Ultra-low Dose CT Research Articles

Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy.

We investigated the feasibility of deep learning-based ultra-low dose kV-fan-beam computed tomography (kV-FBCT) image enhancement algorithm for clinical application in abdominal and pelvic tumor radiotherapy. A total of 76 patients of abdominal and pelvic tumors were prospectively selected. The Catphan504 was acquired with the same conditions as the standard phantom test set. We used a CycleGAN-based model for image enhancement. Normal dose CT (NDCT), ultra-low dose CT (LDCT) and deep learning enhanced CT (DLR) were evaluated by subjective and objective analyses in terms of imaging quality, HU accuracy, and image signal-to-noise ratio (SNR). The image noise of DLR was significantly reduced, and the contrast-to-noise ratio (CNR) was significantly improved compared to the LDCT. The most significant improvement was the acrylic which represented soft tissue in CNR from 1.89 to 3.37, improving by 76%, nearly approaching the NDCT, and in low-density resolution from 7.64 to 12.6, improving by 64%. The spatial frequencies of MTF10 and MTF50 in DLR were 4.28 and 2.35 cycles/mm in DLR, respectively, which are higher than LDCT 3.87 and 2.12 cycles/mm, and even slightly higher than NDCT 4.15 and 2.28 cycles/mm. The accuracy and stability of HU values of DLR were similar to NDCT. The image quality evaluation of the two doctors agreed well with DLR and NDCT. A two-by-two comparison between groups showed that the differences in image scores of LDCT compared with NDCT and DLR were all statistically significant (p<0.05), and the subjective scores of DLR were close to NDCT. The image quality of DLR was close to NDCT with reduced radiation dose, which can fully meet the needs of conventional image-guided adaptive radiotherapy (ART) and achieve the quality requirements of clinical radiotherapy. The proposed method provided a technical basis for LDCT-guided ART.

Low-Field MRI for Dental Imaging in Pediatric Patients With Supernumerary and Ectopic Teeth: A Comparative Study of 0.55 T and Ultra-Low-Dose CT.

This study sought to elucidate the diagnostic performance of 0.55 T magnetic resonance imaging (MRI) for pediatric dental imaging, specifically in terms of the image quality (IQ) for detecting ectopic and/or supernumerary teeth, compared with routine ultra-low-dose computed tomography (ULD-CT) of the jaw. A total of 16 pediatric patients (mean age: 12.4 ± 2.6 years, range: 9-17 years) with ectopic and/or supernumerary teeth screened from January 2023 to January 2024 were enrolled in this prospective, single-center study. All patients underwent ULD-CT as the clinical reference standard and 0.55 T MRI as the study scan on the same day. A 0.6-mm isotropic 3-dimensional T1w FLASH sequence was developed with a dedicated field of view of the upper and lower jaws. ULD-CT was performed using a new single-source computed tomography (CT) scanner equipped with a tin filter (Sn100, slice thickness: 1 mm, quality reference mAs: 24). The IQ for the tooth axis, the tooth length, the tooth root, root resorptions, cysts, the periodontal ligament space, and the mandibular canal was evaluated twice by 3 senior readers using a 5-point Likert scale (LS) (LS score of 1: insufficient, 3: reduced IQ but sufficient for clinical use, and 5: perfect) and compared between both methods. Subsequently, the results were dichotomized into nonvalid (LS score of ≤2) and valid (LS score of ≥3) for clinical use. A total of 49 ectopic and/or supernumerary teeth in 16 pediatric patients were investigated using ULD-CT (CTDI: 0.43 ± 0.09 mGy) and 0.55 T MRI. The mean MRI acquisition time was 9:45 minutes. Motion artifacts were nonsignificantly different between 0.55 T MRI and ULD-CT (P = 0.126). The IQ for the tooth axis, the tooth root, root resorptions, and cysts was similar between the methods. The IQ for the periodontal ligament space and tooth length favored ULD-CT by 14% (confidence interval [CI]: 4.3%-24%) and 7.5% (CI: 1.8%-13%), respectively, whereas that for the mandibular canal favored 0.55 T MRI by -35% (CI: -54%-16%). Sufficient IQ was found especially for cystic lesions (CT: 100% sufficient, MRI: 95% sufficient), the tooth root (CT: 100%, MRI: 98%), root resorptions (CT: 94%; MRI: 85%), the tooth axis (CT: 100%; MRI: 98%), and the tooth length (CT: 99%; MRI: 91%). The findings indicate that 0.55 T MRI is a feasible, radiation-free technique for delineating ectopic and/or supernumerary teeth in pediatric patients. Nevertheless, to date, 0.55 T MRI has not yet been able to provide an optimal IQ for all anatomical tooth and jaw structures. In cases of advanced clinical indications that require optimal spatial resolution, high-resolution CT or cone-beam CT may still be necessary.

Use of ultra-low contrast dose CT aortography for the management of aortic aneurysmal disease

PurposeComputed tomography aortography (CTA) is used in the assessment of aortic pathologies and planning of surgical intervention. However, its dependence on iodinated contrast can result in development of contrast-induced acute kidney injury (CI-AKI). The potential concern of CI-AKI has spurred research into the potential of administration of low contrast volumes in CTA investigations while maintaining overall diagnostic appeal. Several studies have shown that CTA using contrast volumes as low as 30 mL (equivalent to 10.5 g of iodine) can still yield scans of diagnostic quality. We present a retrospective pilot study to evaluate the feasibility of utilising an ultra-low volume of iodinated intravenous contrast in a population of patients with severe renal insufficiency with referral from our vascular surgery unit for CTA evaluation of the thoracic and abdominal aorta.MethodsThis retrospective pilot study examined 12 CTA scans performed with 20 mLs of iodinated contrast and assessed image quality with both quantitative and qualitative markers. All scans were performed on a Siemens SOMATOM Force dual-source CT scanner. Quantitative assessment values were measured via attenuation values at eight aortoiliac locations and used to calculate a signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) at each location. Qualitative analysis of image quality and viability for surgical intervention was obtained from subjective clinical assessment by an interventional radiologist and vascular surgeon.ResultsObtained quantitative assessment values included mean attenuation 189.9 HU, mean SNR 9.6 and mean CNR 8.0. All 12 scans demonstrated individual mean SNR values above predetermined quality thresholds while only five scans produced individual mean CNR values above threshold. Eleven of 12 scans were determined to be of sufficient quality for diagnosis and planning of surgical intervention.ConclusionsOur results suggest that CTA utilising an ultra-low contrast dose of 20 mLs (6 g iodine) yields scans of diagnostic quality for therapeutic decision-making in vascular surgical intervention.

A Multidisciplinary Assessment of Standard Low Dose Versus Ultra-low Dose Chest CT Scans for Pectus Excavatum Imaging

AimEvaluation of ultra-low dose chest CT imaging for the assessment of pectus excavatum severity as determined by pediatric radiologists and pediatric surgeons using Haller (HI) and Correction indices (CI). MethodsA single institution, prospective evaluation of patients being evaluated for pectus excavatum were scanned with a standard low-dose chest CT protocol (CARE) followed by a consecutive ultra-low dose CT scan (ULTRA). 3 surgeons and 4 radiologists were instructed to determine HI and CI in each series. The Intraclass Correlation Coefficient (ICC) was used to calculate the agreement level between CARE and ULTRA. Bland–Altman (BA) and scatter plots were also performed to determine bias of each approach. Results32 patients had CARE and ULTRA consecutively. The ICC for HI demonstrated good reliability with a value of 0.89 and excellent reliability for CI with a value of 0.91. The reliability for HI was greater in the surgeon group (0.89) compared to the radiologist group (0.88). The reliability for CI was greater in the radiologist group (0.92) compared to the surgeon group (0.90). The Bland Altman plots for the HI and CI demonstrate no consistent bias for CARE or ULTRA approach when evaluating HI and CI. ConclusionUltra-low dose CT scan imaging compared to standard low-dose CT appears to be a reliable alternative for evaluating PE severity as assessed by HI and CI. This work supports the evaluation and potential development of a standardized CT imaging protocol capable of reducing radiation exposure without sacrificing imaging for PE patients. Level of Evidence2.

Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm.

To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR). This prospective cross-sectional study included 60 patients referred to our institution for assessment or follow-up of solid pulmonary nodules. All patients underwent low-dose (LD) and ULD chest CT within the same examination session. LD CT data were reconstructed using Adaptive Statistical Iterative Reconstruction-V (ASIR-V), whereas ULD CT data were reconstructed using DLIR and ASIR-V. ULD CT images were reviewed by 2 readers and LD CT images were reviewed by an experienced thoracic radiologist as the reference standard. Quantitative image quality analysis was performed, and the detectability of pulmonary nodules was assessed according to their size and location. The effective radiation dose for ULD CT and LD CT were 0.13±0.01 and 1.16±0.6mSv, respectively. Over the whole population, LD CT revealed 733 nodules. At ULD, DLIR images significantly exhibited better image quality than ASIR-V images. The overall sensitivity of DLIR reconstruction for the detection of solid pulmonary nodules from the ULD CT series was 93% and 82% for the 2 readers, with a good to excellent agreement with LD CT (ICC=0.82 and 0.66, respectively). The best sensitivities were observed in the middle lobe (97% and 85%, respectively). At ULD, DLIR reconstructions, with minimal radiation exposure that could facilitate large-scale screening, allow the detection of pulmonary nodules with high sensitivity in an unrestricted BMI population.

Evaluating Lung Changes in Long COVID: Ultra-Low-Dose vs. Standard-Dose CT Chest.

Frequent chest CTs within a short period during follow-up of long COVID patients may increase the risk of radiation-related health effects in the exposed individuals. We aimed to assess the image quality and diagnostic accuracy of ultra-low-dose CT (ULDCT) chest compared to standard-dose CT (SDCT) in detecting lung abnormalities associated with long COVID. In this prospective study, 100 long COVID patients with respiratory dysfunction underwent SDCT and ULDCT chest that were compared in terms of objective (signal-to-noise ratio, SNR) and subjective image quality (image graininess, sharpness, artifacts, and diagnostic accuracy along with the European guidelines on image quality criteria for CT chest), detection of imaging patterns of long COVID, CT severity score, and effective radiation dose. Additionally, the diagnostic performance of ULDCT was compared among obese (BMI≥30kg/m2) and non-obese (BMI<30kg/m2) subjects. The mean age of study participants was 53 ± 12.9years, and 68% were male. The mean SNR was 31.4 ± 5.5 and 11.3 ± 4.6 for SDCT and ULDCT respectively (p< 0.0001). Common findings seen on SDCT included ground-glass opacities (GGOs, 77%), septal thickening/reticulations (67%), atelectatic/parenchymal bands (63%) and nodules (26%). ULDCT provided sharp images, with no/minimal graininess, and high diagnostic confidence in 81%, 82% and 80% of the cases respectively. The sensitivity of ULDCT for various patterns of long COVID was 72.7% (GGOs), 71.6% (interlobular septal thickening/reticulations), 100% (consolidation), 81% (atelectatic/parenchymal bands) and 76.9% (nodules). ULDCT scans in non-obese subjects exhibited a significantly higher sensitivity (88% vs. 60.3%, p < 0.0001) and diagnostic accuracy (97.7% vs. 84.9%, p < 0.0001) compared to obese subjects. ULDCT showed very strong correlation with SDCT in terms of CT severity score (r = 0.996, p < 0.0001). The mean effective radiation dose with ULDCT was 0.25 ± 0.02mSv with net radiation dose reduction of 94.8% ± 1.7% (p < 0.0001) when compared to SDCT (5.5 ± 1.96mSv). ULDCT scans achieved comparable diagnostic accuracy to SDCT for detecting long COVID lung abnormalities in non-obese patients, while significantly reducing radiation exposure.

Accuracy of Ultralow-Dose Photon-counting CT in the Detection of Lung Changes after Lung Transplant.

Background Data on the diagnostic accuracy of ultralow-dose (ULD) CT protocols for periodic surveillance in recipients of lung transplant are lacking. Purpose To assess the potential for radiation dose reduction using ULD photon-counting CT (PCT) to detect lung abnormalities in recipients of lung transplant during repeat CT follow-up. Materials and Methods Consecutive adult recipients of lung transplant undergoing same-day standard-of-care low-dose (LD) and ULD PCT from March 2023 to May 2023 were prospectively included. The ULD protocols were performed with two target effective doses comprising 20% (hereafter, ULD1) and 10% (hereafter, ULD2) of the standard LD protocol. The 1-mm reconstructions were reviewed by three readers. Subjective image quality, the visibility of certain anatomic structures (using a five-point Likert scale), and the presence of lung abnormalities were independently assessed. The χ2 or t tests were used to evaluate differences between the ULD1 and ULD2 protocols. Results A total of 82 participants (median age, 64 years [IQR, 54-69 years]; 47 male) were included (41 participants for each ULD protocol). The mean effective doses per protocol were 1.41 mSv ± 0.44 (SD) for LD, 0.26 mSv ± 0.08 for ULD1, and 0.17 mSv ± 0.04 for ULD2. According to three readers, the subjective image quality of the ULD images was deemed diagnostic (Likert score ≥3) in 39-40 (ULD1) and 40-41 (ULD2) participants, and anatomic structures could be adequately visualized (Likert score ≥3) in 33-41 (ULD1) and 34-41 (ULD2) participants. The detection accuracy for individual lung anomalies exceeded 70% for both ULD protocols, except for readers 1 and 3 detecting proximal bronchiectasis and reader 3 detecting bronchial wall thickening and air trapping. No evidence of a statistically significant difference in noise (P = .96), signal-to-noise ratio (P = .77), or reader accuracy (all P ≥ .05) was noted between the ULD protocols. Conclusion ULD PCT was feasible for detecting lung abnormalities following lung transplant, with a tenfold radiation dose reduction. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Ciet in this issue.

3D Landmark scout imaging accurately assesses presence and extent of coronary calcification with lower radiation exposure

BackgroundCardiac CT for coronary artery calcium (CAC) scoring exposes patients to 1 ​mSv of radiation. A new CT scout method utilizing ultra-low dose CT (3D Landmark) offers tomographic cross-sectional imaging, which provides axial images from which CAC can be estimated. The purpose of our study is to analyze the association between estimated CAC burden on 3D Landmark scout imaging vs dedicated ECG-gated CACS. MethodsConsecutive patients over a 9-month period undergoing non-contrast ECG-gated CACS planned with 3D Landmark scout imaging were included. Extent of CAC on 3D Landmark scout imaging was scored from 0 to 3 (none, mild, moderate, severe). Agatston CACS was converted to an ordinal score from 0 to 3, corresponding to absent (0), mild (1–99), moderate (100–400), or severe (>400). Fischer's exact test, weighted kappa coefficient, and paired t-tests were used for analysis. ResultsOf 150 patients, 51.3% were female with mean age 49.0 ​± ​16.8 and BMI 28.6 ​± ​12.3. Sensitivity of 3D Landmark in identifying calcium was 96.2%, with specificity of 100%. There was strong interrater agreement between 3D Landmark calcium scoring and CACS, with weighted kappa coefficient 0.97 ​± ​0.01(CI 0.95–0.99).Radiation dose-length-product was significantly lower for 3D Landmark imaging vs. dedicated ECG-gated CACS (9.7 ​± ​3.6 vs 43.8 ​± ​26.4 ​mGy ​cm, p ​< ​0.001) despite longer scan length (465.0 ​± ​160.8 vs 123.0 ​± ​12.7 ​mm, respectively). ConclusionEstimated coronary artery calcium on 3D Landmark scout images correlates strongly with Agatston CACS, demonstrating utility in assessing cardiovascular risk without introducing additional radiation or costs.

Can clinicians identify community-acquired pneumonia on ultralow-dose CT? A diagnostic accuracy study

BackgroundWithout increasing radiation exposure, ultralow-dose computed tomography (CT) of the chest provides improved diagnostic accuracy of radiological pneumonia diagnosis compared to a chest radiograph. Yet, radiologist resources to rapidly report the chest CTs are limited. This study aimed to assess the diagnostic accuracy of emergency clinicians’ assessments of chest ultralow-dose CTs for community-acquired pneumonia using a radiologist’s assessments as reference standard.MethodsThis was a cross-sectional diagnostic accuracy study. Ten emergency department clinicians (five junior clinicians, five consultants) assessed chest ultralow-dose CTs from acutely hospitalised patients suspected of having community-acquired pneumonia. Before assessments, the clinicians attended a focused training course on assessing ultralow-dose CTs for pneumonia. The reference standard was the assessment by an experienced emergency department radiologist. Primary outcome was the presence or absence of pulmonary opacities consistent with community-acquired pneumonia. Sensitivity, specificity, and predictive values were calculated using generalised estimating equations.ResultsAll clinicians assessed 128 ultralow-dose CTs. The prevalence of findings consistent with community-acquired pneumonia was 56%. Seventy-eight percent of the clinicians’ CT assessments matched the reference assessment. Diagnostic accuracy estimates were: sensitivity = 83% (95%CI: 77–88), specificity = 70% (95%CI: 59–81), positive predictive value = 80% (95%CI: 74–84), negative predictive value = 78% (95%CI: 73–82).ConclusionThis study found that clinicians could assess chest ultralow-dose CTs for community-acquired pneumonia with high diagnostic accuracy. A higher level of clinical experience was not associated with better diagnostic accuracy.

Effect of Deep Learning Image Reconstruction Algorithms on Radiomic Features of Pulmonary Nodules in Ultra-Low-Dose CT.

The purpose of this study is to explore the impact of deep learning image reconstruction (DLIR) algorithm on the quantification of radiomic features in ultra-low-dose computed tomography (ULD-CT) compared with adaptive statistical iterative reconstruction-Veo (ASIR-V). One hundred eighty-three patients with pulmonary nodules underwent standard-dose computed tomography (SDCT) (4.30 ± 0.36 mSv) and ULD-CT (UL-A, 0.57 ± 0.09 mSv or UL-B, 0.33 ± 0.04 mSv). SDCT was the reference standard using (ASIR-V) at 50% strength (50%ASIR-V). ULD-CT was reconstructed with 50%ASIR-V, DLIR at medium and high strength (DLIR-M, DLIR-H). Radiomics analysis extracted 102 features, and the intraclass correlation coefficient (ICC) quantified reproducibility between ULD-CT and SDCT reconstructed by 50%ASIR-V, DLIR-M, and DLIR-H for each feature. Among 102 radiomic features, the percentages of reproducibility of 50%ASIR-V, DLIR-M, and DLIR-H were 48.04% (49/102), 49.02% (50/102), and 52.94% (54/102), respectively. Shape and first order features demonstrated high reproducibility across different reconstruction algorithms and radiation doses, with mean ICC values exceeding 0.75. In texture features, DLIR-M and DLIR-H showed improved mean ICC values for pure ground glass nodules (pGGNs) from 0.69 ± 0.23 to 0.75 ± 0.18 and 0.81 ± 0.12, respectively, compared with 50%ASIR-V. Similarly, the mean ICC values for solid nodules (SNs) increased from 0.60 ± 0.19 to 0.66 ± 0.14 and 0.69 ± 0.13, respectively. Additionally, the mean ICC values of texture features for pGGNs and SNs in both ULD-CT groups decreased with reduced radiation dose. DLIR can improve the reproducibility of radiomic features at ultra-low doses compared with ASIR-V. In addition, pGGNs showed better reproducibility at ultra-low doses than SNs.

Ultra-low dose hip CT-based automated measurement of volumetric bone mineral density at proximal femoral subregions.

Forty to fifty percent of women and 13%-22% of men experience an osteoporosis-related fragility fracture in their lifetimes. After the age of 50 years, the risk of hip fracture doubles in every 10 years. x-Ray based DXA is currently clinically used to diagnose osteoporosis and predict fracture risk. However, it provides only 2-D representation of bone and is associated with other technical limitations. Thus, alternative methods are needed. To develop and evaluate an ultra-low dose (ULD) hip CT-based automated method for assessment of volumetric bone mineral density (vBMD) at proximal femoral subregions. An automated method was developed to segment the proximal femur in ULD hip CT images and delineate femoral subregions. The computational pipeline consists of deep learning (DL)-based computation of femur likelihood map followed by shape model-based femur segmentation and finite element analysis-based warping of a reference subregion labeling onto individual femur shapes. Finally, vBMD is computed over each subregion in the target image using a calibration phantom scan. A total of 100 participants (50 females) were recruited from the Genetic Epidemiology of COPD (COPDGene) study, and ULD hip CT imaging, equivalent to 18 days of background radiation received by U.S. residents, was performed on each participant. Additional hip CT imaging using a clinical protocol was performed on 12 participants and repeat ULD hip CT was acquired on another five participants. ULD CT images from 80 participants were used to train the DL network; ULD CT images of the remaining 20 participants as well as clinical and repeat ULD CT images were used to evaluate the accuracy, generalizability, and reproducibility of segmentation of femoral subregions. Finally, clinical CT and repeat ULD CT images were used to evaluate accuracy and reproducibility of ULD CT-based automated measurements of femoral vBMD. Dice scores of accuracy (n=20), reproducibility (n=5), and generalizability (n=12) of ULD CT-based automated subregion segmentation were 0.990, 0.982, and 0.977, respectively, for the femoral head and 0.941, 0.970, and 0.960, respectively, for the femoral neck. ULD CT-based regional vBMD showed Pearson and concordance correlation coefficients of 0.994 and 0.977, respectively, and a root-mean-square coefficient of variation (RMSCV) (%) of 1.39% with the clinical CT-derived reference measure. After 3-digit approximation, each of Pearson and concordance correlation coefficients as well as intraclass correlation coefficient (ICC) between baseline and repeat scans were 0.996 with RMSCV of 0.72%. Results of ULD CT-based bone analysis on 100 participants (age (mean±SD) 73.6±6.6 years) show that males have significantly greater (p<0.01) vBMD at the femoral head and trochanteric regions than females, while females have moderately greater vBMD (p=0.05) at the medial half of the femoral neck than males. Deep learning, combined with shape model and finite element analysis, offers an accurate, reproducible, and generalizable algorithm for automated segmentation of the proximal femur and anatomic femoral subregions using ULD hip CT images. ULD CT-based regional measures of femoral vBMD are accurate and reproducible and demonstrate regional differences between males and females.

Diagnostic Accuracy of Ultra-Low Dose CT Compared to Standard Dose CT for Identification of Fresh Rib Fractures by Deep Learning Algorithm.

The present study aimed to evaluate the diagnostic accuracy of ultra-low dose computed tomography (ULD-CT) compared to standard dose computed tomography (SD-CT) in discerning recent rib fractures using a deep learning algorithm detection of rib fractures (DLADRF). A total of 158 patients undergoing forensic diagnosis for rib fractures were included in this study: 50 underwent SD-CT, and 108 were assessed using ULD-CT. Junior and senior radiologists independently evaluated the images to identify and characterize the rib fractures. The sensitivity of rib fracture diagnosis by radiologists and radiologist + DLADRF was better using SD-CT than ULD-CT. However, the diagnosis sensitivity of DLADRF using ULD-CT alone was slightly more than SD-CT. Nonetheless, no substantial differences were observed in specificity, positive predictive value, and negative predictive value between SD-CT and ULD-CT by the same radiologist, radiologist + DLADRF, and DLADRF (P > 0.05). The area under the curve (AUC) of receiver operating characteristic indicated that senior radiologist + DLADRF was significantly better than senior and junior radiologists, junior radiologists + DLADRF, and DLADRF alone using SD-CT or ULD-CT (all P < 0.05). Also, junior radiologists + DLADRF was better with ULD-CT than senior and junior radiologists (P < 0.05). The AUC of the rib fracture diagnosed by senior radiologists did not differ from DLADRF using ULD-CT. Also, no significant differences were observed between junior + AI and senior and between junior and DLADRF using SD-CT. DLADRF enhanced the diagnostic performance of radiologists in detecting recent rib fractures. The diagnostic outcomes between SD-CT and ULD-CT across radiologists' experience and DLADRF did not differ significantly.

Ultra-low-dose vs. standard-of-care-dose CT of the chest in patients with post-COVID-19 conditions-a prospective intra-patient multi-reader study.

To conduct an intrapatient comparison of ultra-low-dose computed tomography (ULDCT) and standard-of-care-dose CT (SDCT) of the chest in terms of the diagnostic accuracy of ULDCT and intrareader agreement in patients with post-COVID conditions. We prospectively included 153 consecutive patients with post-COVID-19 conditions. All participants received an SDCT and an additional ULDCT scan of the chest. SDCTs were performed with standard imaging parameters and ULDCTs at a fixed tube voltage of 100 kVp (with tin filtration), 50 ref. mAs (dose modulation active), and iterative reconstruction algorithm level 5 of 5. All CT scans were separately evaluated by four radiologists for the presence of lung changes and their consistency with post-COVID lung abnormalities. Radiation dose parameters and the sensitivity, specificity, and accuracy of ULDCT were calculated. Of the 153 included patients (mean age 47.4 ± 15.3 years; 48.4% women), 45 (29.4%) showed post-COVID lung abnormalities. In those 45 patients, the most frequently detected CT patterns were ground-glass opacities (100.0%), reticulations (43.5%), and parenchymal bands (37.0%). The accuracy, sensitivity, and specificity of ULDCT compared to SDCT for the detection of post-COVID lung abnormalities were 92.6, 87.2, and 94.9%, respectively. The median total dose length product (DLP) of ULDCTs was less than one-tenth of the radiation dose of our SDCTs (12.6 mGy*cm [9.9; 15.5] vs. 132.1 mGy*cm [103.9; 160.2]; p < 0.001). ULDCT of the chest offers high accuracy in the detection of post-COVID lung abnormalities compared to an SDCT scan at less than one-tenth the radiation dose, corresponding to only twice the dose of a standard chest radiograph in two views. Ultra-low-dose CT of the chest may provide a favorable, radiation-saving alternative to standard-dose CT in the long-term follow-up of the large patient cohort of post-COVID-19 patients.

Ultra-low-dose computed tomography and chest X-ray in follow-up of high-grade soft tissue sarcoma—a prospective comparative study

Ultra-low-dose computed tomography (ULD-CT) may combine the high sensitivity of conventional computed tomography (CT) in detecting sarcoma pulmonary metastasis, with a radiation dose in the same magnitude as chest X-ray (CXR). Fifty patients with non-metastatic high-grade soft tissue sarcoma treated with curative intention were recruited. Their follow-up involved both CXR and ULD-CT to evaluate their different sensitivity. Suspected findings were confirmed by conventional CT if necessary. Patients with isolated pulmonary metastases were treated with surgery or stereotactic body radiation therapy (SBRT) with curative intent if possible. The median effective dose from a single ULD-CT study was 0.27 mSv (range 0.12 to 0.89 mSv). Nine patients were diagnosed with asymptomatic lung metastases during the follow-up. Only three of them were visible in CXR and all nine in ULD-CT. CXR had therefore only a 33% sensitivity compared to ULD-CT. Four patients were operated, and one had SBRT to all pulmonary lesions. Eight of them, however, died of the disease. Two patients developed symptomatic metastatic recurrence involving extrapulmonary sites+/−the lungs between two imaging rounds. ULD-CT has higher sensitivity for the detection of sarcoma pulmonary metastasis than CXR, with a radiation dose considerably lower than conventional CT.Clinical trial registration: NCT05813808. 04-14-2023.

Diagnostic Accuracy in Detecting Fungal Infection with Ultra-Low-Dose Computed Tomography (ULD-CT) Using Filtered Back Projection (FBP) Technique in Immunocompromised Patients.

Purpose: To compare the accuracy of ultra-low-dose (uLDCT) to standard-of-care low-dose chest CT (LDCT) in the detection of fungal infection in immunocompromised (IC) patients. Method and Materials: One hundred IC patients had paired chest CT scans performed with LDCT followed by uLDCT. The images were independently reviewed by three chest radiologists who assessed the image quality (IQ), diagnostic confidence, and detection of major (macro nodules, halo sign, cavitation, consolidation) and minor (4-10 mm nodules, ground-glass opacity) criteria for fungal disease using a five-point Likert score. Discrepant findings were adjudicated by a fourth chest radiologist. Box-whisker plots were used to analyze IQ and diagnostic confidence. Inter-rater reliability was assessed using interclass correlation coefficients (ICCs). The statistical difference between LDCT and uLDCT results was assessed using Wilcoxon paired test. Results: Lung reconstructions had IQ and diagnostic confidence scores (mean ± std) of 4.52 ± 0.47 and 4.63 ± 0.51 for LDCT and 3.85 ± 0.77 and 4.01 ± 0.88 for uLDCT. The images were clinically acceptable except for uLDCT in obese patients (BMI ≥ 30 kg/m2), which had an IQ ranking from poor to excellent (scores 1 to 5). The accuracy in detecting major and minor radiological findings with uLDCT was 96% and 84% for all the patients. The inter-rater agreements were either moderate, good, or excellent, with ICC values of 0.51-0.96. There was no significant statistical difference between the uLDCT and LDCT ICC values (p = 0.25). The effective dose for uLDCT was one quarter that of LDCT (CTDIvol = 0.9 mGy vs. 3.7 mGy). Conclusions: Thoracic uLDCT, at a 75% dose reduction, can replace LDCT for the detection of fungal disease in IC patients with BMI < 30.0 kg/m2.

Diagnostic performance and image quality of an image-based denoising algorithm applied to radiation dose-reduced CT in diagnosing acute appendicitis

PurposeTo evaluate diagnostic performance and image quality of ultralow-dose CT (ULDCT) in diagnosing acute appendicitis with an image-based deep-learning denoising algorithm (IDLDA).MethodsThis retrospective multicenter study included 180 patients (mean ± standard deviation, 29 ± 9 years; 91 female) who underwent contrast-enhanced 2-mSv CT for suspected appendicitis from February 2014 to August 2016. We simulated ULDCT from 2-mSv CT, reducing the dose by at least 50%. Then we applied an IDLDA on ULDCT to produce denoised ULDCT (D-ULDCT). Six radiologists with different experience levels (three board-certified radiologists and three residents) independently reviewed the ULDCT and D-ULDCT. They rated the likelihood of appendicitis and subjective image qualities (subjective image noise, diagnostic acceptability, and artificial sensation). One radiologist measured image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). We used the receiver operating characteristic (ROC) analyses, Wilcoxon’s signed-rank tests, and paired t-tests.ResultsThe area under the ROC curves (AUC) for diagnosing appendicitis ranged 0.90–0.97 for ULDCT and 0.94–0.97 for D-ULDCT. The AUCs of two residents were significantly higher on D-ULDCT (AUC difference = 0.06 [95% confidence interval, 0.01–0.11; p = .022] and 0.05 [0.00–0.10; p = .046], respectively). D-ULDCT provided better subjective image noise and diagnostic acceptability to all six readers. However, the response of board-certified radiologists and residents differed in artificial sensation (all p ≤ .003). D-ULDCT showed significantly lower image noise, higher SNR, and higher CNR (all p < .001).ConclusionAn IDLDA can provide better ULDCT image quality and enhance diagnostic performance for less-experienced radiologists.

Pedicle screw accuracy placed with assistance of machine vision technology in patients with neuromuscular scoliosis.

Pedicle screws are the primary method of vertebral fixation in scoliosis surgery, but there are lingering concerns over potential malposition. The rates of pedicle screw malposition in pediatric spine surgery vary from 10% to 21%. Malpositioned screws can lead to potentially catastrophic neurological, vascular, and visceral complications. Pedicle screw positioning in patients with neuromuscular scoliosis is challenging due to a combination of large curves, complex pelvic anatomy, and osteopenia. This study aimed to determine the rate of pedicle screw malposition, associated complications, and subsequent revision from screws placed with the assistance of machine vision navigation technology in patients with neuromuscular scoliosis undergoing posterior instrumentation and fusion. A retrospective analysis of the records of patients with neuromuscular scoliosis who underwent thoracolumbar pedicle screw insertion with the assistance of machine-vision image guidance navigation was performed. Screws were inserted by either a staff surgeon, orthopaedic fellow, or orthopaedic resident. Post-operative ultra-low dose CT scans were used to assess pedicle screw accuracy. The Gertzbein classification was used to grade any pedicle breaches (grade 0, no breach; grade 1, <2 mm; grade 2, 2-4 mm; grade 3, >4 mm). A screw was deemed accurate if no breach was identified (grade 0). 25 patients were included in the analysis, with a mean age of 13.6 years (range 11 to 18 years; 13/25 (52.0%) were female. The average pre-operative supine Cobb angle was 90.0 degrees (48-120 degrees). A total of 687 screws from 25 patients were analyzed (402 thoracic, 241 lumbosacral, 44 S2 alar-iliac (S2AI) screws). Surgical trainees (fellows and orthopaedic residents) inserted 46.6% (320/687) of screws with 98.8% (4/320) accuracy. The overall accuracy of pedicle screw insertion was 98.0% (Grade 0, no breach). All 13 breaches that occurred in the thoracic and lumbar screws were Grade 1. Of the 44 S2AI screws placed, one screw had a Grade 3 breach (2.3%) noted on intra-operative radiographs following rod placement and correction. This screw was subsequently revised. None of the breaches resulted in neuromonitoring changes, vessel, or visceral injuries. Machine vision navigation technology combined with careful free-hand pedicle screw insertion techniques demonstrated high levels of pedicle screw insertion accuracy, even in patients with challenging anatomy.

Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CT.

To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR). The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool. DLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P < .001). In general, DLIR showed superior subjective image quality for ULDCT images (P < .001) and comparable quality for LDCT images compared to ASIR-V50 (P = .01-1). The per-nodule sensitivities of observers for Lung-RADS category 3-4 nodules were 70.6-88.2% and 64.7-82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P = .50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P = .65). DLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images.

Novel Prostate Biopsy Technique Using Imaging Fusion in a Patient With Absent Rectum

OBJECTIVETo describe a novel technique using pre-operative multi-parametric prostate MRI and intraoperative CT 3D/3D fusion for systematic and targeted prostate biopsy in a patient lacking a rectum. METHODSA 70-year-old male with prior total colectomy for ulcerative colitis was referred for elevated PSA (8.01) with PIRADS 4 lesion on MRI. Under general anesthesia, an ultra-low-dose (ULD) cone beam CT was performed in supine position using a robotic-armed fluoroscopy system (Artis Zeego Care+Clear™, Siemens). 3D/3D auto-registration of the femoral heads and prostate from the MRI and ULD CT was performed. The prostate edges and two areas of concern were marked. Then, reduced-dose fluoroscopy-guided prostate biopsy was performed transperineally using triangulation technique. RESULTS27 prostate biopsy cores were obtained. Grade group 5 (Gleason 4+5=9) prostate cancer was identified in two cores from the targeted lesion and one core from the prostate base. The remaining twenty-four biopsies were negative for malignancy. Surgical time was 81 minutes. PSMA scan demonstrated no metastasis or lymphadenopathy. Robotic-assisted laparoscopic radical prostatectomy was performed without complications. Final pathology demonstrated T3a, grade group 5 prostate adenocarcinoma involving 10% of the prostate volume with negative surgical margins. CONCLUSIONSThis is the initial report of fluoroscopy-guided prostate biopsy using imaging fusion techniques in a patient without a rectum. This technique allowed precise identification of localized, very high-risk prostate cancer with over three times the number of cores, and much lower radiation dose, than typical CT-guided biopsies. Our technique could provide a new paradigm in targeted prostate biopsy.

P604 Exploring the role of chest X-ray in latent tuberculosis screening in Inflammatory Bowel Disease

Abstract Background Patients being treated with biologics or JAK inhibitors have an increased reactivation risk of latent tuberculosis infection (LTBI), and this is associated with a more severe infective course. Current ECCO guidance recommends the screening for LTBI by a combination of patient clinical data and epidemiological factors, chest X-ray (CXR), and tuberculin skin test or interferon-gamma release assay (IGRA) prior to starting immunosuppressive treatment. The evidence supporting CXR is categorized at evidence level 5, and information on its use in screening for LTBI originates from two decades ago. Apart from radiation exposure, a single posterior-anterior view has limited diagnostic capabilities. This study aims to determine the relation between CXR findings and IGRA test results in patients undergoing LTBI screening before commencing immunosuppressive therapy for Inflammatory bowel disease (IBD) patients in a low-risk population. Methods This is a retrospective analysis whereby IBD patients (≥16 years) on biologics or JAK inhibitors were identified from our local database. The clinical data, epidemiological, CXR and IGRA result were then analysed from the electronic case records. The IGRA test used was the QuantiFERON-TB Gold. Results A total of 356 patients were included in the study, with 58.7% (n=209) being male. Among these patients, the majority had Crohn's disease (66.6%, n=236), 30.1% (n=109) had ulcerative colitis, and the remaining 2.8% were classified as having IBD-unclassified. Most patients (97.1%) had been exposed to anti-TNF alpha medications and 13.2% were exposed to multiple biologicals. The mean age of IBD diagnosis was 30.3 years (SD+/-15.7). None of the patients had any clinical suggestion nor any evidence for exposure to TB. None of the CXRs performed revealed any radiological signs of prior TB infection. 93.8% (n=334) of the IGRA results were negative and 4.2% (n=15) were categorized as indeterminate. Among those with indeterminate results, 66.7% (n=10) were undergoing corticosteroid treatment for active disease. The remaining 2.0% (n=7) had a positive IGRA test and were treated for LTBI.None of these patients had TB reactivation when then treated with biologicals. Conclusion This research highlights that performing routinely a CXR in individuals with a negative IGRA offers little to no benefit, and subjects patients to unnecessary radiation. It also demonstrates the importance of conducting the IGRA test early, before initiating any immunosuppressive treatment. Furthermore, in patients with a negative IGRA, if chest imaging is required for screening purposes, opting for an ultra-low dose CT might be more prudent than a CXR, as it visualises the lungs better and the radiation exposure is not substantially different.