CT of the pancreas: comparison of image quality and pancreatic duct depiction among model-based iterative, adaptive statistical iterative, and filtered back projection reconstruction techniques

An image reconstruction method for transmission computed tomography with the constraint of the linear attenuation coefficients

For the reconstructed image of transmission computed tomography, the linear attenuation coefficients of the diagnosed object may improve the image quality by adding additional constraint besides the projection data. In the present work, an image reconstruction method with the constraint of the linear attenuation coefficients is developed and two models including a classical numerical Shepp-Logan model and a Monte Carlo model are used to show the corresponding benefits. The results indicate that the number of the projection angles is potentially decreased to 1/3 of itself while the quality of the reconstructed image is not deteriorated.

Image Quality and Lesion Detectability of Pancreatic Phase Thin-Slice Computed Tomography Images With a Deep Learning-Based Reconstruction Algorithm

OBJECTIVE

To evaluate the image quality and lesion detectability of pancreatic phase thin-slice computed tomography (CT) images reconstructed with a deep learning-based reconstruction (DLR) algorithm compared with filtered-back projection (FBP) and hybrid iterative reconstruction (IR) algorithms.

METHODS

Fifty-three patients who underwent dynamic contrast-enhanced CT including pancreatic phase were enrolled in this retrospective study. Pancreatic phase thin-slice (0.625 mm) images were reconstructed with each FBP, hybrid IR, and DLR. Objective image quality and signal-to-noise ratio of the pancreatic parenchyma, and contrast-to-noise ratio of pancreatic lesions were compared between the 3 reconstruction algorithms. Two radiologists independently assessed the image quality of all images. The diagnostic performance for the detection of pancreatic lesions was compared among the reconstruction algorithms using jackknife alternative free-response receiver operating characteristic analysis.

RESULTS

Deep learning-based reconstruction resulted in significantly lower image noise and higher signal-to-noise ratio and contrast-to-noise ratio than hybrid IR and FBP ( P < 0.001). Deep learning-based reconstruction also yielded significantly higher visual scores than hybrid IR and FBP ( P < 0.01). The diagnostic performance of DLR for detecting pancreatic lesions was highest for both readers, although a significant difference was found only between DLR and FBP in one reader ( P = 0.02).

CONCLUSIONS

Deep learning-based reconstruction showed improved objective and subjective image quality of pancreatic phase thin-slice CT relative to other reconstruction algorithms and has potential for improving lesion detectability.

New advances in CT imaging of pancreas diseases: a narrative review

Computed tomography (CT) plays a pivotal role as a diagnostic tool in many diagnostic and diffuse pancreatic diseases. One of the major limits of CT is related to the radiation exposure of young patients undergoing repeated examinations. Besides the standard CT protocol, the most recent technological advances, such as low-voltage acquisitions with high performance X-ray tubes and iterative reconstructions, allow for significant optimization of the protocol with dose reduction. The variety of CT tools are further expanded by the introduction of dual energy: the production of energy-selective images (i.e., virtual monochromatic images) improves the image contrast and lesion detection while the material-selective images (e.g., iodine maps or virtual unenhanced images) are valuable for lesion detection and dose reduction. The perfusion techniques provide diagnostic and prognostic information lesion and parenchymal vascularization and interstitium. Both dual energy and perfusion CT have the potential for pushing the limits of conventional CT from morphological evaluation to quantitative imaging applied to inflammatory and oncological diseases. Advances in post-processing of CT images, such as pancreatic volumetry, texture analysis and radiomics provide relevant information for pancreatic function but also for the diagnosis, management and prognosis of pancreatic neoplasms. Artificial intelligence is promising for optimization of the workflow in qualitative and quantitative analyses. Finally, basic concepts on the role of imaging on screening of pancreatic diseases will be provided.

Advanced imaging techniques for chronic pancreatitis

MRI and MRCP play an important role in the diagnosis of chronic pancreatitis (CP) by imaging pancreatic parenchyma and ducts. MRI/MRCP is more widely used than computed tomography (CT) for mild to moderate CP due to its increased sensitivity for pancreatic ductal and gland changes; however, it does not detect the calcifications seen in advanced CP. Quantitative MR imaging offers potential advantages over conventional qualitative imaging, including simplicity of analysis, quantitative and population-based comparisons, and more direct interpretation of detected changes. These techniques may provide quantitative metrics for determining the presence and severity of acinar cell loss and aid in the diagnosis of chronic pancreatitis. Given the fact that the parenchymal changes of CP precede the ductal involvement, there would be a significant benefit from developing MRI/MRCP-based, more robust diagnostic criteria combining ductal and parenchymal findings. Among cross-sectional imaging modalities, multi-detector CT (MDCT) has been a cornerstone for evaluating chronic pancreatitis (CP) since it is ubiquitous, assesses primary disease process, identifies complications like pseudocyst or vascular thrombosis with high sensitivity and specificity, guides therapeutic management decisions, and provides images with isotropic resolution within seconds. Conventional MDCT has certain limitations and is reserved to provide predominantly morphological (e.g., calcifications, organ size) rather than functional information. The emerging applications of radiomics and artificial intelligence are poised to extend the current capabilities of MDCT. In this review article, we will review advanced imaging techniques by MRI, MRCP, CT, and ultrasound.

Thin-slice brain CT with iterative model reconstruction algorithm for small lacunar lesions detection: Image quality and diagnostic accuracy evaluation

This study was aimed to evaluate the image quality and lacunar lesion detection of thin-slice brain computed tomography (CT) images with different reconstruction algorithms, including filtered back projection (FBP), hybrid iterative reconstruction (HIR), and iterative model reconstruction (IMR) by comparison of routine slice images with FBP reconstruction. Sixty-one patients underwent noncontrast brain CT and images were reconstructed with a routine slice of 5.0 mm by FBP and thin slice of 1.0 mm by IMR, HIR, and FBP algorithms, respectively. Objective analyses included CT attenuation, noise, artifacts index of posterior cranial fossa, and contrast-to-noise ratio (CNR). Subjective analyses were performed according to overall image quality using a 5-point scale [1 (unacceptable) to 5 (excellent)]. In addition, lacunar lesion detection was compared in images with different reconstruction settings among 26 patients with lacunar lesions, with magnetic resonance imaging (MRI) as reference.Thin-slice IMR images enabled the lowest noise, artifacts index, and the best CNR. Both IMR and HIR thin-slice images enabled better scores in subjective image quality than routine slice FBP images. Moreover, both thin-slice IMR and HIR images enabled higher sensitivity and positive predictive value (PPV) in lesion detection of 35-mm lacunar lesions compared with routine slice FBP images.Thin-slice IMR images improve image quality, meanwhile yield better detection of small lacunar lesions in brain CT compared with routine slice FBP images.

Model-based iterative reconstruction and adaptive statistical iterative reconstruction: dose-reduced CT for detecting pancreatic calcification

Background

Iterative reconstruction methods have attracted attention for reducing radiation doses in computed tomography (CT).

Purpose

To investigate the detectability of pancreatic calcification using dose-reduced CT reconstructed with model-based iterative construction (MBIR) and adaptive statistical iterative reconstruction (ASIR).

Material and Methods

This prospective study approved by Institutional Review Board included 85 patients (57 men, 28 women; mean age, 69.9 years; mean body weight, 61.2 kg). Unenhanced CT was performed three times with different radiation doses (reference-dose CT [RDCT], low-dose CT [LDCT], ultralow-dose CT [ULDCT]). From RDCT, LDCT, and ULDCT, images were reconstructed with filtered-back projection (R-FBP, used for establishing reference standard), ASIR (L-ASIR), and MBIR and ASIR (UL-MBIR and UL-ASIR), respectively. A lesion (pancreatic calcification) detection test was performed by two blinded radiologists with a five-point certainty level scale.

Results

Dose-length products of RDCT, LDCT, and ULDCT were 410, 97, and 36 mGy-cm, respectively. Nine patients had pancreatic calcification. The sensitivity for detecting pancreatic calcification with UL-MBIR was high (0.67–0.89) compared to L-ASIR or UL-ASIR (0.11–0.44), and a significant difference was seen between UL-MBIR and UL-ASIR for one reader (P = 0.014). The area under the receiver-operating characteristic curve for UL-MBIR (0.818–0.860) was comparable to that for L-ASIR (0.696–0.844). The specificity was lower with UL-MBIR (0.79–0.92) than with L-ASIR or UL-ASIR (0.96–0.99), and a significant difference was seen for one reader (P < 0.01).

Conclusion

In UL-MBIR, pancreatic calcification can be detected with high sensitivity, however, we should pay attention to the slightly lower specificity.

Feasibility Study of Radiation Dose Reduction in Adult Female Pelvic CT Scan with Low Tube-Voltage and Adaptive Statistical Iterative Reconstruction

OBJECTIVE

To evaluate image quality of female pelvic computed tomography (CT) scans reconstructed with the adaptive statistical iterative reconstruction (ASIR) technique combined with low tube-voltage and to explore the feasibility of its clinical application.

MATERIALS AND METHODS

Ninety-four patients were divided into two groups. The study group used 100 kVp, and images were reconstructed with 30%, 50%, 70%, and 90% ASIR. The control group used 120 kVp, and images were reconstructed with 30% ASIR. The noise index was 15 for the study group and 11 for the control group. The CT values and noise levels of different tissues were measured. The contrast to noise ratio (CNR) was calculated. A subjective evaluation was carried out by two experienced radiologists. The CT dose index volume (CTDIvol) was recorded.

RESULTS

A 44.7% reduction in CTDIvol was observed in the study group (8.18 ± 3.58 mGy) compared with that in the control group (14.78 ± 6.15 mGy). No significant differences were observed in the tissue noise levels and CNR values between the 70% ASIR group and the control group (p = 0.068-1.000). The subjective scores indicated that visibility of small structures, diagnostic confidence, and the overall image quality score in the 70% ASIR group was the best, and were similar to those in the control group (1.87 vs. 1.79, 1.26 vs. 1.28, and 4.53 vs. 4.57; p = 0.122-0.585). No significant difference in diagnostic accuracy was detected between the study group and the control group (42/47 vs. 43/47, p = 1.000).

CONCLUSION

Low tube-voltage combined with automatic tube current modulation and 70% ASIR allowed the low CT radiation dose to be reduced by 44.7% without losing image quality on female pelvic scan.

Improving head and neck CTA with hybrid and model-based iterative reconstruction techniques

AIM

To compare image quality of head and neck computed tomography angiography (CTA) reconstructed with filtered back projection (FBP), hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MIR) algorithms.

MATERIALS AND METHODS

The raw data of 34 studies were simultaneously reconstructed with FBP, HIR (iDose(4), Philips Healthcare, Best, the Netherlands), and with a prototype version of a MIR algorithm (IMR, Philips Healthcare). Objective (contrast-to-noise ratio [CNR], vascular contrast, automatic vessel analysis [AVA], stenosis grade) and subjective image quality (ranking at level of the circle of Willis, carotid bifurcation, and shoulder) of the five reconstructions were compared using repeated-measures analysis of variance (ANOVA) and post-hoc analysis.

RESULTS

Vascular contrast was significantly higher in both the circle of Willis and carotid bifurcation with both levels of MIR compared to the other reconstruction methods (all p<0.0001). The CNR was highest for high MIR, followed by low MIR, high HIR, mid HIR and FBP (p<0.001 except low MIR versus high HIR; p>0.33). AVA showed most complete carotids in both MIR-levels, followed by high HIR (p>0.08), mid HIR (p<0.023) and FBP (p<0.010), vertebral arteries completeness was similar (p=0.40 and p=0.06). Stenosis grade showed no significant differences (p=0.16). High HIR showed the best subjective image quality at the circle of Willis and carotid bifurcation level, followed by mid HIR. At shoulder level, low MIR and high HIR were ranked best, followed by high MIR.

CONCLUSION

Objectively, MIR significantly improved the overall image quality, reduced image noise, and improved automated vessel analysis, whereas FBP showed the lowest objective image quality. Subjectively, the highest level of HIR was considered superior at the level of the circle of Willis and the carotid bifurcation, and along with the lowest level of MIR for the origins of the neck arteries at shoulder level.