Low-Dose Pelvic Computed Tomography Using Adaptive Iterative Dose Reduction 3-Dimensional Algorithm: A Phantom Study

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.

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

Purpose

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

Methods

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

Results

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

Conclusion

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

Graphical abstract

Comparison of CT image quality between the AIDR 3D and FIRST iterative reconstruction algorithms: an assessment based on phantom measurements and clinical images

Modern CT iterative reconstruction algorithms are transitioning from a statistical-based to model-based approach. However, increasing complexity does not ensure improved image quality for all indications, and thorough characterization of new algorithms is important to understand their potential clinical impacts. This study performs both quantitative and qualitative analyses of image quality to compare Canon's statistical-based Adaptive Iterative Dose Reduction 3D (AIDR 3D) algorithm to its model-based algorithm, Forward-projected model-based Iterative Reconstruction SoluTion(FIRST). A phantom was used to measure the task-specific modulation transfer function (MTF_Task), the noise power spectrum (NPS), and the low-contrast object-specific CNR (CNR_LO) for each algorithm using three dose levels and the convolution algorithm (kernel) appropriate for abdomen, lung, and brain imaging. Additionally, MTF_Taskwas measured at four contrast levels, and CNR_LOwas measured for two object sizes. Lastly, three radiologists participated in a preference study to compare clinical image quality for three study types: non-contrast abdomen, pulmonary embolism (PE), and lung screening. Nine questions related to the appearance of anatomical features or image quality characteristics were scored for twenty exams of each type. The behavior of both algorithms depended strongly on the kernel selected. Phantom measurements suggest that FIRST should be beneficial over AIDR 3D for abdomen imaging, but do not suggest a clear overall benefit to FIRST for lung or brain imaging; metrics suggest performance may be equivalent to or slightly favor AIDR 3D, depending on the size of the object being imaged and whether spatial resolution or low-contrast resolution is more important for the task at hand. Overall, radiologists strongly preferred AIDR 3D for lung screening, slightly preferred AIDR 3D for non-contrast abdomen, and had no preference for PE. FIRST was superior for the reduction of metal artifacts. Radiologist preference may be influenced by changes to noise texture.

Reduction of the radiation dose and the amount of contrast material in hepatic dynamic CT using low tube voltage and adaptive iterative dose reduction 3-dimensional

The purpose of this study was to prospectively evaluate the image quality and the diagnostic ability of low tube voltage and reduced contrast material dose hepatic dynamic computed tomography (CT) reconstructed with adaptive iterative dose reduction 3-dimensional (AIDR 3D).Eighty-nine patients underwent hepatic dynamic CT using one of the 2 protocols: tube voltage of 120 kVp, contrast dose of 600 mgI/kg, and filtered back projection in Protocol A (n = 46), and tube voltage of 100 kVp, contrast dose of 500 mgI/kg, and AIDR 3D in Protocol B (n = 43). The volume CT dose index (CTDIvol) and size-specific dose estimates (SSDEs) were compared between the 2 groups. Objective image noise and tumor to liver contrast-to-noise ratio (CNR) were also compared. Three radiologists independently reviewed image quality. The jackknife alternative free-response receiver-operating characteristic (JAFROC) analysis was performed to compare diagnostic performance.The mean CTDIvol and SSDE of Protocol B (14.3 and 20.2, respectively) were significantly lower than those of Protocol A (22.1 and 31.4, P < .001). There were no significant differences in either objective image noise or CNR. In the qualitative analysis, 2 readers assigned significant lower scores to images of Protocol B for at least one of the 3 phases regarding overall image quality (P < .05). There was no significant difference in the JAFROC1 figure of merit between protocols.Low tube voltage CT with AIDR 3D yielded a reduction in radiation dose and in the amount of contrast material while maintaining diagnostic performance.

Prevalence and awareness of sacroiliac joint alterations on lumbar spine CT in low back pain patients younger than 40 years

Background Computed tomography (CT) examinations of the lumbar spine are commonly performed in patients aged ≤40 years due to low back pain (LBP). Purpose To investigate the prevalence and awareness of radiologists for the presence of structural post-inflammatory/other sacroiliac joint (SIJ) alterations on lumbar spine CTs of young patients with LBP. Material and Methods A total of 484 lumbar spine CT examinations (272 men, 212 women; average age, 31 years; age range, 18-40 years) of patients with LBP in which the entire SIJs were visualized were retrospectively reviewed. SIJs were scored (consensus) by two senior radiologists (study reading) for the presence of post-inflammatory structural SIJ findings or other SIJs alterations. The original reports were compared to the study reading. Fifty CT examinations were re-evaluated for reliability assessment (intra-class correlation coefficient [ICC]). Results A total of 150 (31%) abnormal SIJ examinations were registered (ICC: r = 0.7-0.8; P < 0.0001): suspected sacroiliitis = 50 (10.2%); definite sacroiliitis = 16 (3.3%); osteitis-condensans-ilii = 38 (7.8%); diffuse idiopathic skeletal hyperostosis = 24 (5%); degenerative changes = 22 (4.5%); accessory SIJ = 22 (4.5%); and tumor = 1. The SIJs were referenced 39 times (8.0%) in the original readings: pathological findings (n = 15); and normal SIJ (n = 24). Total diagnostic accuracy for these reports only and for the entire readings were 49% and 69%, respectively, and 13% and 1.3%, respectively, for the pathological findings. Conclusion Sacroiliitis and other SIJ alterations are prevalent in young individuals with LBP, albeit, the majority of these alterations are not recognized nor reported by senior radiologists thus may delay efficacious treatment.