Early results of ultra-low-dose CT-scan for extremity traumas in emergency room.

Background: Ultra-low dose computed tomography (ULD-CT) was shown to be a good alternative to digital radiographs in various locations. This study aimed to assess the diagnostic sensitivity and specificity of ULD-CT versus digital radiographs in patients consulting for extremity traumas in emergency room. Methods: Digital radiography and ULD-CT scan were performed in patients consulting at the emergency department (February-August 2018) for extremity traumas. Fracture detection was evaluated retrospectively by two blinded independent radiologists. Sensitivity and specificity were evaluated using best value comparator (BVC) and a Bayesian latent class model (LCM) approaches and clinical follow-up. Image quality, quality diagnostic and diagnostic confidence level were evaluated (Likert scale). The effective dose received was calculated. Results: Seventy-six consecutive patients (41 men, mean age: 35.2±13.2 years), with 31 wrists/hands and 45 ankles/feet traumas were managed by emergency physicians. According to clinical data, radiography had 3 false positive and 10 false negative examinations, and ULD-CT, 2 of each. Radiography and ULD-CT specificities were similar; sensitivities were lower for radiography, with BVC and Bayesian. With Bayesian, ULD-CT and radiography sensitivities were 90% (95% CI: 87-93%) and 76% (95% CI: 71-81%, P<0.0001) and specificities 96% (95% CI: 93-98%) and 93% (95% CI: 87-97%, P=0.84). The inter-observer agreement was higher for ULD-CT for all subjective indexes. The effective dose for ULD-CT and radiography was 0.84±0.14 and 0.58±0.27 µSv (P=0.002) for hand/wrist, and 1.50±0.32 and 1.44±0.78 µSv (P=NS) for foot/ankle. Conclusions: With an effective dose level close to radiography, ULD-CT showed better detection of extremities fractures in the emergency room and may allow treatment adaptation. Further studies need to be performed to assess impact of such examination in everyday practice. Trial Registration: ClinicalTrials.gov Identifier: NCT04832490.

Efficacy and safety of prostate artery embolization for patients with lower urinary tract symptoms and indwelling urinary catheter: A retrospective multicenter study.

PURPOSE: The purpose of this multicenter study was to evaluate the clinical success at three months of prostate artery embolization (PAE), assess PAE safety in centers with various experiences and identify factors associated with PAE success. PATIENTS AND METHODS: This multicenter, retrospective study included patients who underwent PAE for lower urinary tract symptoms (LUTS) including those with indwelling urinary catheter. PAE clinical success was defined as either 25% improvement of the International Prostate Symptom Score (IPSS) or 1-point improvement of quality of life (QoL) score, or catheter removal at three months. Multivariable analyses were performed using a logistic regression adjusted on patient variables, technical parameters and center experience in PAE. RESULTS: A total of 383 men (mean age, 68.4 ± 9.7 [standard deviation] years; range: 46-94) with LUTS, including 99 (25.8%) patients with indwelling urinary catheter, were included in seven centers from January 2017 to March 2019. Five patients reported major complications (1.3%), three (0.8%) penile ulceration, three (0.8%) acute urinary retention, one (0.3%) prostatic abscess, and 56 (14.6%) minor complications. Follow up data were available for 271 patients (center 1: n = 159; other centers: n = 112). Clinical success was reported in 232 patients (85.6%). In multivariable analyses, presence of cardiovascular comorbidities (diabetes, stroke history, myocardial infarction and lower limb artery disease) was the single independent variable inversely associated with PAE clinical success (odds ratio = 0.396; 95% confidence interval: 0.17-0.91; P = 0.029). There was no center effect. CONCLUSION: Our results show that PAE is safe and effective in centers with various PAE experiences. Cardiovascular comorbidity is the single independent variable associated with PAE failure.

Comparison of peak skin dose and dose map obtained with real-time software and radiochromic films in patients undergoing abdominopelvic embolization.

PURPOSE: The purpose of this study was to compare peak skin dose (PSD) and dose map calculated by Dose-Tracking-System® (DTS) software and measured with radiochromic films in patients undergoing abdominopelvic embolization. MATERIAL AND METHODS: The PSD measured by radiochromic films (PSDFilm) or calculated by DTS software (PSDDTS) were compared in patients who underwent abdominopelvic embolization between September 2020 and April 2021. Concordance between PSDFilm and PSDDTS was computed using the Lin's concordance correlation coefficient and the clinical concordance using Bland Altman analysis. PSD values were compared using the paired Mann-Whitney-Wilcoxon test. RESULTS: A total of 40 patients were included. There were 32 men and 8 women with a mean age of 73.0 ± 14.6 (SD) years (age range: 30-92 years). Median PSDFilm was 756 mGy (IQR: 390; 1094) and median PSDDTS was 768 mGy (IQR: 421; 1076), resulting in a median difference of -5% (IQR: -10%; 0%) between PSDFilm and PSDDTS (P = 0.024). The concordance correlation between PSDFilm and PSDDTS was substantial in patients (0.986; 95% CI: 0.977-0.992). Bland Altman analysis showed that PSDDTS was underestimated compared to PSDFilm by -36 mGy (95% CI: -68--4). Visually similar dose maps were found with DTS and radiochromic films. CONCLUSION: Real-time DTS software allows computing PSD with high accuracy and generating adequate dose map. PSDDTS was slightly underestimated compared to PSDFilm requiring the use of a correction factor for the PSDDTS to avoid lack of follow-up for some patients.

Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study.

BACKGROUND: New reconstruction algorithms based on deep learning have been developed to correct the image texture changes related to the use of iterative reconstruction algorithms. The purpose of this study was to evaluate the impact of a new deep learning image reconstruction [Advanced intelligent Clear-IQ Engine (AiCE)] algorithm on image-quality and dose reduction compared to a hybrid iterative reconstruction (AIDR 3D) algorithm and a model-based iterative reconstruction (FIRST) algorithm. METHODS: Acquisitions were carried out using the ACR 464 phantom (and its body ring) at six dose levels (volume computed tomography dose index 15/10/7.5/5/2.5/1 mGy). Raw data were reconstructed using three levels (Mild/Standard/Strong) of AIDR 3D, of FIRST and AiCE. Noise-power-spectrum (NPS) and task-based transfer function (TTF) were computed. Detectability index was computed to model the detection of a small calcification (1.5-mm diameter and 500 HU) and a large mass in the liver (25-mm diameter and 120 HU). RESULTS: NPS peaks were lower with AiCE than with AIDR 3D (-41%±6% for all levels) or FIRST (-15%±6% for Strong level and -41%±11% for both other levels). The average NPS spatial frequency was lower with AICE than AIDR 3D (-9%±2% using Mild and -3%±2% using Strong) but higher than FIRST for Standard (6%±3%) and Strong (25%±3%) levels. For acrylic insert, values of TTF at 50 percent were higher with AICE than AIDR 3D and FIRST, except for Mild level (-6%±6% and -13%±3%, respectively). For bone insert, values of TTF at 50 percent were higher with AICE than AIDR 3D but lower than FIRST (-19%±14%). For both simulated lesions, detectability index values were higher with AICE than AIDR 3D and FIRST (except for Strong level and for the small feature; -21%±14%). Using the Standard level, dose could be reduced by -79% for the small calcification and -57% for the large mass using AICE compared to AIDR 3D. CONCLUSIONS: The new deep learning image reconstruction algorithm AiCE generates an image-quality with less noise and/or less smudged/smooth images and a higher detectability than the AIDR 3D or FIRST algorithms. The outcomes of our phantom study suggest a good potential of dose reduction using AiCE but it should be confirmed clinically in patients.

Optimization of image quality and accuracy of low iodine concentration quantification as function of dose level and reconstruction algorithm for abdominal imaging using dual-source CT: A phantom study.

PURPOSE: The purpose of this study was to assess the impact of advanced modeled iterative reconstruction (ADMIRE) algorithm and dose levels on the accuracy of Hounsfield unit (HU) measurement, image noise and contrast-to-noise ratio (CNR) in virtual monochromatic images (VMIs) with low iodine concentrations, and the accuracy of iodine quantification. MATERIALS AND METHODS: A CT phantom was scanned with dual-source CT using abdomen-pelvis examination parameters at four dose levels: 5, 8, 11 and 20 mGy. Images were reconstructed using filtered-back projection (FBP) and ADMIRE levels 3 and 5 (A3-A5). HU accuracy was assessed calculating the root-mean-square deviation (RMSDHU). Image noise and CNR were computed on VMIs at 40/50/60/70 keV for 4 iodine inserts with 0.5, 1, 2 and 5 mg/mL concentrations. Accuracy of iodine quantification was assessed by the RMSDiodine and iodine bias (IB). RESULTS: The RMSDHU decreased significantly as the dose levels increased compared to 5 mGy, except for 8 mGy with A3 (P = 0.380) and with A5 level (P = 0.945). Noise increased by 63.0 ± 3.0 (standard deviation [SD])% from 20 mGy to 5 mGy. Noise decreased significantly by -53.8 ± 0.9 (SD) % with A5 compared to FBP. The CNR decreased by -43.1 ± 6.5 (SD)% from 20 mGy to 5 mGy. It increased using ADMIRE, and as the ADMIRE levels increased. The RMSDiodine and IB decreased as the dose level increased, and this was similar for all reconstruction types. CONCLUSION: ADMIRE strongly improves image quality in VMIs and slightly improves HU accuracy but does not affect the accuracy of iodine quantification.

Comparison of two deep learning image reconstruction algorithms in chest CT images: A task-based image quality assessment on phantom data.

PURPOSE: The purpose of this study was to compare the effect of two deep learning image reconstruction (DLR) algorithms in chest computed tomography (CT) with different clinical indications. MATERIAL AND METHODS: Acquisitions on image quality and anthropomorphic phantoms were performed at six dose levels (CTDIvol: 10/7.5/5/2.5/1/0.5mGy) on two CT scanners equipped with two different DLR algorithms (TrueFidelityTM and AiCE). Raw data were reconstructed using the filtered back-projection (FBP) and the lowest/intermediate/highest DLR levels (L-DLR/M-DLR/H-DLR) of each algorithm. Noise power spectrum, task-based transfer function (TTF) and detectability index (d') were computed: d' modelled detection of a soft tissue mediastinal nodule, ground-glass opacity, or high-contrast pulmonary lesion. Subjective image quality of anthropomorphic phantom images was analyzed by two radiologists. RESULTS: For the L-DLR/M-DLR levels, the noise magnitude was lower with TrueFidelityTM than with AiCE from 2.5 to 10 mGy. For H-DLR, noise magnitude was lower with AiCE . For L-DLR and M-DLR, the average NPS spatial frequency (fav) values were greater for AiCE except for 0.5 mGy. For H-DLR levels, fav was greater for TrueFidelityTM than for AiCE. TTF50% values were greater with AiCE for the air insert, and lower than TrueFidelityTM for the polyethylene insert. From 2.5 to10 mGy, d' was greater for AiCE than for TrueFidelityTM for H-DLR for all lesions, but similar for L-DLR and M-DLR. Image quality was rated clinically appropriate for all levels of both algorithms, for dose from 2.5 to 10 mGy, except for L-DLR of AiCE. CONCLUSION: DLR algorithms reduce the image-noise and improve lesion detectability. Their operations and properties impacted both noise-texture and spatial resolution.

Optimization of image quality and accuracy of low iodine concentration quantification as function of kVp pairs for abdominal imaging using dual-source CT: A phantom study.

PURPOSE: To determine the suitable kVp pair for optimal image quality of the virtual monochromatic images (VMIs) and iodine quantification accuracy at low concentration, using a third generation dual-source CT (DSCT). MATERIALS AND METHODS: Multi-energy CT phantoms with and without body rings were scanned with a DSCT using four kVp pairs (tube "A"/"B" voltage): 100/Sn150, 90/Sn150, 80/Sn150 and 70/Sn150 kVp. The reference mAs was adjusted to obtain a CTDIvol close to 11 mGy. HU values accuracy (RMSDHU), noise (SD) and contrast-to-noise ratio (CNR) of iodine inserts of 0.5, 1, 2 and 5 mg/mL concentrations were assessed on VMIs at 40/50/60/70 keV. Iodine quantification accuracy was assessed using the RMSDiodine and iodine bias (IBiodine). RESULTS: The RMSDHU decreased when the tube "A" voltage increased. The mean noise value increased significantly with tube "A" voltage (p < 0.001) but decreased between 80/Sn150 and 90/Sn150 kVp for the small phantom (1.1 ± 0.1%; p = 0.047). The CNR significantly decreased with tube "A" voltage (p < 0.001), except between 80/Sn150 and 90/Sn150 kVp for all inserts and between 90/Sn150 kVp and 100/Sn150 kVp for the 1.0 and 0.5 mg/mL inserts in the large phantom. In the small phantom, no significant difference was found between 80/Sn150 kVp and 90/Sn150 kVp for all inserts and between 80/Sn150, 90/Sn150 and 100/Sn150 kVp for the 1 and 0.5 mg/mL inserts. The RMSDiodine and IBiodine decreased as the tube "A" voltage of the kVp pair increased. CONCLUSION: The kVp pair of 70/Sn150 led to better image quality in VMIs and sufficient iodine accuracy.

Comparison of two versions of a deep learning image reconstruction algorithm on CT image quality and dose reduction: A phantom study.

PURPOSE: To compare the impact on CT image quality and dose reduction of two versions of a Deep Learning Image Reconstruction algorithm. MATERIAL AND METHODS: Acquisitions on the CT ACR 464 phantom were performed at five dose levels (CTDIvol : 10/7.5/5/2.5/1 mGy) using chest or abdomen pelvis protocol parameters. Raw data were reconstructed using the filtered-back projection (FBP), the enhanced level of AIDR 3D (AIDR 3De), and the three levels of AiCE (Mild, Standard, and Strong) for the two versions (AiCE V8 vs AiCE V10). The noise power spectrum (NPS) and task-based transfer function (TTF) for bone (high-contrast insert) and acrylic (low-contrast insert) inserts were computed. To quantify the changes of noise magnitude and texture, the square root of the area under the NPS curve and the average spatial frequency (fav ) of the NPS curve were measured. The detectability index (d') was computed to model the detectability of either a large mass in the liver or lung, or a small calcification or high contrast tissue boundaries. RESULTS: The noise magnitude was lower with both AiCE versions than with AIDR 3De. The noise magnitude was lower with AiCE V10 than with AiCE V8 (-4 ± 6% for Mild, -13 ± 3% for Standard, and -48 ± 0% for Strong levels). fav and TTF50% values for both inserts shifted towards higher frequencies with AiCE than with AIDR 3De. Compared to AiCE V08, fav shifted towards higher frequencies with AiCE V10 (45 ± 4%, 36 ± 3%, and 5 ± 4% for all levels, respectively). The TTF50% values shifted towards higher frequencies with AiCE V10 as compared with AiCE V8 for both inserts, except for the Strong level for the acrylic insert. Whatever the dose and AiCE levels, d' values were higher with AiCE V10 than with AiCE V8 for the small object/calcification and for the large object/lesion. CONCLUSION: As compared to AIDR 3De, lower noise magnitude and higher spatial resolution and detectability index were found with both versions of AiCE. As compared to AiCE V8, AiCE V10 reduced noise and improved spatial resolution and detectability without changing the noise texture in a simple geometric phantom, except for the Strong level. AiCE V10 seems to have a greater potential for dose reduction than AiCE V8.

Impact of four kVp combinations available in a dual-source CT on the spectral performance of abdominal imaging: A task-based image quality assessment on phantom data.

PURPOSE: To compare the spectral performance of four combinations of kVp available in a third generation dual-source CT (DSCT) on abdominal imaging. METHODS: An image-quality phantom was scanned with a DSCT using four kVp pairs (tube "A" voltage/tube "B" voltage): 100/Sn150 kVp, 90/Sn150 kVp, 80/Sn150 kVp, and 70/Sn150 kVp, classic parameters and dose level for abdomen examination (CTDIvol : 11 mGy). The noise power spectrum (NPS) and the task-based transfer function (TTF) of two inserts were computed on virtual monochromatic images (VMIs) at 40/50/60/70 keV and for mixed, low-, and high-kVp images. Detectability index (d') was computed on VMIs and mixed images to model the detection task of liver metastasis (LM) and hepatocellular carcinoma (HCC). Iodine quantification accuracy was assessed using the Root Mean Square Deviation (RMSDiodine ) and the iodine bias (IB). RESULTS: Noise magnitude decreased by -55%± 0% between 40 and 70 keV for all kVp pairs. Compared to 70/Sn150 kVp, noise magnitude was increased by 9% ± 0% with 80/Sn150 kVp, by 16% ± 1% with 90/Sn150 kVp and by 24%± 1% with 100/Sn150 kVp. The average NPS spatial frequency (fav ) shifted toward higher frequencies as energy level increased for all kVp pairs. Lowest fav values were found for 70/Sn150 kVp and highest for 100/Sn150 kVp. The value of TTF at 50% (f50 ) shifted toward lower frequencies with increasing energy level. The highest f50  values occurred for 100/Sn150 kVp and the lowest for 80/Sn150 kVp. For both lesions, d' was highest for 70/Sn150 kVp and lowest for 100/Sn150 kVp. Compared to 70/Sn150 kVp, d' decreased by -6% ± 3% with 80/Sn150 kVp, by -11% ± 2% with 90/Sn150 kVp and by -13%± 2% with 100/Sn150 kVp. For all acquisitions, the RSMDiodine and IB were the lowest for 100/Sn150 kVp (0.29 ± 0.10 mg/ml and 0.88 ± 0.30 mg/ml, respectively) and increased when the tube "A" voltage decreased (2.34 ± 0.29 mg/ml for 70/Sn150 kVp and 7.42 ± 0.51 mg/ml respectively). CONCLUSION: 70/Sn150 kVp presented the lowest image noise and highest detectability in VMIs of two small focal liver lesions. 100/Sn150 kVp presented the lowest image noise on mixed images and highest accuracy of iodine quantification in iodine images.

Impact of additional mattresses in emergency CT on the automated patient centering proposed by a 3D camera: a phantom study.

To assess the impact of the use of additional mattresses of different thicknesses on radiation dose and image noise based on the patient centering proposed by a 3D camera for CT. An anthropomorphic phantom was placed on mattresses of different thicknesses (from 3.5 to 13.5 cm) on the table of a CT scanner. The automated patient centering proposed by a 3D camera was analysed as a function of mattress thickness and corrected for table height. For this purpose, the impact on image noise in the lung tissues in the chest area and in the soft tissues in the abdomen-pelvis area, modulated mAs (mAsmod) by the tube current modulation system (TCM) and volume CT dose index (CTDIvol) was assessed slice-by-slice along the z-axis after CT scans. With the use of a mattress, the automated centering proposed by the 3D camera resulted in placement of the phantom above the isocentre. This incorrect positioning led to a significant increase in the mAsmod along the z-axis (p < 0.05) and in the CTDIvol. Image noise was significantly higher (p < 0.05) for automated phantom centering than with manual phantom centering. Differences of image noise between acquisitions with mattresses after automatic and manual phantom centering increased with the mattress thicknesses. The use of an additional mattress placed between the patient's back and the table-top would require correcting the vertical centering proposed by the 3D camera. This manual correction is essential to avoid increased dose delivered to the patient and higher image noise.

Impact of dose reduction and the use of an advanced model-based iterative reconstruction algorithm on spectral performance of a dual-source CT system: A task-based image quality assessment.

PURPOSE: To assess the impact of dose reduction and the use of an advanced modeled iterative reconstruction algorithm (ADMIRE) on image quality in low-energy monochromatic images from a dual-source dual energy computed tomography CT (DSCT) platform. MATERIALS AND METHODS: Acquisitions on an image-quality phantom were performed using DSCT equipment with 100/Sn150 kVp for four dose levels (CTDIvol: 20/11/8/5mGy). Raw data were reconstructed for six energy levels (40/50/60/70/80/100 keV) using filtered back projection and two levels of ADMIRE (A3/A5). Noise power spectrum (NPS) and task-based transfer function (TTF) were calculated on virtual monoenergetic images (VMIs). Detectability index (d') was computed to model the detection task of two enhanced iodine lesions as function of keV. RESULTS: Noise-magnitude was significantly reduced between 40 to 70 keV by -56±0% (SD) (range: -56%--55%) with FBP; -56±0% (SD) (-56%--56%) with A3; and -57±1% (SD) (range: -57%--56%) with A5. The average spatial frequency of the NPS peaked at 70 keV and decreased as ADMIRE level increased. TTF values at 50% were greatest at 40 keV and shifted towards lower frequencies as the keV increased. The detectability of both lesions increased with increasing dose level and ADMIRE level. For the simulated lesion with iodine at 2mg/mL, d' values peaked at 70 keV for all reconstruction types, except for A3 at 20 mGy and A5 at 11 and 20 mGy, where d' peaked at 60 keV. For the other simulated lesion, d' values were highest at 40 keV and decreased beyond. CONCLUSION: At low keV on VMIs, this study confirms that iterative reconstruction reduces the noise magnitude, improves the spatial resolution and increases the detectability of enhanced iodine lesions.