iterative algorithm for recovering limited-support images from blurred digital photographs

Recovering original images from blurred images is a challenging task. We propose a new deconvolution method termed incoherent nonlinear deconvolution using an iterative algorithm (INDIA). Two inputs are introduced into the algorithm: one is a random or engineered point spread function of the scattering system, and the other is a blurred or distorted image of some object produced from this system. The two functions are Fourier transformed, and their phase distributions are processed independently of their magnitude. The algorithm yields the image of the original object with reduced blurring effects. The results of the new method are compared to two linear and two nonlinear algorithms under various types of blurs. The root mean square error and structural similarity between the original and recovered images are chosen as the comparison criteria between the five different algorithms. The simulation and experimental results confirm the superior performance of INDIA compared to the other tested deblurring methods.

Deep-learning image reconstruction for 80-kVp pancreatic CT protocol: Comparison of image quality and pancreatic ductal adenocarcinoma visibility with hybrid-iterative reconstruction

PURPOSE

To evaluate the image quality and visibility of pancreatic ductal adenocarcinoma (PDAC) in 80-kVp pancreatic CT protocol and compare them between hybrid-iterative reconstruction (IR) and deep-learning image reconstruction (DLIR) algorithms.

METHOD

A total of 56 patients who underwent 80-kVp pancreatic protocol CT for pancreatic disease evaluation from January 2022 to July 2022 were included in this retrospective study. Among them, 20 PDACs were observed. The CT raw data were reconstructed using 40% adaptive statistical IR-Veo (hybrid-IR group) and DLIR at medium- and high-strength levels (DLIR-M and DLIR-H groups, respectively). The CT attenuation of the abdominal aorta, pancreas, and PDAC (if present) at the pancreatic phase and those of the portal vein and liver at the portal venous phase; background noise; signal-to-noise ratio (SNR) of these anatomical structures; and tumor-to-pancreas contrast-to-noise ratio (CNR) were calculated. The confidence scores for the image noise, overall image quality, and visibility of PDAC were qualitatively assigned using a five-point scale. Quantitative and qualitative parameters were compared among the three groups using Friedman test.

RESULTS

The CT attenuation of all anatomical structures were comparable among the three groups (P = .26-.86), except that of the pancreas (P = .001). Background noise was lower (P <.001) and SNRs (P <.001) and tumor-to-pancreas CNR (P <.001) were higher in the DLIR-H group than those in the other two groups. The image noise, overall image quality, and visibility of PDAC were better in the DLIR-H group than in the other two groups (P <.001-.003).

CONCLUSION

In 80-kVp pancreatic CT protocol, DLIR at a high-strength level improved image quality and visibility of PDAC.

Deep-learning image-reconstruction algorithm for dual-energy CT angiography with reduced iodine dose: preliminary results

AIM

To evaluate the computed tomography (CT) attenuation values, background noise, arterial depiction, and image quality in whole-body dual-energy CT angiography (DECTA) at 40 keV with a reduced iodine dose using deep-learning image reconstruction (DLIR) and compare them with hybrid iterative reconstruction (IR).

MATERIAL AND METHODS

Whole-body DECTA with a reduced iodine dose (200 mg iodine/kg) was performed in 22 patients, and DECTA data at 1.25-mm section thickness with 50% overlap were reconstructed at 40 keV using 40% adaptive statistical iterative reconstruction with Veo (hybrid-IR group), and DLIR at medium and high levels (DLIR-M and DLIR-H groups). The CT attenuation values of the thoracic and abdominal aortas and iliac artery and background noise were measured. Arterial depiction and image quality on axial, multiplanar reformatted (MPR), and volume-rendered (VR) images were assessed by two readers. Quantitative and qualitative parameters were compared between the hybrid-IR, DLIR-M, and DLIR-H groups.

RESULTS

The vascular CT attenuation values were almost comparable between the three groups (p=0.013-0.97), but the background noise was significantly lower in the DLIR-H group than in the hybrid-IR and DLIR-M groups (p<0.001). The arterial depictions on axial and MPR images and in almost all arteries on VR images were comparable (p=0.14-1). The image quality of axial, MPR, and VR images was significantly better in the DLIR-H group (p<0.001-0.015).

CONCLUSION

DLIR significantly reduced background noise and improved image quality in DECTA at 40 keV compared with hybrid-IR, while maintaining the arterial depiction in almost all arteries.

Chest Computed Tomography Images in Neonatal Bronchial Pneumonia under the Adaptive Statistical Iterative Reconstruction Algorithm

This study was to explore the application value of chest computed tomography (CT) images processed by artificial intelligence (AI) algorithms in the diagnosis of neonatal bronchial pneumonia (NBP). The AI adaptive statistical iterative reconstruction (ASiR) algorithm was adopted to reconstruct the chest CT image to compare and analyze the effect of the reconstruction of CT image under the ASiR algorithm under different preweight and postweight values based on the objective measurement and subjective evaluation. 85 neonates with pneumonia treated in hospital from September 1, 2015, to July 1, 2020, were selected as the research objects to analyze their CT imaging characteristics. Subsequently, the peripheral blood of healthy neonates during the same period was collected, and the levels of C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) were detected. The efficiency of CT examination, CRP, ESR, and combined examination in the diagnosis of NBP was analyzed. The results showed that the subjective quality score, lung window subjective score, and mediastinal window subjective score were the highest after CT image reconstruction when the preweight value of the ASiR algorithm was 50%. After treatment, 79 NBP cases (92.9%) showed ground-glass features in CT images. Compared with the healthy neonates, the levels of CRP and ESR in the peripheral blood of neonates with bronchial pneumonia were much lower (P < 0.05). The accuracy rates of CT examination, CRP examination, ESR examination, CRP + ESR examination, and CRP + ESR + CT examination for the diagnosis of NBP were 80.7%, 75.3%, 75.1%, 80.3%, and 98.6%, respectively. CT technology based on AI algorithm showed high clinical application value in the feature analysis of NBP.

Deep learning image reconstruction for pancreatic low-dose computed tomography: comparison with hybrid iterative reconstruction

PURPOSE

To evaluate image quality, image noise, and conspicuity of pancreatic ductal adenocarcinoma (PDAC) in pancreatic low-dose computed tomography (LDCT) reconstructed using deep learning image reconstruction (DLIR) and compare with those of images reconstructed using hybrid iterative reconstruction (IR).

METHODS

Our institutional review board approved this prospective study. Written informed consent was obtained from all patients. Twenty-eight consecutive patients with PDAC undergoing chemotherapy (14 men and 14 women; mean age, 68.4 years) underwent pancreatic LDCT for therapy evaluation. The LDCT images were reconstructed using 40% adaptive statistical iterative reconstruction-Veo (hybrid-IR) and DLIR at medium and high levels (DLIR-M and DLIR-H). The image noise, diagnostic acceptability, and conspicuity of PDAC were qualitatively assessed using a 5-point scale. CT numbers of the abdominal aorta, portal vein, pancreas, PDAC, background noise, signal-to-noise ratio (SNR) of the anatomical structures, and tumor-to-pancreas contrast-to-noise ratio (CNR) were calculated. Qualitative and quantitative parameters were compared between the hybrid-IR, DLIR-M, and DLIR-H images.

RESULTS

CT dose-index volumes and dose-length product in pancreatic LDCT were 2.3 ± 1.0 mGy and 74.9 ± 37.0 mGy•cm, respectively. The image noise, diagnostic acceptability, and conspicuity of PDAC were significantly better in DLIR-H than those in hybrid-IR and DLIR-M (all P < 0.001). The background noise was significantly lower in the DLIR-H images (P < 0.001) and resulted in improved SNRs (P < 0.001) and CNR (P < 0.001) compared with those in the hybrid-IR and DLIR-M images.

CONCLUSION

DLIR significantly reduced image noise and improved image quality in pancreatic LDCT images compared with hybrid-IR.

Deep learning image reconstruction algorithm for pancreatic protocol dual-energy computed tomography: image quality and quantification of iodine concentration

OBJECTIVES

To evaluate the image quality and iodine concentration (IC) measurements in pancreatic protocol dual-energy computed tomography (DECT) reconstructed using deep learning image reconstruction (DLIR) and compare them with those of images reconstructed using hybrid iterative reconstruction (IR).

METHODS

The local institutional review board approved this prospective study. Written informed consent was obtained from all participants. Thirty consecutive participants with pancreatic cancer (PC) underwent pancreatic protocol DECT for initial evaluation. DECT data were reconstructed at 70 keV using 40% adaptive statistical iterative reconstruction-Veo (hybrid-IR) and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The diagnostic acceptability and conspicuity of PC were qualitatively assessed using a 5-point scale. IC values of the abdominal aorta, pancreas, PC, liver, and portal vein; standard deviation (SD); and coefficient of variation (CV) were calculated. Qualitative and quantitative parameters were compared between the hybrid-IR, DLIR-M, and DLIR-H groups.

RESULTS

The diagnostic acceptability and conspicuity of PC were significantly better in the DLIR-M group compared with those in the other groups (p < .001-.001). The IC values of the anatomical structures were almost comparable between the three groups (p = .001-.9). The SD of IC values was significantly lower in the DLIR-H group (p < .001) and resulted in the lowest CV (p < .001-.002) compared with those in the hybrid-IR and DLIR-M groups.

CONCLUSIONS

DLIR could significantly improve image quality and reduce the variability of IC values than could hybrid-IR.

KEY POINTS

Image quality and conspicuity of pancreatic cancer were the best in DLIR-M. DLIR significantly reduced background noise and improved SNR and CNR. The variability of iodine concentration was reduced in DLIR.

Simulated twin-phase pancreatic CT generated using single portal venous phase dual-energy CT acquisition in pancreatic ductal adenocarcinoma

PURPOSE

To evaluate the diagnostic performance of a simulated twin-phase pancreatic protocol CT generated from a single portal venous phase (PVP) dual-energy CT (DECT) acquisition in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

In this retrospective study, we included 63 patients with PDAC who underwent pancreatic protocol (pancreatic phase [PP] and PVP) DECT. Two data sets were created from this original acquisition-(1) Standard protocol (50 keV PP/65 keV PVP) and (2) Simulated protocol (40 keV/65 keV PVP). Using a 5-point scale, three readers scored image quality, tumor conspicuity, and arterial involvement by the PDAC. Signal-to-noise ratio (SNR) of the pancreas and tumor-to-pancreas contrast-to-noise ratio (CNR) were calculated. Qualitative scores, quantitative parameters, and radiation dose were compared between standard and simulated protocols.

RESULTS

No significant difference in detection rate of PDAC was seen between the standard (58/63, 92.1%) and simulated protocols (56/63, 88.9%) (P = 0.76). Subjective scoring for arterial involvement for celiac (P = 0.86), superior mesenteric (P = 0.88), splenic (P = 0.86), common hepatic (P = 0.52), gastroduodenal (P = 0.95), first jejunal (P = 0.48) arteries, and aorta (P = 1.00) were comparable between two protocols. The image quality (P = 0.14), the SNR of the pancreas (P = 0.15), and CNR (P = 0.54) were comparable between two protocols. The projected mean dose-length product (DLP) (629.6 ± 148.3 mGy cm) in the simulated protocol showed a 44% reduction in radiation dose compared to the standard protocol (mean DLP, 1123.3 ± 268.9 mGy cm) (P < 0.0001).

CONCLUSIONS

Low keV images generated from a PVP DECT acquisition allows creation of a twin-phase pancreatic protocol CT with comparable diagnostic accuracy for detecting PDAC with significant reduction in radiation dose. Reduced radiation dose is desirable in surveillance and screening for pancreatic diseases.

Low keV portal venous phase as a surrogate for pancreatic phase in a pancreatic protocol dual-energy CT: feasibility, image quality, and lesion conspicuity

OBJECTIVE

To assess the feasibility of a proposed pancreatic protocol CT generated from portal-venous phase (PVP) dual-energy CT (DECT) acquisition and its impact on image quality, lesion conspicuity, and arterial visualization/involvement.

METHODS

We included 111 patients (mean age, 66.8 years) who underwent pancreatic protocol DECT (pancreatic phase, PP, and PVP). The original DECT acquisition was used to create two data sets-standard protocol (50 keV PP/65 keV PVP) and proposed protocol (40 keV/65 keV PVP). Three reviewers evaluated the two data sets for image quality, lesion conspicuity, and arterial visualization/involvement using a 5-point scale. The signal-to-noise ratio (SNR) of pancreas and lesion-to-pancreas contrast-to-noise ratio (CNR) was calculated. Qualitative scores, quantitative parameters, and dose-length product (DLP) were compared between standard and proposed protocols.

RESULTS

The image quality, SNR of pancreas, and lesion-to-pancreas CNR of the standard and proposed protocol were comparable (p = 0.11-1.00). Lesion conspicuity was comparable between the standard and proposed protocols for pancreatic ductal adenocarcinoma (p = 0.55) and pancreatic cysts (p = 0.28). The visualization of larger arteries and arterial involvement were comparable between the two protocols (p = 0.056-1.00) while the scores were higher for smaller vessels in the standard protocol (p < 0.0001-0.0015). DLP of the proposed protocol (670.4 mGy·cm) showed a projected 42% reduction than the standard protocol (1145.9 mGy·cm) (p < 0.0001).

CONCLUSION

Pancreatic protocol CT generated from a single PVP DECT acquisition is feasible and could potentially be an alternative to the standard pancreatic protocol with PP and PVP.

KEY POINTS

• The lesion conspicuity for focal pancreatic lesions was comparable between the proposed protocol and standard dual-phase pancreatic protocol CT. • Qualitative and quantitative image assessments were almost comparable between two protocols. • The radiation dose of a proposed protocol showed a projected 42% reduction from the conventional protocol.

Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection

OBJECTIVES

To evaluate image quality and lesion detection capabilities of low-dose (LD) portal venous phase whole-body computed tomography (CT) using deep learning image reconstruction (DLIR).

METHODS

The study cohort of 59 consecutive patients (mean age, 67.2 years) who underwent whole-body LD CT and a prior standard-dose (SD) CT reconstructed with hybrid iterative reconstruction (SD-IR) within one year for surveillance of malignancy were assessed. The LD CT images were reconstructed with hybrid iterative reconstruction of 40% (LD-IR) and DLIR (LD-DLIR). The radiologists independently evaluated image quality (5-point scale) and lesion detection. Attenuation values in Hounsfield units (HU) of the liver, pancreas, spleen, abdominal aorta, and portal vein; the background noise and signal-to-noise ratio (SNR) of the liver, pancreas, and spleen were calculated. Qualitative and quantitative parameters were compared between the SD-IR, LD-IR, and LD-DLIR images. The CT dose-index volumes (CTDI_vol) and dose-length product (DLP) were compared between SD and LD scans.

RESULTS

The image quality and lesion detection rate of the LD-DLIR was comparable to the SD-IR. The image quality was significantly better in SD-IR than in LD-IR (p < 0.017). The attenuation values of all anatomical structures were comparable between the SD-IR and LD-DLIR (p = 0.28-0.96). However, background noise was significantly lower in the LD-DLIR (p < 0.001) and resulted in improved SNRs (p < 0.001) compared to the SD-IR and LD-IR images. The mean CTDI_vol and DLP were significantly lower in the LD (2.9 mGy and 216.2 mGy•cm) than in the SD (13.5 mGy and 1011.6 mGy•cm) (p < 0.0001).

CONCLUSION

LD CT images reconstructed with DLIR enable radiation dose reduction of >75% while maintaining image quality and lesion detection rate and superior SNR in comparison to SD-IR.

ADVANCES IN KNOWLEDGE

Deep learning image reconstruction algorithm enables around 80% reduction in radiation dose while maintaining the image quality and lesion detection compared to standard-dose whole-body CT.

Diagnostic Value of Model-Based Iterative Reconstruction Combined with a Metal Artifact Reduction Algorithm during CT of the Oral Cavity

BACKGROUND AND PURPOSE

Metal artifacts reduce the quality of CT images and increase the difficulty of interpretation. This study compared the ability of model-based iterative reconstruction and hybrid iterative reconstruction to improve CT image quality in patients with metallic dental artifacts when both techniques were combined with a metal artifact reduction algorithm.

MATERIALS AND METHODS

This retrospective clinical study included 40 patients (men, 31; women, 9; mean age, 62.9 ± 12.3 years) with oral and oropharyngeal cancer who had metallic dental fillings or implants and underwent contrast-enhanced ultra-high-resolution CT of the neck. Axial CT images were reconstructed using hybrid iterative reconstruction and model-based iterative reconstruction, and the metal artifact reduction algorithm was applied to all images. Finally, hybrid iterative reconstruction + metal artifact reduction algorithms and model-based iterative reconstruction + metal artifact reduction algorithm data were obtained. In the quantitative analysis, SDs were measured in ROIs over the apex of the tongue (metal artifacts) and nuchal muscle (no metal artifacts) and were used to calculate the metal artifact indexes. In a qualitative analysis, 3 radiologists blinded to the patients' conditions assessed the image-quality scores of metal artifact reduction and structural depictions.

RESULTS

Hybrid iterative reconstruction + metal artifact reduction algorithms and model-based iterative reconstruction + metal artifact reduction algorithms yielded significantly different metal artifact indexes of 82.2 and 73.6, respectively (95% CI, 2.6-14.7; P < .01). The latter algorithms resulted in significant reduction in metal artifacts and significantly improved structural depictions(P < .01).

CONCLUSIONS

Model-based iterative reconstruction + metal artifact reduction algorithms significantly reduced the artifacts and improved the image quality of structural depictions on neck CT images.

Imaging studies in pediatric fibromuscular dysplasia (FMD): a single-center experience

BACKGROUND

Fibromuscular dysplasia (FMD) is a non-inflammatory vascular disease that in children unlike in adults shows no sex predilection. FMD is often underdiagnosed, and its pathophysiology is unclear. Delayed diagnosis may lead to refractory hypertension and decreases the chance of successful treatment. Doppler ultrasound (US), magnetic resonance angiography (MRA), computed tomography angiography (CTA), and catheter-based angiography (angiography) are currently used to help make a clinicoradiological diagnosis of FMD. The main aim of the study was to compare the efficacy of imaging modalities which can allow for earlier and improved detection. Furthermore, an anatomical mapping of the location of lesions can help determine the best treatment modalities.

METHODS

All patients with non-syndromic non-inflammatory renovascular hypertension were recruited from the Nephrology Department at the Children's Hospital of Philadelphia (CHOP) and enrolled in the U.S. FMD Registry maintained at the University of Michigan. Clinical presentation and imaging findings on US, CT, and MRI of children diagnosed with FMD were evaluated.

RESULTS

Mean age at diagnosis was 7 ± 4.9 years (4 months-17 years). Family history of hypertension (HTN) (52%), FMD (8.7%), Caucasian (60%), headache (48%), and HTN (80%) were the most prevalent symptom and sign at presentation. Bruits were 100% specific for renal artery stenosis (RAS) diagnosis but were heard in the minority of patients (3 patients, 12%). FMD was mainly unifocal within a single site (68%) or multiple sites (28%) and involved the main or first order renal branch in about 68% of children. Isolated distal lesions beyond the second order branches were found in about 25% of children. US imaging was significantly less sensitive than angiography (28%, p = 0.003). MRA had a better sensitivity (62.5%, p = 0.3) than US. Overall, CTA had the best sensitivity (84.2%, p = 0.4) compared to angiography; however, only angiography showed distal vessel disease.

CONCLUSIONS

Limitations of the study include the sample size and biases-only patients diagnosed with FMD were included in this study and most patients were referred to a pediatric nephrologist for unexplained hypertension. Angiography should be performed as part of the initial work-up of any child suspected of having renovascular FMD, regardless of the findings seen on US, MRA, or CTA.