Diagnostic performance of ultra-low dose versus standard dose CT for non-traumatic abdominal emergencies

Comparison of low-energy virtual monoenergetic images between photon-counting CT and energy-integrating detectors CT: A phantom study

PURPOSE

The purpose of this study was to assess image quality and dose level using a photon-counting CT (PCCT) scanner by comparison with a dual-source CT (DSCT) scanner on virtual monoenergetic images (VMIs) at low energy levels.

MATERIALS AND METHODS

A phantom was scanned using a DSCT and a PCCT with a volume CT dose index of 11 mGy, and additionally at 6 mGy and 1.8 mGy for PCCT. Noise power spectrum and task-based transfer function were evaluated from 40 to 70 keV on VMIs to assess noise magnitude and noise texture (fav) and spatial resolution on two iodine inserts (f50), respectively. A detectability index (d') was computed to assess the detection of two contrast-enhanced lesions according to the energy level used.

RESULTS

For all energy levels, noise magnitude values were lower with PCCT than with DSCT at 11 and 6 mGy, but greater at 1.8 mGy. fav values were higher with PCCT than with DSCT at 11 mGy (8.6 ± 1.5 [standard deviation [SD]%), similar at 6 mGy (1.6 ± 1.5 [SD]%) and lower at 1.8 mGy (-17.8 ± 2.2 [SD]%). For both inserts, f50 values were higher with PCCT than DSCT at 11- and 6 mGy for all keV levels, except at 6 mGy and 40 keV. d' values were higher with PCCT than with DSCT at 11- and 6 mGy for all keV and both simulated lesions. Similar d' values to those of the DSCT at 11 mGy, were obtained at 2.25 mGy for iodine insert at 2 mg/mL and at 0.96 mGy for iodine insert at 4 mg/mL at 40 keV.

CONCLUSION

Compared to DSCT, PCCT reduces noise magnitude and improves noise texture, spatial resolution and detectability on VMIs for all low-keV levels.

Comparison of the spectral performance between two dual-source CT systems on low-energy virtual monoenergetic images: A phantom study

PURPOSE

To compare the spectral performance of two different DSCT (DSCT-Pulse and DSCT-Force) on virtual monoenergetic images (VMIs) at low energy levels.

METHODS

An image quality phantom was scanned on the two DSCTs at three dose levels: 11/6/1.8 mGy. Level 3 of an advanced modeled iterative reconstruction algorithm was used. Noise power spectrum and task-based transfer function were computed on VMIs from 40 to 70 keV to assess noise magnitude and noise texture (fav) and spatial resolution (f50). A detectability index (d') was computed to assess the detection of one contrast-enhanced abdominal lesion as a function of the keV level used.

RESULTS

For all dose levels and all energy levels, noise magnitude was significantly higher (p < 0.05) with DSCT-Pulse than with DSCT-Force (12.6 ± 2.7 % at 1.8 mGy, 9.1 ± 2.9 % at 6 mGy and 4.0 ± 2.7 % at 11 mGy). For all energy levels, fav values were significantly higher (p < 0.05) with DSCT-Pulse than with DSCT-Force at 1.8 mGy (4.8 ± 3.9 %) and at 6 mGy (5.5 ± 2.5 %) but similar at 11 mGy (0.2 ± 3.6 %; p = 0.518). For all energy levels, f50 values were significantly higher with DSCT-Pulse than with DSCT-Force (12.7 ± 5.6 % at 1.8 mGy, 17.9 ± 4.5 % at 6 mGy and 13.1 ± 2.6 % at 11 mGy). For all keV, similar d' values were found with both DSCT-Force and DSCT-Pulse at 11 mGy (-1.0 ± 3.1 %; p = 0.084). For other dose levels, d' values were significantly lower with DSCT-Pulse than with DSCT-Force (9.1 ± 3.2 % at 1.8 mGy and -6.3 ± 3.9 % at 6 mGy).

CONCLUSION

Compared with the DSCT-Force, the DSCT-Pulse improved noise texture and spatial resolution, but noise magnitude was slightly higher and detectability slightly lower, particularly when the dose level was reduced.

The diagnostic utility of computed tomography scans performed for febrile neutropenia in a single centre

BACKGROUND

Computed tomography (CT) imaging has become a first line investigation for most cases of febrile neutropenia (FN) which can be the only sign of infection in oncology patients undergoing active chemotherapy and bone marrow transplants. The utility of routine non-targeted imaging remains unclear.

OBJECTIVE

To assess and compare the diagnostic rate between targeted, non-targeted and pan-scan CT in identifying an acute source of infection in adult oncology patients with FN.

MATERIALS AND METHODS

A retrospective observational study was conducted between February 2019 and March 2023 on 417 consecutive CT examinations for the clinical indication of source identification in FN. Scans were noted for the anatomical regions that were imaged and reports were classified as positive, negative or equivocal for infection. Pre-existing pathology was also noted. Results were tabulated and statistical analyses for comparison between groups of scans was performed using chi-square test.

RESULTS

All targeted regional scans had statistically significant difference in positive rate compared to non-targeted scans of the respective region; chest (Χ²(1)=18.11, P<.001); sinus (Χ²(1)=15.36, P<.001); abdomen and pelvis (Χ²(1)=5.95, P=.01). Pneumonia (41.3 %) was much more likely to be the diagnosis compared to sinusitis (16.2 %) in concomitant CT chest to sinus examinations (Χ²(1)=45.3, P<.001). Pan-scans had a higher incidence of positive diagnosis compared to all-targeted scans (Χ²(1)=4.91, P=.03) but when compared to higher yield targeted scans (abdomen and chest), there was no statistical difference (Χ²(1)=2.43, P=.12). 20/54 patients had pan-scans despite having localising symptoms.

CONCLUSION

Imaging guided by presenting signs and symptoms can help to reduce unnecessary imaging and promote more judicious use of non-targeted and pan-scan CT in current practices.

Imaging of hepato-pancreato-biliary emergencies in patients with cancer

Hepato-pancreato-biliary (HPB) emergencies in patients with cancer encompass an extensive array of various conditions, including primary malignancies that require prompt treatment, associated severe complications, and life-threatening consequences arising from treatment. In patients with cancer, the liver can be affected by chemotherapy-induced hepatotoxicity, veno-occlusive disease, Budd-Chiari syndrome, liver hemorrhage, and other complications arising from cancer therapy with all these complications requiring timely diagnosis and prompt treament. Cholecystitis induced by systemic anticancer therapies can result in severe conquences if not promptly identified and treated. The application of immunotherapy in cancer therapy is associated with cholangitis. Hemobilia, often caused by medical interventions, may require arterial embolization in patients with severe bleeding and hemodynamic instability. Malignant biliary obstruction in patients with biliary cancers may necessitate palliative strategies such as biliary stenting. In pancreatic cancer, patients often miss surgical treatment due to advanced disease stages or distant metastases, leading to potential emergencies at different treatment phases. This comprehensive review underscores the complexities of diagnostic and treatment roles of medical imaging in managing HPB emergencies in patients with cancer. It illustrates the crucial role of imaging techniques, including magnetic resonance imaging, computed tomography and ultrasound, in diagnosing and managing these conditions for timely intervention. It provides essential insights into the critical nature of early diagnosis and intervention in cancer-related HPB emergencies, ultimately impacting patient outcomes and survival rates.

Effects of ionizing radiation exposure during pregnancy

PURPOSE

To review the effects of ionizing radiation to the conceptus and the relationship to the timing of the exposure during pregnancy. To consider strategies that would mitigate potential harms associated with exposure to ionizing radiation during pregnancy.

METHODS

Data reported in the peer-reviewed literature on entrance KERMA received from specific radiological examinations were combined with published results from experiment or Monte Carlo modeling of tissue and organ doses per entrance KERMA to estimate total doses that could be received from specific procedures. Data reported in the peer-reviewed literature on dose mitigation strategies, best practices for shielding, consent, counseling and emerging technologies were reviewed.

RESULTS

For procedures utilizing ionizing radiation for which the conceptus is not included in the primary radiation beam, typical doses are well below the threshold for causing tissue reactions and the risk of induction of childhood cancer is low. For procedures that include the conceptus in the primary radiation field, longer fluoroscopic interventional procedures or multiphase/multiple exposures potentially could approach or exceed thresholds for tissue reactions and the risk of cancer induction must be weighed against the expected risk/benefit of performing (or not) the imaging examination. Gonadal shielding is no longer considered best practice. Emerging technologies such as whole-body DWI/MRI, dual-energy CT and ultralow dose studies are gaining importance for overall dose reduction strategies.

CONCLUSION

The ALARA principle, considering potential benefits and risks should be followed with respect to the use of ionizing radiation. Nevertheless, as Wieseler et al. (2010) state, "no examination should be withheld when an important clinical diagnosis is under consideration." Best practices require updates on current available technologies and guidelines.

First Results of a New Deep Learning Reconstruction Algorithm on Image Quality and Liver Metastasis Conspicuity for Abdominal Low-Dose CT

The study's aim was to assess the impact of a deep learning image reconstruction algorithm (Precise Image; DLR) on image quality and liver metastasis conspicuity compared with an iterative reconstruction algorithm (IR). This retrospective study included all consecutive patients with at least one liver metastasis having been diagnosed between December 2021 and February 2022. Images were reconstructed using level 4 of the IR algorithm (i4) and the Standard/Smooth/Smoother levels of the DLR algorithm. Mean attenuation and standard deviation were measured by placing the ROIs in the fat, muscle, healthy liver, and liver tumor. Two radiologists assessed the image noise and image smoothing, overall image quality, and lesion conspicuity using Likert scales. The study included 30 patients (mean age 70.4 ± 9.8 years, 17 men). The mean CTDI_vol was 6.3 ± 2.1 mGy, and the mean dose-length product 314.7 ± 105.7 mGy.cm. Compared with i4, the HU values were similar in the DLR algorithm at all levels for all tissues studied. For each tissue, the image noise significantly decreased with DLR compared with i4 (p < 0.01) and significantly decreased from Standard to Smooth (-26 ± 10%; p < 0.01) and from Smooth to Smoother (-37 ± 8%; p < 0.01). The subjective image assessment confirmed that the image noise significantly decreased between i4 and DLR (p < 0.01) and from the Standard to Smoother levels (p < 0.01), but the opposite occurred for the image smoothing. The highest scores for overall image quality and conspicuity were found for the Smooth and Smoother levels.

Abdominal atherosclerosis is not a risk factor of nonocclusive mesenteric ischemia among critically ill patients: a propensity matching study

BACKGROUND

Although risk factors of occlusive acute mesenteric ischemia are well known, triggering factors of nonocclusive mesenteric ischemia (NOMI) remain unclear. Alongside to the known risk factors for NOMI, the role of atherosclerosis is not fully elucidated. The purpose of our study was to evaluate whether abdominal atherosclerosis is a risk factor for NOMI.

METHODS

From January 2018 to December 2021, all consecutive patients admitted to the intensive care unit who underwent contrast-enhanced CT for suspicion of NOMI were evaluated for inclusion. Clinical and biological data at the time of the CT scan were retrospectively extracted from medical charts and reviewed by a single radiologist. The cohorts were matched by a 1:1 propensity score based on the patient clinical, biological data, and abdominal CT features associated with NOMI. Noncontrast CT acquisitions were used to calculate calcium scores of the abdominal aorta, celiac trunk, superior mesenteric artery (SMA), and common iliac artery according to the Agatston method. Analyses were performed before and after propensity score matching.

RESULTS

Among the 165 critically ill patients included, 59 (36%) had NOMI. Before matching analysis, the SMA and total abdominal Agatston calcium scores were not different between patients without and with NOMI (52.00 [IQR = 0, 473] vs. 137.00 [IQR = 0, 259], P = 0.857, respectively, and 7253 [IQR = 1220, 21738] versus 5802 [IQR = 2075, 15,084]; P = 0.723). The results were similar after matching 38 patients with NOMI and 38 without: 153 [IQR = 0, 665] versus 85 [IQR = 0, 240] (P = 0.312) for the SMA calcium score, and 7915 [IQR = 1812, 21561] versus 4139 [IQR = 1440, 9858] (P = 0.170) for the total abdominal Agatston calcium score.

CONCLUSION

Our results suggest that atherosclerosis is not a risk factor for NOMI in critically ill patients.

Deep learning-based low-dose CT for adaptive radiotherapy of abdominal and pelvic tumors

The shape and position of abdominal and pelvic organs change greatly during radiotherapy, so image-guided radiation therapy (IGRT) is urgently needed. The world’s first integrated CT-linac platform, equipped with fan beam CT (FBCT), can provide a diagnostic-quality FBCT for achieve adaptive radiotherapy (ART). However, CT scans will bring the risk of excessive scanning radiation dose. Reducing the tube current of the FBCT system can reduce the scanning dose, but it will lead to serious noise and artifacts in the reconstructed images. In this study, we proposed a deep learning method, Content-Noise Cycle-Consistent Generative Adversarial Network (CNCycle-GAN), to improve the image quality and CT value accuracy of low-dose FBCT images to meet the requirements of adaptive radiotherapy. We selected 76 patients with abdominal and pelvic tumors who received radiation therapy. The patients received one low-dose CT scan and one normal-dose CT scan in IGRT mode during different fractions of radiotherapy. The normal dose CT images (NDCT) and low dose CT images (LDCT) of 70 patients were used for network training, and the remaining 6 patients were used to validate the performance of the network. The quality of low-dose CT images after network restoration (RCT) were evaluated in three aspects: image quality, automatic delineation performance and dose calculation accuracy. Taking NDCT images as a reference, RCT images reduced MAE from 34.34 ± 5.91 to 20.25 ± 4.27, PSNR increased from 34.08 ± 1.49 to 37.23 ± 2.63, and SSIM increased from 0.92 ± 0.08 to 0.94 ± 0.07. The P value is less than 0.01 of the above performance indicators indicated that the difference were statistically significant. The Dice similarity coefficients (DCS) between the automatic delineation results of organs at risk such as bladder, femoral heads, and rectum on RCT and the results of manual delineation by doctors both reached 0.98. In terms of dose calculation accuracy, compared with the automatic planning based on LDCT, the difference in dose distribution between the automatic planning based on RCT and the automatic planning based on NDCT were smaller. Therefore, based on the integrated CT-linac platform, combined with deep learning technology, it provides clinical feasibility for the realization of low-dose FBCT adaptive radiotherapy for abdominal and pelvic tumors.

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.

Improved image quality and dose reduction in abdominal CT with deep-learning reconstruction algorithm: a phantom study

OBJECTIVES

To assess the impact of a new artificial intelligence deep-learning reconstruction (Precise Image; AI-DLR) algorithm on image quality against a hybrid iterative reconstruction (IR) algorithm in abdominal CT for different clinical indications.

METHODS

Acquisitions on phantoms were performed at 5 dose levels (CTDI_vol: 13/11/9/6/1.8 mGy). Raw data were reconstructed using level 4 of iDose⁴ (i4) and 3 levels of AI-DLR (Smoother/Smooth/Standard). Noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed: d' modelled detection of a liver metastasis (LM) and hepatocellular carcinoma at portal (HCCp) and arterial (HCCa) phases. Image quality was subjectively assessed on an anthropomorphic phantom by 2 radiologists.

RESULTS

From Standard to Smoother levels, noise magnitude and average NPS spatial frequency decreased and the detectability (d') of all simulated lesions increased. For both inserts, TTF values were similar for all three AI-DLR levels from 13 to 6 mGy but decreased from Standard to Smoother levels at 1.8 mGy. Compared to the i4 used in clinical practice, d' values were higher using the Smoother and Smooth levels and close for the Standard level. For all dose levels, except at 1.8 mGy, radiologists considered images satisfactory for clinical use for the 3 levels of AI-DLR, but rated images too smooth using the Smoother level.

CONCLUSION

Use of the Smooth and Smoother levels of AI-DLR reduces the image noise and improves the detectability of lesions and spatial resolution for standard and low-dose levels. Using the Smooth level is apparently the best compromise between the lowest dose level and adequate image quality.

KEY POINTS

• Evaluation of the impact of a new artificial intelligence deep-learning reconstruction (AI-DLR) on image quality and dose compared to a hybrid iterative reconstruction (IR) algorithm. • The Smooth and Smoother levels of AI-DLR reduced the image noise and improved the detectability of lesions and spatial resolution for standard and low-dose levels. • The Smooth level seems the best compromise between the lowest dose level and adequate image quality.

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 (RMSD_HU). 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 RMSD_iodine and iodine bias (IB).

RESULTS

The RMSD_HU 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 RMSD_iodine 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.

Impact of Morphotype on Image Quality and Diagnostic Performance of Ultra-Low-Dose Chest CT

Objectives: The image quality of an Ultra-Low-Dose (ULD) chest CT depends on the patient’s morphotype. We hypothesize that there is a threshold beyond which the diagnostic performance of a ULD chest CT is too degraded. This work assesses the influence of morphotype (Body Mass Index BMI, Maximum Transverse Chest Diameter MTCD and gender) on image quality and the diagnostic performance of a ULD chest CT. Methods: A total of 170 patients from three prior prospective monocentric studies were retrospectively included. Renewal of consent was waived by our IRB. All the patients underwent two consecutive unenhanced chest CT acquisitions with a full dose (120 kV, automated tube current modulation) and a ULD (135 kV, fixed tube current at 10 mA). Image noise, subjective image quality and diagnostic performance for nine predefined lung parenchyma lesions were assessed by two independent readers, and correlations with the patient’s morphotype were sought. Results: The mean BMI was 26.6 ± 5.3; 20.6% of patients had a BMI > 30. There was a statistically significant negative correlation of the BMI with the image quality (ρ = −0.32; IC95% = (−0.468; −0.18)). The per-patient diagnostic performance of ULD was sensitivity, 77%; specificity, 99%; PPV, 94% and NPV, 65%. There was no statistically significant influence of the BMI, the MTCD nor the gender on the per-patient and per-lesion diagnostic performance of a ULD chest CT, apart from a significant negative correlation for the detection of emphysema. Conclusions: Despite a negative correlation between the BMI and the image quality of a ULD chest CT, we did not find a correlation between the BMI and the diagnostic performance of the examination, suggesting a possible use of the ULD protocol in obese patients.

Body packing, body stuffing and body pushing: Characteristics and pitfalls on low-dose CT

Because of availability and low radiation dose level, low dose computed tomography (CT) is now commonly used to identify illicit in corpore drug transportation. This review illustrates the most common CT findings of in corpore drug transportation and describes complications due to in corpore drug transportation, with a special emphasis on low dose CT. Major information such as number of packets, exact location and aspect of packets must be assessed. Radiologist must be aware of the imaging characteristics of "in corpore" illicit drug transportation, and should know situations that may alter drug smugglers management.