Radiation dose reduction for CT assessment of urolithiasis using iterative reconstruction: A prospective intra-individual study

Comparison of diagnostic accuracy of ultra low-dose computed tomography and X-ray of the kidneys, ureters and bladder for urolithiasis in the follow-up setting

INTRODUCTION

Urolithiasis is frequently followed up with a low-dose computed tomography of the kidneys ureters and bladder (LD-CTKUB) with doses typically less than 3 millisieverts. Although X-ray is a lower dose (0.5-1.1 mSv) alternative for follow up, it has lower diagnostic accuracy and is limited to radiopaque calculi. This study aims to compare the diagnostic accuracy of sub-millisievert ultra-low dose CT (ULD-CTKUB) against X-ray KUB for the follow up of urolithiasis when both are compared against the standard of care of a low-dose CT KUB (LD-CTKUB).

METHODS

This prospective study included patients with a known diagnosis of urolithiasis on prior CTKUB presenting for follow up. Each patient underwent a repeat reference LD-CTKUB, ULD-CTKUB and X-ray KUB. All imaging studies were interpreted by three readers. The radiation dose and quantitative noise was calculated for each CT. Both CT and X-ray were assessed for the presence, number and size of all calculi ≥2 mm.

RESULTS

A total of 58 patients were included in this study. LD-CTKUB identified 197 calculi. ULD-CTKUB in our study had a mean effective dose of 0.5 mSv compared to X-ray KUB where doses range in the literature from 0.5 to 1.1 mSv. Per-patient pooled analysis for intrarenal calculi when comparing ULD-CTKUB versus X-ray KUB against a reference LD-CTKUB found a sensitivity of 90% versus 67% (P < 0.01) and specificity of 93% versus 98% (P = 0.18) respectively. For ureteric calculi, the sensitivity was 67% versus 33% (P < 0.01) and specificity 94% versus 94% (P = 1.00) respectively. Per-stone pooled analysis detection rate was 79% for ULD-CTKUB versus 48% for X-ray (P < 0.01) when each was compared to the reference LD-CTKUB. Interobserver agreement was high for intrarenal calculi and moderate for ureteric calculi.

CONCLUSION

Sub-millisievert ULD-CTKUB had lower doses and higher sensitivity than X-ray in patients requiring follow up of known urolithiasis.

Deep-Learning-Based Image Denoising in Imaging of Urolithiasis: Assessment of Image Quality and Comparison to State-of-the-Art Iterative Reconstructions

This study aimed to compare the image quality and diagnostic accuracy of deep-learning-based image denoising reconstructions (DLIDs) to established iterative reconstructed algorithms in low-dose computed tomography (LDCT) of patients with suspected urolithiasis. LDCTs (CTDIvol, 2 mGy) of 76 patients (age: 40.3 ± 5.2 years, M/W: 51/25) with suspected urolithiasis were retrospectively included. Filtered-back projection (FBP), hybrid iterative and model-based iterative reconstruction (HIR/MBIR, respectively) were reconstructed. FBP images were processed using a Food and Drug Administration (FDA)-approved DLID. ROIs were placed in renal parenchyma, fat, muscle and urinary bladder. Signal- and contrast-to-noise ratios (SNR/CNR, respectively) were calculated. Two radiologists evaluated image quality on five-point Likert scales and urinary stones. The results showed a progressive decrease in image noise from FBP, HIR and DLID to MBIR with significant differences between each method (p < 0.05). SNR and CNR were comparable between MBIR and DLID, while it was significantly lower in HIR followed by FBP (e.g., SNR: 1.5 ± 0.3; 1.4 ± 0.4; 1.0 ± 0.3; 0.7 ± 0.2, p < 0.05). Subjective analysis confirmed best image quality in MBIR, followed by DLID and HIR, both being superior to FBP (p < 0.05). Diagnostic accuracy for urinary stone detection was best using MBIR (0.94), lowest using FBP (0.84) and comparable between DLID (0.90) and HIR (0.90). Stone size measurements were consistent between all reconstructions and showed excellent correlation (r2 = 0.958-0.975). In conclusion, MBIR yielded the highest image quality and diagnostic accuracy, with DLID producing better results than HIR and FBP in image quality and matching HIR in diagnostic precision.

Detection and size measurements of kidney stones on virtual non-contrast reconstructions derived from dual-layer computed tomography in an ex vivo phantom setup

OBJECTIVES

To systematically investigate the usability of virtual non-contrast reconstructions (VNC) derived from dual-layer CT (DLCT) for detection and size measurements of kidney stones with regards to different degrees of surrounding iodine-induced attenuation and radiation dose.

METHODS

Ninety-two kidney stones of varying size (3-14 mm) and composition were placed in a phantom filled with different contrast media/water mixtures exhibiting specific iodine-induced attenuation (0-1500 HU). DLCT-scans were acquired using CTDI_vol of 2 mGy and 10 mGy. Conventional images (CI) and VNC_0H-1500HU were reconstructed. Reference stone size was determined using a digital caliper (Man-M). Visibility and stone size were assessed. Statistical analysis was performed using the McNemar test, Wilcoxon test, and the coefficient of determination.

RESULTS

All stones were visible on CI_0HU and VNC_200HU. Starting at VNC_{400 HU}, the detection rate decreased with increasing HU and was significantly lower as compared to CI_0HU on VNC_{≥ 600HU} (100.0 vs. 94.0%, p < 0.05). The overall detection rate was higher using 10 mGy as compared to 2 mGy protocol (87.9 vs. 81.8%; p < 0.001). Stone size was significantly overestimated on all VNC compared to Man-M (7.0 ± 3.5 vs. 6.6 ± 2.8 mm, p < 0.001). Again, the 10 mGy protocol tended to show a better correlation with Man-M as compared to 2 mGy protocol (R² = 0.39-0.68 vs. R² = 0.31-0.57).

CONCLUSIONS

Detection and size measurements of kidney stones surrounded by contrast media on VNC are feasible. The detection rate of kidney stones decreases with increasing iodine-induced attenuation and with decreasing radiation dose as well as stone size, while remaining comparable to CI_0HU on VNC _{≤ 400 HU}.

KEY POINTS

• The detection rate of kidney stones on VNC depends on the surrounding iodine-induced attenuation, the used radiation dose, and the stone size. • The detection rate of kidney stones on VNC decreases with greater iodine-induced attenuation and with lower radiation dose, particularly in small stones. • The visibility of kidney stones on VNC _{≤ 400 HU} remains comparable to true-non-contrast scans even when using a low-dose technique.

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

Diagnostic performance and image quality of deep learning image reconstruction (DLIR) on unenhanced low-dose abdominal CT for urolithiasis

BACKGROUND

Patients with urolithiasis undergo radiation overexposure from computed tomography (CT) scans. Improvement of image reconstruction is necessary for radiation dose reduction.

PURPOSE

To evaluate a deep learning-based reconstruction algorithm for CT (DLIR) in the detection of urolithiasis at low-dose non-enhanced abdominopelvic CT.

MATERIAL AND METHODS

A total of 75 patients who underwent low-dose abdominopelvic CT for urolithiasis were retrospectively included. Each examination included three reconstructions: DLIR; filtered back projection (FBP); and hybrid iterative reconstruction (IR; ASiR-V 70%). Image quality was subjectively and objectively assessed using attenuation and noise measurements in order to calculate the signal-to-noise ratio (SNR), absolute contrast, and contrast-to-noise ratio (CNR). Attenuation of the largest stones were also compared. Detectability of urinary stones was assessed by two observers.

RESULTS

Image noise was significantly reduced with DLIR: 7.2 versus 17 and 22 for ASiR-V 70% and FBP, respectively. Similarly, SNR and CNR were also higher compared to the standard reconstructions. When the structures had close attenuation values, contrast was lower with DLIR compared to ASiR-V. Attenuation of stones was also lowered in the DLIR series. Subjective image quality was significantly higher with DLIR. The detectability of all stones and stones >3 mm was excellent with DLIR for the two observers (intraclass correlation [ICC] = 0.93 vs. 0.96 and 0.95 vs. 0.99). For smaller stones (<3 mm), results were different (ICC = 0.77 vs. 0.86).

CONCLUSION

For low-dose abdominopelvic CT, DLIR reconstruction exhibited image quality superior to ASiR-V and FBP as well as an excellent detection of urinary stones.

Sinogram-based deep learning image reconstruction technique in abdominal CT: image quality considerations

OBJECTIVES

To investigate the image quality and perception of a sinogram-based deep learning image reconstruction (DLIR) algorithm for single-energy abdominal CT compared to standard-of-care strength of ASIR-V.

METHODS

In this retrospective study, 50 patients (62% F; 56.74 ± 17.05 years) underwent portal venous phase. Four reconstructions (ASIR-V at 40%, and DLIR at three strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H)) were generated. Qualitative and quantitative image quality analysis was performed on the 200 image datasets. Qualitative scores were obtained for image noise, contrast, small structure visibility, sharpness, and artifact by three blinded radiologists on a 5-point scale (1, excellent; 5, very poor). Radiologists also indicated image preference on a 3-point scale (1, most preferred; 3, least preferred). Quantitative assessment was performed by measuring image noise and contrast-to-noise ratio (CNR).

RESULTS

DLIR had better image quality scores compared to ASIR-V. Scores on DLIR-H for noise (1.40 ± 0.53), contrast (1.41 ± 0.55), small structure visibility (1.51 ± 0.61), and sharpness (1.60 ± 0.54) were the best (p < 0.05) followed by DLIR-M (1.85 ± 0.52, 1.66 ± 0.57, 1.69 ± 0.59, 1.68 ± 0.46), DLIR-L (2.29 ± 0.58, 1.96 ± 0.61, 1.90 ± 0.65, 1.86 ± 0.46), and ASIR-V (2.86 ± 0.67, 2.55 ± 0.58, 2.34 ± 0.66, 2.01 ± 0.36). Ratings for artifacts were similar for all reconstructions (p > 0.05). DLIRs did not influence subjective textural perceptions and were preferred over ASIR-V from the beginning. All DLIRs had a higher CNR (26.38-102.30%) and lower noise (20.64-48.77%) than ASIR-V. DLIR-H had the best objective scores.

CONCLUSION

Sinogram-based deep learning image reconstructions were preferred over iterative reconstruction subjectively and objectively due to improved image quality and lower noise, even in large patients. Use in clinical routine may allow for radiation dose reduction.

KEY POINTS

• Deep learning image reconstructions (DLIRs) have a higher contrast-to-noise ratio compared to medium-strength hybrid iterative reconstruction techniques. • DLIR may be advantageous in patients with large body habitus due to a lower image noise. • DLIR can enable further optimization of radiation doses used in abdominal CT.

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.

Imaging strategies for patients with suspicion of uncomplicated colic pain: diagnostic accuracy and management assessment

OBJECTIVE

Compare different imaging scenarios in the diagnosis of uncomplicated renal colic due to urolithiasis (URCU).

MATERIALS AND METHODS

A total of 206 prospectively included patients had been admitted with suspected URCU and had undergone abdominal plain film (APF), US and unenhanced CT after clinical STONE score evaluation. CT was the reference standard. We assessed sensitivity (Se), specificity (Spe) and Youden index for colic pain diagnosis, percentage of patients managed by urologic treatment with stone identified, percentage of alternative diagnoses (AD) and exposure to radiation, according to single imaging approaches, strategies driven by patient characteristics and conditional imaging strategies after APF and US.

RESULTS

One hundred (48.5%) patients had a final diagnosis of URCU and 19 underwent urologic treatment. The conditional strategy, i.e. CT in patients who had no stone identified at US, had a perfect sensitivity and specificity. This enabled diagnosis of all stones requiring urology management while decreasing the number of CT exams by 22%. The strategy whereby CT was used when there was neither direct or indirect APF + US finding of colic pain nor alternative diagnoses in patients with a STONE score ≥ 10 had a sensitivity of 0.95 and a specificity of 0.99, identified 84% of stones managed by urologic treatment and decreased the number of CT examinations by 76%.

CONCLUSION

In patients with clinical findings consistent with URCU, the use of ultrasound as first-line imaging modality, with CT restricted to patients with negative US and a STONE score ≥ 10, led to a sensitivity and specificity of above 95%, identified 84% of stones requiring urological management and reduced the number of CT scans needed by fourfold.

KEY POINTS

• For diagnosis, the use of APF + US as first-line imaging, with CT restricted to patients with both a normal APF + US and a STONE score ≥ 10, provides both a sensitivity and specificity superior or equal to 95% and reduces the number of CT scans necessary by fourfold. • For management, the use of APF + US as first-line imaging, with CT restricted to patients with both a normal APF + US and a STONE score ≥ 10, maintains a 84% stone identification rate in urology-treated patients.

Size and volume of kidney stones in computed tomography: Influence of acquisition techniques and image reconstruction parameters

PURPOSE

Computed tomography (CT) is routinely used to assess suspected urolithiasis. Information obtained from CT include presence, location and size of stones, with the latter frequently determining treatment strategy. While there is consensus regarding measurements procedures of kidney stones, influence of radiation dose and reconstruction techniques on stone measurements are unknown. The purpose of this study was to systematically evaluate the influence of these technical determinants on kidney stone size measurements.

METHOD

47 kidney stones of different composition were scanned using a 64-row-multi-detector CT in a 3D-printed, semi-anthropomorphic phantom. Reference stone sizes were measured manually with a digital caliper (Man-M). Stones were imaged with 2 and 10 mGy CTDI. Images were reconstructed using filtered-back-projection, hybrid-iterative and model-based-iterative reconstruction algorithms (FBP, HIR, MBIR) in combination with different kernels and denoising levels. All stones underwent semi-automatic, threshold-based segmentation for computation of maximum diameter and volume. Statistics were conducted using ANOVA ± correction for multiple comparisons.

RESULTS

Overall stone size as compared to manual measurements was overestimated in CT (10.0 ± 3.1 vs. 8.8 ± 2.9 mm, p < 0.05) yet showing a good correlation (R² = 0.66). Radiation dose and denoising levels did not significantly influence measurements (p > 0.05). MBIR and sharp kernels showed closest agreement with Man-M (9.3 ± 3.1 vs. 8.8 ± 2.9 mm, p < 0.05). Differences within single stones were as high as 40 % (e.g. Man-M: 5.9 mm, CT: 7.3-12.0 mm).

CONCLUSIONS

CT-based measurements of kidney stone size appear unaffected by radiation dose and denoising technique, whereas reconstruction algorithms and kernels demonstrate a relevant impact on size measurements. Smallest differences were found using MBIR with a sharp kernel.

Comparison of submillisievert CT with standard-dose CT for urolithiasis

BACKGROUND

Patients with renal stones receive multiple computed tomography (CT) examinations. We investigated whether submillisievert (sub-mSv) CT for stone detection could reduce radiation dose at exposure levels comparable to kidney, ureter, and bladder (KUB) radiography.

PURPOSE

To evaluate the radiation dose exposure, diagnostic performance, and image quality of sub-mSv non-contrast CT using advanced modelled iterative reconstruction algorithm with spectral filtration for the detection of urolithiasis.

MATERIAL AND METHODS

A total of 145 consecutive patients underwent non-contrast CT using a third-generation dual-source scanner to obtain two datasets, i.e. 16.7% (sub-mSv CT, tube detector A) and 100% (standard-dose CT, combination of tube detector A and B) tube loads with spectral filtration. The performance of sub-mSv CT for the detection of stones was analyzed by two readers and compared with that of standard-dose CT. Image quality was measured subjectively and objectively.

RESULTS

In total, 171 stones were detected in 79 patients. The mean effective radiation doses of sub-mSv CT was 0.3 mSv. The sensitivity and specificity values for diagnosis of stones measuring ≥3 mm was 95.1% and 100% for sub-mSv CT. The sensitivity and specificity for all stone detection was 74.9% and 97.8%, respectivey, for sub-mSv CT. The image quality was lower for sub-mSv CT than for standard-dose CT (P < 0.01).

CONCLUSION

Sub-mSv CT can be achieved with radiation doses close to KUB radiography. Sub-mSv CT with spectral filtration can be used to detect stones measuring ≥3 mm and be used as a follow-up imaging modality as an alternative to KUB radiography.

Modern imaging techniques in urinary stone disease

PURPOSE OF REVIEW

Radiological imaging techniques are a fast developing field in medicine. Therefore, the purpose of this review was to identify and discuss the latest changes of modern imaging techniques in the management of urinary stone disease.

RECENT FINDINGS

The introduction of iterative image reconstruction enables low-dose and ultra-low-dose (ULD) protocols. Although current guidelines recommend their utilization in nonobese patients recent studies indicate that low-dose imaging may be feasible in obese (<30 kg/m) but not in bariatric patients. Use of dual energy computed tomography (CT) technologies should balance between additional information and radiation dose aspects. If available on a dose neutral basis, dual energy imaging and analysis should be performed. Current guidelines recommend measuring the largest diameter for clinical decision making; however, recent studies suggest a benefit from measuring the volume based on multiplanar reformation. Quantitative imaging is still an experimental approach.

SUMMARY

The use of low-dose and even ULD CT protocols should be diagnostic standard, even in obese patients. If dual energy imaging is available, it should be limited to specific clinical questions. The stone volume should be reported in addition to the largest diameter for treatment decision and a more valid comparability of upcoming studies.

Can visual analogue scale be used in radiologic subjective image quality assessment?

BACKGROUND

Assessment of qualitative or subjective image quality in radiology is traditionally performed using a fixed-point scale even though reproducibility has proved challenging.

OBJECTIVE

Image quality of 3-T coronary magnetic resonance (MR) angiography was evaluated using three scoring methods, hypothesizing that a continuous scoring scale like visual analogue scale would improve the assessment.

MATERIALS AND METHODS

Adolescents corrected for transposition of the great arteries with arterial switch operation, ages 9-15 years (n=12), and healthy, age-matched controls (n=12), were examined with 3-D steady-state free precession magnetic resonance imaging. Image quality of the coronary artery origin was evaluated by using a fixed-point scale (1-4), visual analogue scale of 10 cm and a visual analogue scale with reference points (figurative visual analogue scale). Satisfactory image quality was set to a fixed-point scale 3=visual analogue scale/figurative visual analogue scale 6.6 cm. Statistical analysis was performed using Cohen kappa coefficient and agreement index.

RESULTS

The mean interobserver scores for the fixed-point scale, visual analogue scale and figurative visual analogue scale were, respectively, in the left main stem 2.8, 5.7, 7.0; left anterior descending artery 2.8, 4.7, 6.6; circumflex artery 2.5, 4.5, 6.2, and right coronary artery 3.2, 6.3, 7.7. Scoring with a fixed-point scale gave an intraobserver κ of 0.52-0.77 while interobserver κ was lacking. For visual analogue scale and figurative visual analogue scale, intraobserver agreement indices were, respectively, 0.08-0.58 and 0.43-0.71 and interobserver agreement indices were up to 0.5 and 0.65, respectively.

CONCLUSION

Qualitative image quality evaluation with coronary 3-D steady-state free precession MR angiography, using a visual analogue scale with reference points, had better reproducibility compared to a fixed-point scale and visual analogue scale. Image quality, being a continuum, may be better determined by this method.