Assessment of sub-milli-sievert abdominal computed tomography with iterative reconstruction techniques of different vendors

Visceral adipose tissue volume effect in Crohn's disease using reduced exposure CT enterography

PURPOSE

The purpose of this investigation was to assess the effect of visceral adipose tissue volume (VA) on reader efficacy in diagnosing and characterizing small bowel Crohn's disease using lower exposure CT enterography (CTE). Secondarily, we investigated the effect of lower exposure and VA on reader diagnostic confidence.

METHODS

Prospective paired investigation of 256 CTE, 129 with Crohn's disease, were reconstructed at 100% and simulated 50% and 30% exposure. The senior author provided the disease classification for the 129 patients with Crohn's disease. Patient VA was measured, and exams were evaluated by six readers for presence or absence of Crohn's disease and phenotype using a 0-10-point scale. Logistic regression models assessed the effect of VA on sensitivity and specificity.

RESULTS

The effect of VA on sensitivity was significantly reduced at 30% exposure (odds radio [OR]: 1.00) compared to 100% exposure (OR: 1.12) (p = 0.048). There was no statistically significant difference among the exposures with respect to the effect of visceral fat on specificity (p = 0.159). The study readers' probability of agreement with the senior author on disease classification was 60%, 56%, and 53% at 100%, 50%, and 30% exposure, respectively (p = 0.004). When detecting low severity Crohn's disease, readers' mean sensitivity was 83%, 75%, and 74% at 100%, 50%, and 30% exposure, respectively (p = 0.002). In low severity disease, sensitivity also tended to increase as visceral fat increased (ORs per 1000 cm3 increase in visceral fat: 1.32, 1.31, and 1.18, p = 0.010, 0.016, and 0.100, at 100%, 50%, and 30% exposure).

CONCLUSIONS

While the interaction is complex, VA plays a role in detecting and characterizing small bowel Crohn's disease when exposure is altered, particularly in low severity disease.

Application of deep learning reconstruction of ultra-low-dose abdominal CT in the diagnosis of renal calculi

BACKGROUND

Renal calculi are a common and recurrent urological disease and are usually detected by CT. In this study, we evaluated the diagnostic capability, image quality, and radiation dose of abdominal ultra-low-dose CT (ULDCT) with deep learning reconstruction (DLR) for detecting renal calculi.

METHODS

Sixty patients with suspected renal calculi were prospectively enrolled. Low-dose CT (LDCT) images were reconstructed with hybrid iterative reconstruction (LD-HIR) and was regarded as the standard for stone and lesion detection. ULDCT images were reconstructed with HIR (ULD-HIR) and DLR (ULD-DLR). We then compared stone detection rate, abdominal lesion detection rate, image quality and radiation dose between LDCT and ULDCT.

RESULTS

A total of 130 calculi were observed on LD-HIR images. Stone detection rates of ULD-HIR and ULD-DLR images were 93.1% (121/130) and 95.4% (124/130). A total of 129 lesions were detected on the LD-HIR images. The lesion detection rate on ULD-DLR images was 92.2%, with 10 cysts < 5 mm in diameter missed. The CT values of organs on ULD-DLR were similar to those on LD-HIR and lower than those on ULD-HIR. Signal-to-noise ratio was highest and noise lowest on ULD-DLR. The subjective image quality of ULD-DLR was similar to that of LD-HIR and better than that of ULD-HIR. The effective radiation dose of ULDCT (0.64 ± 0.17 mSv) was 77% lower than that of LDCT (2.75 ± 0.50 mSv).

CONCLUSION

ULDCT combined with DLR could significantly reduce radiation dose while maintaining suitable image quality and stone detection rate in the diagnosis of renal calculi.

CT Noise-Reduction Methods for Lower-Dose Scanning: Strengths and Weaknesses of Iterative Reconstruction Algorithms and New Techniques

Iterative reconstruction (IR) algorithms are the most widely used CT noise-reduction method to improve image quality and have greatly facilitated radiation dose reduction within the radiology community. Various IR methods have different strengths and limitations. Because IR algorithms are typically nonlinear, they can modify spatial resolution and image noise texture in different regions of the CT image; hence traditional image-quality metrics are not appropriate to assess the ability of IR to preserve diagnostic accuracy, especially for low-contrast diagnostic tasks. In this review, the authors highlight emerging IR algorithms and CT noise-reduction techniques and summarize how these techniques can be evaluated to help determine the appropriate radiation dose levels for different diagnostic tasks in CT. In addition to advanced IR techniques, we describe novel CT noise-reduction methods based on convolutional neural networks (CNNs). CNN-based noise-reduction techniques may offer the ability to reduce image noise while maintaining high levels of image detail but may have unique drawbacks. Other novel CT noise-reduction methods are being developed to leverage spatial and/or spectral redundancy in multiphase or multienergy CT. Radiologists and medical physicists should be familiar with these different alternatives to adapt available CT technology for different diagnostic tasks. The scope of this article is (a) to review the clinical applications of IR algorithms as well as their strengths, weaknesses, and methods of assessment and (b) to explore new CT image reconstruction and noise-reduction techniques that promise to facilitate radiation dose reduction. ^©RSNA, 2021.

Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT

OBJECTIVE. The objective of this study was to compare image quality and clinically significant lesion detection on deep learning reconstruction (DLR) and iterative reconstruction (IR) images of submillisievert chest and abdominopelvic CT. MATERIALS AND METHODS. Our prospective multiinstitutional study included 59 adult patients (33 women, 26 men; mean age ± SD, 65 ± 12 years old; mean body mass index [weight in kilograms divided by the square of height in meters] = 27 ± 5) who underwent routine chest (n = 22; 16 women, six men) and abdominopelvic (n = 37; 17 women, 20 men) CT on a 640-MDCT scanner (Aquilion ONE, Canon Medical Systems). All patients gave written informed consent for the acquisition of low-dose (LD) CT (LDCT) after a clinically indicated standard-dose (SD) CT (SDCT). The SDCT series (120 kVp, 164-644 mA) were reconstructed with interactive reconstruction (IR) (adaptive iterative dose reduction [AIDR] 3D, Canon Medical Systems), and the LDCT (100 kVp, 120 kVp; 30-50 mA) were reconstructed with filtered back-projection (FBP), IR (AIDR 3D and forward-projected model-based iterative reconstruction solution [FIRST], Canon Medical Systems), and deep learning reconstruction (DLR) (Advanced Intelligent Clear-IQ Engine [AiCE], Canon Medical Systems). Four subspecialty-trained radiologists first read all LD image sets and then compared them side-by-side with SD AIDR 3D images in an independent, randomized, and blinded fashion. Subspecialty radiologists assessed image quality of LDCT images on a 3-point scale (1 = unacceptable, 2 = suboptimal, 3 = optimal). Descriptive statistics were obtained, and the Wilcoxon sign rank test was performed. RESULTS. Mean volume CT dose index and dose-length product for LDCT (2.1 ± 0.8 mGy, 49 ± 13mGy·cm) were lower than those for SDCT (13 ± 4.4 mGy, 567 ± 249 mGy·cm) (p < 0.0001). All 31 clinically significant abdominal lesions were seen on SD AIDR 3D and LD DLR images. Twenty-five, 18, and seven lesions were detected on LD AIDR 3D, LD FIRST, and LD FBP images, respectively. All 39 pulmonary nodules detected on SD AIDR 3D images were also noted on LD DLR images. LD DLR images were deemed acceptable for interpretation in 97% (35/37) of abdominal and 95-100% (21-22/22) of chest LDCT studies (p = 0.2-0.99). The LD FIRST, LD AIDR 3D, and LD FBP images had inferior image quality compared with SD AIDR 3D images (p < 0.0001). CONCLUSION. At submillisievert chest and abdominopelvic CT doses, DLR enables image quality and lesion detection superior to commercial IR and FBP images.

Can fully iterative reconstruction technique enable routine abdominal CT at less than 1 mSv?

Objective

We assessed the effect of the forward projected model-based reconstruction technique (FIRST) on lesion detection of routine abdomen CT at <1 mSv.

Materials and methods

Thirty-seven adult patients gave written informed consent for acquisition of low-dose CT (LDCT) immediately after their clinically-indicated, standard of care dose (SDCT), routine abdomen CT on a 640-slice MDCT (Aquillion One, Canon Medical System). The LDCT series were reconstructed with FIRST (at STD (Standard) and STR (Strong) levels), and SDCT series with filtered back projection (FBP). Two radiologists assessed lesions in LD-FBP and FIRST images followed by SDCT images. Then, SDCT and LDCT were compared for presence of artifacts in a randomized and blinded fashion. Patient demographics, size and radiation dose descriptors (CTDIvol, DLP) were recorded. Descriptive statistics and inter-observer variability were calculated for data analysis.

Results

Mean CTDIvol for SDCT and LDCT were 13 ± 4.7 mGy and 2.2 ± 0.8 mGy, respectively. There were 46 true positive lesions detected on SDCT. Radiologists detected 38/46 lesions on LD-FIRST-STD compared to 26/46 lesions on LD-FIRST-STR. The eight lesions (liver and kidney cysts, pancreatic lesions, sub-cm peritoneal lymph node) missed on LD-FIRST-STD were seen in patients with BMI > 25.8 kg/m². Diagnostic confidence for lesion assessment was optimal in LD-FIRST-STD setting in most patients regardless of their size. The inter-observer agreement (kappa-value) for overall image quality were 0.98 and 0.84 for LD-FIRST-STD and STR levels, respectively.

Conclusion

FIRST enabled optimal lesion detection in routine abdomen CT at less than 1 mSv radiation dose in patients with body mass less than ≤25.8 kg/m².

Diagnostic performance of advanced modeled iterative reconstruction applied images for detecting urinary stones on submillisievert low-dose computed tomography

Background Repeated computed tomography (CT) scans may be an issue in young adults with urinary stones. Therefore, it is important to know how far the dose can be reduced while maintaining the diagnostic performance. Purpose To generate a hypothesis that it is feasible to decrease the radiation dose to a sub-millisievert (mSv) level with the addition of advanced modeled iterative reconstruction (ADMIRE) while maintaining the sensitivity to standard-dose CT (SDCT) for the detection of urinary stones. Material and Methods Ninety-two consecutive patients with urinary stones underwent non-enhanced CT that consisted of standard (120 kVp, 200 mAs) and lose-dose (LDCT) (80 kVp, 60 mAs). The LDCT images were reconstructed separately with five different strengths of ADMIRE (hereafter, S1-S5) and filtered back projection (FBP). Two blinded radiologists independently recorded a number of urinary stones in the six LDCT datasets and SDCT. The sensitivity of each set for detecting urinary stones was compared using the McNemar test. Results A total of 240 urinary stones were analyzed. The sensitivities of the six LDCT datasets showed no difference (FBP, S1-S5, for reader 1: 78%, 79%, 79%, 80%, 80%, and 80%; for reader 2: 64%, 63%, 64%, 64%, 65%, and 66%, P > 0.05, respectively), which were lower than those of SDCT for both readers (reader 1: 88%; reader 2: 81%, P < 0.0001, respectively). Conclusion Despite the addition of ADMIRE, it may not be feasible to decrease the radiation dose to a sub-mSv level while maintaining the sensitivity to SDCT for the detection of urinary stones.

Imaging Advances in Urolithiasis

The prevalence of urinary stones in the United States has been described as 1 in 11 persons reporting a history of stones. Imaging plays a crucial role in diagnosis, management, and follow-up for these patients and imaging technology over the last 100 years has advanced as the disease prevalence has increased. CT remains the gold standard for imaging urolithiasis and changes in this technology, with the addition of multidetector CT and dual-energy CT, as well as the changes in utilization of CT, have decreased the radiation dose encountered by patients and allowed for improved stone detection. The use of digital tomography has been introduced for follow-up of recurrent stone formers offering the potential to lower radiation exposure over the course of a patient's lifelong treatment. However, there is still a demand for improved imaging techniques to detect smaller stones and stones in larger patients at lower radiation doses as well as the continued need for the judicious use of all imaging modalities for healthcare cost containment and patient safety.