Experience With Iterative Reconstruction Techniques for Abdominopelvic Computed Tomography in Morbidly and Super Obese Patients

Optimizing computed tomography image reconstruction for focal hepatic lesions: Deep learning image reconstruction vs iterative reconstruction

Background

Deep learning image reconstruction (DLIR) is a novel computed tomography (CT) reconstruction technique that minimizes image noise, enhances image quality, and enables radiation dose reduction. This study aims to compare the diagnostic performance of DLIR and iterative reconstruction (IR) in the evaluation of focal hepatic lesions.

Methods

We conducted a retrospective study of 216 focal hepatic lesions in 109 adult participants who underwent abdominal CT scanning at our institution. We used DLIR (low, medium, and high strength) and IR (0 %, 10 %, 20 %, and 30 %) techniques for image reconstruction. Four experienced abdominal radiologists independently evaluated focal hepatic lesions based on five qualitative aspects (lesion detectability, lesion border, diagnostic confidence level, image artifact, and overall image quality). Quantitatively, we measured and compared the level of image noise for each technique at the liver and aorta.

Results

There were significant differences (p < 0.001) among the seven reconstruction techniques in terms of lesion borders, image artifacts, and overall image quality. Low-strength DLIR (DLIR-L) exhibited the best overall image quality. Although high-strength DLIR (DLIR-H) had the least image noise and fewest artifacts, it also had the lowest scores for lesion borders and overall image quality. Image noise showed a weak to moderate positive correlation with participants' body mass index and waist circumference.

Conclusions

The optimal-strength DLIR significantly improved overall image quality for evaluating focal hepatic lesions compared to the IR technique. DLIR-L achieved the best overall image quality while maintaining acceptable levels of image noise and quality of lesion borders.

Pragmatic approaches to reducing radiation dose in brain computed tomography scan using scan parameter modification

Background:

High radiation dose of patients has become a concern in the computed tomography (CT) examinations. The aim of this study is to guide the radiology technician in modifying or optimizing the underlying parameters of the CT scan to reduce the patient radiation dose and produce an acceptable image quality for diagnosis.

Methods:

The body mass measurement device phantom was repeatedly scanned by changing the scan parameters. To analyze the image quality, software-based and observer-based evaluations were employed. To study the effect of scan parameters such as slice thickness and reconstruction filter on image quality and radiation dose, the structural equation modeling was used.

Results:

By changing the reconstruction filter from standard to soft and slice thickness from 2.5 mm to 5 mm, low-contrast resolution did not change significantly. In addition, by increasing the slice thickness and changing the reconstruction filter, the spatial resolution at different radiation conditions did not significantly differ from the standard irradiation conditions (P > 0.05).

Conclusion:

In this study, it was shown that in the brain CT scan imaging, the radiation dose was reduced by 30%–50% by increasing the slice thickness or changing the reconstruction filter. It is necessary to adjust the CT scan protocols according to clinical requirements or the special conditions of some patients while maintaining acceptable image quality.

Third-generation iterative reconstruction on a dual-source, high-pitch, low-dose chest CT protocol with tin filter for spectral shaping at 100 kV: a study on a small series of COVID-19 patients

OBJECTIVES

To investigate the role of third-generation iterative reconstruction (ADMIRE) in dual-source, high-pitch chest CT protocol with spectral shaping at 100 kVp in Coronavirus disease 2019 (COVID-19).

METHODS

Confirmed COVID-19 inpatients undergoing to unenhanced chest CT were scanned with a dual-energy acquisition (DECT, 90/150Sn kV) and a dual-source, high-pitch acquisition with tin-filtered 100 kVp (LDCT). On the DECT with ADMIRE 3 (DECT3) were evaluated the pulmonary findings and their extension (25-point score). Two radiologists in consensus evaluated with 5-point scales the overall image quality, the anatomical structures, and the elementary findings on LDCT reconstructed with filtered backprojection (LDCT0), with ADMIRE 3 (LDCT3) and 5 (LDCT5), and on DECT3. The signal-to-noise ratio (SNR), the body mass index, the exposure times, and the radiation doses were recorded.

RESULTS

Seventy-five patients (57 M/18F; median age: 63 y.o.) were included, with median pulmonary extension of 13/25 points. The imaging findings were detected in proportion comparable to the available literature. The ADMIRE significantly improved the SNR in LDCT (p < 0.00001) with almost no significant differences in overweight patients. The LDCT had median effective dose of 0.39 mSv and acquisition time of 0.71 s with significantly less motion artifacts than DECT (p < 0.00001). The DECT3 and LDCT3 provided the best image quality and depiction of pulmonary anatomy and imaging findings, with significant differences among all the series (p < 0.00001).

CONCLUSION

The LDCT with spectral shaping and ADMIRE3 provided acceptable image quality in the evaluation of patients with COVID-19, with significantly reduced radiation dose and motion artifacts.

Effect of obesity on ability to lower exposure for detection of low-attenuation liver lesions

Purpose

The purpose of this study was to assess the effect of obesity and iterative reconstruction on the ability to reduce exposure by studying the accuracy for detection of low‐contrast low‐attenuation (LCLA) liver lesions on computed tomography (CT) using a phantom model.

Methods

A phantom with four unique LCLA liver lesions (5‐ to 15‐mm spheres, –24 to –6 HU relative to 90‐HU background) was scanned without (“thin” phantom) and with (“obese” phantom) a 5‐cm thick fat‐attenuation ring at 150 mAs (thin phantom) and 450 mAs (obese phantom) standard exposures and at 33% and 67% exposure reductions. Images were reconstructed using standard filtered back projection (FBP) and with iterative reconstruction (Adaptive Model‐Based Iterative Reconstruction strength 3, ADMIRE). A noninferiority analysis of lesion detection was performed.

Results

Mean area under the curve (AUC) values for lesion detection were significantly higher for the thin phantom than for the obese phantom regardless of exposure level (P < 0.05) for both FBP and ADMIRE. At 33% exposure reduction, AUC was noninferior for both FBP and ADMIRE strength 3 (P < 0.0001). At 67% exposure reduction, AUC remained noninferior for the thin phantom (P < 0.0035), but was no longer noninferior for the obese phantom (P ≥ 0.7353). There were no statistically significant differences in AUC between FBP and ADMIRE at any exposure level for either phantom.

Conclusions

Accuracy for lesion detection was not only significantly lower in the obese phantom at all relative exposures, but detection accuracy decreased sooner while reducing the exposure in the obese phantom. There was no significant difference in lesion detection between FBP and ADMIRE at equivalent exposure levels for either phantom.

Technological developments of X-ray computed tomography over half a century: User's influence on protocol optimization

Since the introduction of Computed Tomography (CT), technological improvements have been impressive. At the same time, the number of adjustable acquisition and reconstruction parameters has increased substantially. Overall, these developments led to improved image quality at a reduced radiation dose. However, many parameters are interrelated and part of automated algorithms. This makes it more complicated to adjust them individually and more difficult to comprehend their influence on CT protocol adjustments. Moreover, the user's influence in adapting protocol parameters is sometimes limited by the manufacturer's policy or the user's knowledge. As a consequence, optimization can be a challenge. A literature search in Embase, Medline, Cochrane, and Web of Science was performed. The literature was reviewed with the objective to collect information regarding technological developments in CT over the past five decades and the role of the associated acquisition and reconstruction parameters in the optimization process.

Rapid kVp-switching DECT portal venous phase abdominal CT scans in patients with large body habitus: image quality considerations

PURPOSE

To assess the diagnostic image quality and material decomposition characteristics of portal venous phase abdominal CT scans performed on rapid kVp-switching DECT (rsDECT) in patients with large body habitus.

METHODS

We retrospectively included consecutive patients with large body habitus (≥ 90 kg) undergoing portal venous phase abdominal CT scans on rsDECT scanners between Sep 2014 and March 2018. Qualitative and quantitative assessment of the DECT data sets [65 keV monoenergetic, material density iodine (MD-I) and material density water (MD-W) images] was performed for determination of image quality (IQ) and image noise. Correlation of qualitative assessment scores with weight, BMI and patients' diameter were calculated using Pearson correlation test. Optimal thresholds were calculated using AUC and Youden index to define most appropriate size cut off, below which the IQ of material density images is largely acceptable.

RESULTS

The 65 keV monoenergetic images were of diagnostic quality (diagnostic acceptability, DA ≥ 3) in 97.8% of patients (n = 91/93). However, there was significant IQ degradation of MD-I images in 20.4% (n = 19/93, DA < 3) of patients. Similarly, there was significant degradation (DA < 3) of MD-W images in 26.9% (25/92). Clinically significant artifacts (PA ≥ 3/4) were seen in 31% (n = 29/93) and 32.3% (30/93) of MD-I and MD-W images respectively. Optimal threshold for diagnostic acceptability of MD-I images were 110 kg for weight and 33.5 kg/m2 for BMI.

CONCLUSION

Rapid kVp-switching DECT provides diagnostically acceptable monoenergetic images for patients with large body habitus (≥ 90 kg). There is degradation of IQ in the material density specific images particularly in patients weighing > 110 kg and with BMI > 33.5 kg/m2, due to higher number of artifacts.

Vascular computed tomography angiography technique and indications

Non-invasive cross-sectional imaging techniques play a crucial role in the assessment of the vascular disease processes. Computed tomography angiography (CTA) is an imaging method of choice for a wide range of vascular diseases that span across different vascular territories. A diagnostic quality CTA requires a robust imaging protocol tailored according to the physiologic state and vascular area of interest. This review article is aimed to provide an overview of the technical considerations and clinical applications of CTA.

Dual-Source Dual-Energy CT in Detection and Characterization of Urinary Stones in Patients With Large Body Habitus: Observations in a Large Cohort

OBJECTIVE

The objective of our study was to investigate the impact of large body habitus on dual-energy CT (DECT) image quality and stone characterization.

MATERIALS AND METHODS

We retrospectively included 105 consecutive patients with large body habitus (> 90 kg) who underwent stone protocol DECT between 2015 and 2017. The evaluation of DECT datasets was performed for image quality assessment based on European Guidelines on Quality Criteria for Computed Tomography and for determination of stone composition (i.e., uric acid vs non-uric acid). Correlation between DECT characterization and crystallography results was performed when available. The cohort was divided into two groups on the basis of body weight (≤ 104 kg and > 104 kg), and comparisons were made for image quality and stone characterization.

RESULTS

One hundred ninety-seven urinary tract calculi (size: mean ± SD, 5.7 ± 5.3 mm; range, 1.4-56 mm) were detected in 73% (79/108) of examinations in 105 patients (weight: mean ± SD, 104.0 ± 12.7 kg; range, 91-163 kg). The overall mean image quality score of blended images and color maps was 3.7 and 3.9, respectively, and the effective dual-energy FOV limitation did not hamper stone characterization. The diagnostic acceptability scores of blended images and color maps were slightly lower in patients weighing > 104 kg than in patients ≤ 104 kg (mean scores [highest score, 4 points]: blended images, 3.62 vs 3.82 [p = 0.0314]; color maps, 3.75 vs 3.98 [p = 0.0034]), but the scores were within acceptable range. Stone characterization as uric acid versus non-uric acid was achieved in 80% (158/197) of calculi (size: mean ± SD, 6.4 ± 5.7 mm; range, 1.6-56 mm), and DECT stone characterization was (95.6%) accurate with reference to crystallography. Twenty percent (39/197) of calculi could not be characterized on DECT, and these calculi were significantly smaller in size (size: mean ± SD, 2.8 ± 1.4 mm; range, 1.4-8.2 mm; p < 0.001) than those that could be characterized. The mean size of uncharacterized calculi was slightly larger in patients weighing > 104 kg (3.3 ± 1.6 mm) than in those weighing ≤ 104 kg (2.2 ± 0.6 mm).

CONCLUSION

In patients with large body habitus, dual-source DECT provides acceptable image quality and allows characterization of almost all clinically significant calculi.

Rapid kVp switching dual-energy CT in the assessment of urolithiasis in patients with large body habitus: preliminary observations on image quality and stone characterization

PURPOSE

The purpose of this study was to investigate the image quality (IQ) considerations of rapid kVp switching dual-energy CT (rsDECT) in the assessment of urolithiasis in patients with large body habitus and to evaluate whether it allows stone characterization.

MATERIALS AND METHODS

In this IRB-approved, HIPAA compliant retrospective study, 93 consecutive patients (M/F = 72/21, mean age 56.9 years, range 23-83 years) with large body habitus (> 90 kg/198 lbs) who underwent dual-energy (DE) stone protocol CT on a rapid kVp switching DECT scanner between January 2013 and December 2016 were included. Scan acquisition protocol included an initial unenhanced single-energy CT (SECT) scan of KUB followed by targeted DECT in the region of stones. Two readers evaluated both CT data sets (axial 5 mm 120 kVp/140 kVp QC/70 keV monoenergetic, material density water/iodine images and coronal/sagittal 3 mm images) for the assessment of image quality (Scores: 1-4) and characterization of stone composition (reference standard: crystallography).

RESULTS

One hundred and five CT examinations were performed in 93 patients (mean body weight 105.12 ± 13.53 kg, range 91-154 kg), and a total of 321 urinary tract calculi (mean size-4.8 ± 3.2 mm, range 1.2-22 mm) were detected. Both SECT and targeted monoenergetic images were of acceptable image quality (mean IQ: 3.77 and 3.83, kappa 0.79 and 0.87 respectively). Material density water and iodine images had lower IQ scores (mean IQ: 2.97 and 3.09 respectively) with image quality deterioration due to severe photon starvation/streak artifacts in 20% (21/105) and 17% (18/105) scans, respectively. Characterization of stone composition into uric acid/non-uric acid stones was achieved in 93.14% (299/321) of calculi (mean size: 4.99 ± 3.3 mm, range 1.2-22 mm), while 7% (22/321) stones could not be characterized (mean size 3.03 ± 1.16 mm, range 1.6-6.4 mm) (p < 0.001). Most common reason for non-characterization was image quality deterioration of the material density iodine images due to severe photon starvation artifacts. On multivariate regression, stone size and patient weight were predictors of stone composition determination on DECT (p < 0.05). The transverse diameter had a weak negative correlation with stone composition determination, but it was not statistically significant. Stone characterization into uric acid vs. non-uric acid stones was accurate in 95% (n = 38/40) of stones in comparison with crystallography.

CONCLUSION

In patients with large body habitus, rsDECT allowed characterization of most calculi (93%) despite image quality deterioration due to photon starvation/streak artifacts in up to 20% of material density images. Stone size and patient weight were predictors of stone composition determination on DECT, and small calculi in very large patients may not be characterized.

Technical challenges of imaging & image-guided interventions in obese patients

Obese patients challenge imaging departments in their ability to obtain diagnostic quality images and to perform image-guided interventions. These technical challenges include properly accommodating large patients on imaging equipment, adjusting equipment settings to address imaging limitations, and pre-planning and preparation for image-guided interventions to insure safe and successful outcomes. Knowing and addressing these challenges can result in successfully addressing the imaging and image-guided interventions needs of obese patients.