CT Image Quality Research Articles

CBCT-to-CT Synthesis for Cervical Cancer Adaptive Radiotherapy via U-Net-Based Model Hierarchically Trained with Hybrid Dataset.

To develop a deep learning framework based on a hybrid dataset to enhance the quality of CBCT images and obtain accurate HU values. A total of 228 cervical cancer patients treated in different LINACs were enrolled. We developed an encoder-decoder architecture with residual learning and skip connections. The model was hierarchically trained and validated on 5279 paired CBCT/planning CT images and tested on 1302 paired images. The mean absolute error (MAE), peak signal to noise ratio (PSNR), and structural similarity index (SSIM) were utilized to access the quality of the synthetic CT images generated by our model. The MAE between synthetic CT images generated by our model and planning CT was 10.93 HU, compared to 50.02 HU for the CBCT images. The PSNR increased from 27.79 dB to 33.91 dB, and the SSIM increased from 0.76 to 0.90. Compared with synthetic CT images generated by the convolution neural networks with residual blocks, our model had superior performance both in qualitative and quantitative aspects. Our model could synthesize CT images with enhanced image quality and accurate HU values. The synthetic CT images preserved the edges of tissues well, which is important for downstream tasks in adaptive radiotherapy.

Reduction of contrast medium for transcatheter aortic valve replacement planning using a spectral detector CT: a prospective clinical trial

IntroductionThis study investigated the use of dual-energy spectral detector computed tomography (CT) and virtual monoenergetic imaging (VMI) reconstructions in pre-interventional transcatheter aortic valve replacement (TAVR) planning. We aimed to determine the minimum required contrast medium (CM) amount to maintain diagnostic CT imaging quality for TAVR planning.MethodsIn this prospective clinical trial, TAVR candidates received a standardized dual-layer spectral detector CT protocol. The CM amount (Iohexol 350 mg iodine/mL, standardized flow rate 3 mL/s) was reduced systematically after 15 patients by 10 mL, starting at 60 mL (institutional standard). We evaluated standard, and 40- and 60-keV VMI reconstructions. For image quality, we measured signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and diameters in multiple vessel sections (i.e., aortic annulus: diameter, perimeter, area; aorta/arteries: minimal diameter). Mixed regression models (MRM), including interaction terms and clinical characteristics, were used for comparison.ResultsSixty consecutive patients (mean age, 79.4 ± 7.5 years; 28 females, 46.7%) were included. In pre-TAVR CT, the CM reduction to 40 mL is possible without affecting the image quality (MRM: SNR: –1.1, p = 0.726; CNR: 0.0, p = 0.999). VMI 40-keV reconstructions showed better results than standard reconstructions with significantly higher SNR (+ 6.04, p < 0.001). Reduction to 30 mL CM resulted in a significant loss of quality (MRM: SNR: –12.9, p < 0.001; CNR: –13.9, p < 0.001), regardless of the reconstruction. Across the reconstructions, we observed no differences in the metric evaluation (p > 0.914).ConclusionAmong TAVR candidates undergoing pre-interventional CT at a dual-layer spectral detector system, applying 40 mL CM is sufficient to maintain diagnostic image quality. VMI 40-keV reconstructions improve the vessel attenuation and are recommended for evaluation.Clinical relevance statementContrast medium reduction to 40 mL in pre-interventional transcatheter aortic valve replacement CT using dual-energy CT maintains image quality, while 40-keV virtual monoenergetic imaging reconstructions enhance vessel attenuation. These results offer valuable recommendations for interventional transcatheter aortic valve replacement evaluation and potentially improve nephroprotection in patients with compromised renal function.Key Points• Patients undergoing transcatheter aortic valve replacement (TAVR), requiring pre-interventional CT, are often multimorbid with impaired renal function.• Using a spectral detector dual-layer CT, contrast medium reduction to 40 mL is feasible, maintaining diagnostic image quality.• The additional application of virtual monoenergetic image reconstructions with 40 keV improves vessel attenuation significantly in clinical practice.

Quality improvement of images with metal artifact reduction using a noise recovery technique in computed tomography.

In metal artifact reduction (MAR) in computed tomography (CT) based on projection data inpainting, X-ray photon noise has not been considered in the inpainting process. This study aims to assess the effectiveness of a MAR technique incorporating noise recovery in such projection data regions, compared with existing MAR techniques based on projection data normalization (NMAR), including one with frequency splitting (FSNMAR). Phantoms simulating hip prostheses and dental fillings were scanned using a 64-row multi slice CT scanner. The projection data was processed by NMAR and NMAR with noise recovery (NRNMAR); the processed data was sent back to the CT system for reconstruction. For the phantoms and clinical cases with hip prostheses and dental fillings, images were reconstructed without MAR, and with NMAR, NRNMAR, and FSNMAR (incorporated in the CT system). To validate the efficacy of noise recovery, noise power spectra (NPSs) were measured from the images of the hip prosthesis phantom with and without metals. The artifact index (AI) was compared between NRNMAR and FSNMAR. The resultant NPSs of NRNMAR were very similar to those of phantom images with no metals, endorsing the efficacy of noise recovery. The NMAR images had unnatural noise textures and FSNMAR caused additional streaks. NRNMAR exhibited some significant improvements in these respects: It reduced the AI by as much as 66.2-88.6% compared to FSNMAR, except for the case of a unilateral prosthesis. In conclusion, NRNMAR, which simply adds white noise to the projection data, would be effective in improving the quality of CT images with metal artifacts reduction.

A novel degradable PCL/PLLA strapping band for internal fixation of fracture

Early fracture fixation is the critical factor in fracture healing. Common internal fracture implants are made of metallic materials, which often affects the imaging quality of CT and MRI. Most patients will choose secondary surgery to remove the internal fixation implants, which causes secondary damage to them. The development of new degradable internal fracture implants has attracted more and more attention from orthopedic surgeons and researchers. Based on these problems, we improved the various properties of medical grade polycaprolactone (PCL) by adding poly(L-lactide) (PLLA). We produced PCL/PLLA strapping bands with different mass ratios by injection molding. We compared the mechanical properties, degradation properties, cell biocompatibility, bone marrow mesenchymal stem cells (BMSCs) adhesion, proliferation, osteogenic differentiation and fracture fixation effect of these strapping bands. The results showed that the tensile strength and yield force of the strapping bands increased with the increase of the content of PLLA. The addition of PLLA could significantly improve the mechanical strength in the early stage and accelerate the degradation rate of the strapping band. PCL/PLLA (80/20) strapping band had no significant cytotoxicity toward rBMSCs and could promote osteogenic differentiation of rBMSCs. The strapping band could ensure femoral fracture healing of beagles in 3 months and didn’t cause damage to the surrounding tissues and main organs. This study will provide some new insights into the biodegradable products of PCL/PLLA blends for internal fixation of fracture.Graphical We produced novel degradable PCL/PLLA strapping bands with different mass ratios by injection molding. We tested the biological safety of the prepared internal fixation strapping bands for fracture, such as cell experiment in vitro and animal experiment, and studied the degradation behavior in vitro. The strapping bands could ensure femoral fracture healing of beagles. This study will provide some new insights into the biodegradable products of PCL/PLLA blends for internal fixation of fracture. A Immunofluorescence staining of rBMSCs (live cells: green; dead cells: red). B Young’s modulus change curve during strapping bands degradation. C The implantation process of strapping bands. D Micro-CT images of the beagle’s fracture recovery after the operation.

Implementation of dual-energy material decomposition technique in stationary CT baggage scanner withπ-angle sparsity for enhancing threat detection

Two-dimensional X-ray inspection systems are widely used in aviation security applications; however, they have inherent limitations in recognizing the three-dimensional (3D) shapes of hidden objects. Therefore, there is a growing demand for the implementation of advanced 3D X-ray inspection systems at airports for more accurate detection of threats in luggage and personal belongings. In this study, we designed a new stationary computed tomography (CT) baggage scanner with π-angle sparsity (i.e., 20 pairs of X-ray sources and line detectors were placed within a scan angle of 180°) and compressed sensing (CS)-based reconstruction, and implemented a dual-energy material decomposition (DEMD) technique in the proposed system to separate soft and dense materials of an examined object to enhance threat detection. To validate the efficacy of the proposed approach (CS/180°/P20), we conducted a feasibility study using numerical simulation before its practical implementation. Polychromatic projections were emulated at X-ray tube voltages of 60 and 140 kVp, and DEMD was applied to the projections prior to CT reconstruction. Conventional and dual-energy CT images were reconstructed using both standard filtered-backprojection (FBP) and state-of-the-art CS-based algorithms to compare the image quality. According to our simulation results, the CS-reconstructed images were almost unaffected by the clearly evident streak artifacts on the FBP-reconstructed images because of the use of 20 extreme sparse-view projections, and the image quality of the dual-energy CT images obtained using the proposed CT configuration was similar to that obtained using the conventional CT configuration with 720 dense projections, indicating the efficacy of the proposed approach. Consequently, high-quality dual-energy CT images of soft and dense materials were successfully obtained using the proposed stationary CT configuration.

Computed Tomography Image Quality Analysis Using Reverse Engineering for Prosthesis Design

Computed Tomography Image Quality Analysis Using Reverse Engineering for Prosthesis Design

SemiMAR: Semi-Supervised Learning for CT Metal Artifact Reduction.

Metal artifacts lead to CT imaging quality degradation. With the success of deep learning (DL) in medical imaging, a number of DL-based supervised methods have been developed for metal artifact reduction (MAR). Nonetheless, fully-supervised MAR methods based on simulated data do not perform well on clinical data due to the domain gap. Although this problem can be avoided in an unsupervised way to a certain degree, severe artifacts cannot be well suppressed in clinical practice. Recently, semi-supervised metal artifact reduction (MAR) methods have gained wide attention due to their ability in narrowing the domain gap and improving MAR performance in clinical data. However, these methods typically require large model sizes, posing challenges for optimization. To address this issue, we propose a novel semi-supervised MAR framework. In our framework, only the artifact-free parts are learned, and the artifacts are inferred by subtracting these clean parts from the metal-corrupted CT images. Our approach leverages a single generator to execute all complex transformations, thereby reducing the model's scale and preventing overlap between clean part and artifacts. To recover more tissue details, we distill the knowledge from the advanced dual-domain MAR network into our model in both image domain and latent feature space. The latent space constraint is achieved via contrastive learning. We also evaluate the impact of different generator architectures by investigating several mainstream deep learning-based MAR backbones. Our experiments demonstrate that the proposed method competes favorably with several state-of-the-art semi-supervised MAR techniques in both qualitative and quantitative aspects.

Comparison of image quality, contrast administration, and radiation doses in pediatric abdominal dual-layer detector dual-energy CT using propensity score matching analysis

PurposeTo compare the image quality, contrast administration, and radiation dose between single-energy CT (SECT) and dual-energy CT (DECT) in pediatric patients. MethodsFrom March to December 2021, children who underwent abdominal SECT or DECT were retrospectively included in this study. The DECT group received 10–30 % less contrast than the routine dose. CT images were obtained at hepatic venous phase using a routine reconstruction method (iDose4). DECT scans were additionally reconstructed with a virtual monoenergetic image (VMI) at 40 and 65 keV. Quantitative image evaluations compared the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the liver, portal vein, and pancreas. Qualitative analysis assessed degree of contrast enhancement, lesion or organ conspicuity, image noise, artificiality, and overall image quality. ResultsAmong 318 patients, 112 (median age, 16 years; 56 in each group) were included after propensity score matching. Compared with the SECT group, DECT group with iDose4 demonstrated lower CNRs and SNRs, while VMI at 40 or 65 keV showed no significant difference. In qualitative analysis, iDose4 produced higher scores on artificiality, and VMI at 40 keV demonstrated superior contrast enhancement and lesion conspicuity in the DECT group. Overall image quality was higher with VMI 65 keV among the DECT patients, and there was no significant difference compared to SECT. The volume CT dose index (CTDIvol) did not differ significantly between the two groups (median, 2.8 mGy vs. 2.9 mGy; p = 0.802). The injected contrast volume was reduced by 10 % in the DECT group. ConclusionPediatric abdominal DECT with reduced contrast administration showed no significant differences in image quality and radiation dose compared to SECT.

Low-dose CT image quality evaluation method based on radiomics and deep residual network with attention mechanism

Low-dose computed tomography (CT) produces less radiation but may degrade image quality. Research on automatic evaluation and classification of low-dose CT image quality is necessary, which is helpful to clinicians. A low-dose CT image quality evaluation method is proposed based on radiomics and deep learning. After pre-training on the ImageNet dataset, the trained deep learning model is then transferred to the target dataset of low-dose CT image. Region of interest is obtained through a deep residual network with attention mechanism. Pyradiomics is used to extract radiomics features of ROI, and T-test and LASSO algorithm are used for feature screening. Finally, support vector machine, random forest and logistic regression models are adopted to classify and predict the quality of low-dose CT image based on the selected radiomics features. Experimental results show that the trained deep residual network model with attention module can achieve 92% recognition accuracy of region of interest, and the evaluation accuracy of the three machine learning models achieve 86.6% (support vector machine), 84.5% (random forest) and 61.7% (logistic regression), respectively. Transfer learning can effectively reduce the required number of samples for the training of deep learning model and avoid overfitting. After the introduction of attention module in deep residual network model, accuracy rises faster, and the final accuracy is slightly higher than that of deep residual network model without attention module, but the improvement is slightly lower than expected. Radiomics features of region of interest reflect the quality differences of low-dose CT images which can be employed for the quality evaluation of low-dose CT image.

An evaluation of an energy independent CT reconstruction algorithm for use in radiotherapy treatment planning.

Radiotherapy treatment planning relies upon density information provided by CT for accurate dose calculations. Hounsfield units (HUs) are converted to electron/physical density via an energy dependant calibration curve. Multiple curves are required to make full use of the available accelerating potentials (kVp). The curves are bi-linear with a discontinuity occurring at soft-tissue densities. The commercial algorithm, DirectDensityTM (Siemens Healthcare GmbH), constructs a single calibration curve covering all available kVp. This enables the optimisation of the CT image quality, e.g. in terms of contrast, or the reduction of the imaging dose, whilst rendering the radiotherapy treatment dose calculation robust to the energy used to acquire the CT image. We report our investigations on the clinical utilisation of the DirectDensityTM algorithm for radiotherapy treatments, by using all accelerating potentials, i.e. from 70 kVp up to 140 kVp, available at our CT treatment simulator, in contrast to previous studies that were limited to accelerating potentials spanning a subset of the available kVp. The DirectDensityTM (DD) reconstruction algorithm available on a SOMATOM go.Open Pro CT scanner (Siemens Healthineers) was evaluated using the RayStation v. 9 treatment planning system (RaySearch Laboratories, Stockholm, Sweden) and a CIRS Model 002LFC IMRT Thorax Phantom (SunNuclear, Melbourne, FL), which was imaged at all available kVp with clinical protocols corresponding to various anatomical sites. The DD images were compared to those with the standard reconstruction algorithm acquired only at 120 kVp, as per our routine clinical practice. The effect of increasing kVp on HU is investigated for relevant tissue substitutes. In addition, a dosimetric comparison is performed for a VMAT plan technique with 6 MV X-rays using retrospective patient CT data sets representing four anatomical sites (pelvis, thorax, brain and "head and neck") with five patients for each site. The original dose distributions were calculated on images acquired at 120 kVp using the standard clinical iterative reconstruction (Qr40) and compared with dose distributions recalculated on images reconstructed with the new DD (Sm40) algorithm. The maximum difference for radiotherapy doses calculated using images of the phantom reconstructed with Qr40 (120 kVp) or DD (all available kVp) was 0.73%. The patient plans on the anatomically representative sites studied here showed a mean PTV dose difference of -0.2% (s.d. 0.7) for D99%, -0.4% (s.d. 0.4%) for D50% and -0.3% (s.d. 0.4%) for D2%. Incidentally, we found a previously unreported decrease in HU, mostly notable for bone type inserts (~34 HU (cortical bone)), at 110 kVp for the DD reconstructed images. The effect was not noted for the standard Qr40 reconstructions. DD has a minimal dosimetric impact in the dose calculations for radiotherapy treatments and could be implemented with existing clinical workflows. Attention should be paid to the HU values for images acquired at 110 kVp (DD algorithm), which warrants further investigation. This is the first paper where DD was evaluated at all available kVp, leading to the incidental discovery of abnormal HU values at 110 kVp for this algorithm.

An organ-effective modulation for non-contrast chest computed tomography imaging: effect on image quality and thyroid exposure reduction.

We investigate the efficacy of organ-effective modulation (OEM) technique for thyroid dose reduction among various body habitus and its impact on image quality in chest non-contrast computed tomography (CT). We prospectively enrolled 64 patients who underwent non-contrast chest CT from January to May 2022. The skin-absorbed radiation dose over the thyroid (Dthyroid) was obtained using a thermoluminescence dosemeter. Signal-to-noise ratio and image noise was also quantitatively assessed. In subjective analyses, two radiologists independently evaluated images based on a 5-point scale. The OEM group showed a markedly decrease in Dthyroid when compared with the non-OEM group (p<0.05). No significant difference was observed regarding the image noise (p<0.05), except for the ventral air space. The subjective scores of two radiologists showed no significant differences between the non-OEM and OEM groups. OEM can effectively reduce the radiation exposure of thyroid without compromising on image quality in non-contrast chest CT.

Superior metal artifact reduction of tin-filtered low-dose CT in imaging of lumbar spinal instrumentation compared to conventional computed tomography

ObjectiveTo compare the image quality of low-dose CT (LD-CT) with tin filtration of the lumbar spine after metal implants to standard clinical CT, and to evaluate the potential for metal artifact and dose reduction.Materials and methodsCT protocols were optimized in a cadaver torso. Seventy-four prospectively included patients with metallic lumbar implants were scanned with both standard CT (120 kV) and tin-filtered LD-CT (Sn140kV). CT dose parameters and qualitative measures (1 = worst,4 = best) were compared. Quantitative measures included noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and the width and attenuation of the most prominent hypodense metal artifact. Standard CT and LD-CT were assessed for imaging findings.ResultsTin-filtered LD-CT was performed with 60% dose saving compared to standard CT (median effective dose 3.22 mSv (quartile 1–3: 2.73–3.49 mSv) versus 8.02 mSv (6.42–9.27 mSv; p < .001). Image quality of CT and tin-filtered low-dose CT was good with excellent depiction of anatomy, while image noise was lower for CT and artifacts were weaker for tin-filtered LD-CT. Quantitative measures also revealed increased noise for tin-filtered low-dose CT (41.5HU), lower SNR (2) and CNR (0.6) compared to CT (32HU,3.55,1.03, respectively) (all p < .001). However, tin-filtered LD-CT performed superior regarding the width and attenuation of hypodense metal artifacts (2.9 mm and -767.5HU for LD-CT vs. 4.1 mm and -937HU for CT; all p < .001). No difference between methods was observed in detection of imaging findings.ConclusionTin-filtered LD-CT with 60% dose saving performs comparable to standard CT in detection of pathology and surgery related complications after lumbar spinal instrumentation, and shows superior metal artifact reduction.

Insights about cervical lymph nodes: Evaluating deep learning–based reconstruction for head and neck computed tomography scan

PurposeThis study aimed to investigate differences in cervical lymph node image quality on dual-energy computed tomography (CT) scan with datasets reconstructed using filter back projection (FBP), hybrid iterative reconstruction (IR), and deep learning–based image reconstruction (DLIR) in patients with head and neck cancer. MethodSeventy patients with head and neck cancer underwent follow-up contrast-enhanced dual-energy CT examinations. All datasets were reconstructed using FBP, hybrid IR with 30 % adaptive statistical IR (ASiR-V), and DLIR with three selectable levels (low, medium, and high) at 2.5- and 0.625-mm slice thicknesses. Herein, signal, image noise, signal-to-noise ratio, and contrast-to-noise ratio of lymph nodes and overall image quality, artifact, and noise of selected regions of interest were evaluated by two radiologists. Next, cervical lymph node sharpness was evaluated using full width at half maximum. ResultsDLIR exhibited significantly reduced noise, ranging from 3.8 % to 35.9 % with improved signal-to-noise ratio (11.5–105.6 %) and contrast-to-noise ratio (10.5–107.5 %) compared with FBP and ASiR-V, for cervical lymph nodes (p < 0.001). Further, 0.625-mm-thick images reconstructed using DLIR-medium and DLIR-high had a lower noise than 2.5-mm-thick images reconstructed using FBP and ASiR-V. The lymph node margins and vessels on DLIR-medium and DLIR-high were sharper than those on FBP and ASiR-V (p < 0.05). Both readers agreed that DLIR had a better image quality than the conventional reconstruction algorithms. ConclusionDLIR-medium and -high provided superior cervical lymph node image quality in head and neck CT. Improved image quality affords thin-slice DLIR images for dose-reduction protocols in the future.

Generating synthetic images from cone beam computed tomography using self-attention residual UNet for head and neck radiotherapy

Background and purposeAccurate CT numbers in Cone Beam CT (CBCT) are crucial for precise dose calculations in adaptive radiotherapy (ART). This study aimed to generate synthetic CT (sCT) from CBCT using deep learning (DL) models in head and neck (HN) radiotherapy. Materials and methodsA novel DL model, the 'self-attention-residual-UNet' (ResUNet), was developed for accurate sCT generation. ResUNet incorporates a self-attention mechanism in its long skip connections to enhance information transfer between the encoder and decoder. Data from 93 HN patients, each with planning CT (pCT) and first-day CBCT images were used. Model performance was evaluated using two DL approaches (non-adversarial and adversarial training) and two model types (2D axial only vs. 2.5D axial, sagittal, and coronal). ResUNet was compared with the traditional UNet through image quality assessment (Mean Absolute Error (MAE), Peak-Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM)) and dose calculation accuracy evaluation (DVH deviation and gamma evaluation (1 %/1mm)). ResultsImage similarity evaluation results for the 2.5D-ResUNet and 2.5D-UNet models were: MAE: 46±7 HU vs. 51±9 HU, PSNR: 66.6±2.0 dB vs. 65.8±1.8 dB, and SSIM: 0.81±0.04 vs. 0.79±0.05. There were no significant differences in dose calculation accuracy between DL models. Both models demonstrated DVH deviation below 0.5 % and a gamma-pass-rate (1 %/1mm) exceeding 97 %. ConclusionsResUNet enhanced CT number accuracy and image quality of sCT and outperformed UNet in sCT generation from CBCT. This method holds promise for generating precise sCT for HN ART.

Structure-preserving low-dose computed tomography image denoising using a deep residual adaptive global context attention network.

Low-dose computed tomography (LDCT) scans can effectively reduce the radiation damage to patients, but this is highly detrimental to CT image quality. Deep convolutional neural networks (CNNs) have shown their potential in improving LDCT image quality. However, the conventional CNN-based approaches rely fundamentally on the convolution operations, which are ineffective for modeling the correlations among nonlocal similar structures and the regionally distinct statistical properties in CT images. This modeling deficiency hampers the denoising performance for CT images derived in this manner. In this paper, we propose an adaptive global context (AGC) modeling scheme to describe the nonlocal correlations and the regionally distinct statistics in CT images with negligible computation load. We further propose an AGC-based long-short residual encoder-decoder (AGC-LSRED) network for efficient LDCT image noise artifact-suppression tasks. Specifically, stacks of residual AGC attention blocks (RAGCBs) with long and short skip connections are constructed in the AGC-LSRED network, which allows valuable structural and positional information to be bypassed through these identity-based skip connections and thus eases the training of the deep denoising network. For training the AGC-LSRED network, we propose a compound loss that combines the L1 loss, adversarial loss, and self-supervised multi-scale perceptual loss. Quantitative and qualitative experimental studies were performed to verify and validate the effectiveness of the proposed method. The simulation experiments demonstrated the proposed method exhibits the best result in terms of noise suppression [root-mean-square error (RMSE) =9.02; peak signal-to-noise ratio (PSNR) =33.17] and fine structure preservation [structural similarity index (SSIM) =0.925] compared with other competitive CNN-based methods. The experiments on real data illustrated that the proposed method has advantages over other methods in terms of radiologists' subjective assessment scores (averaged scores =4.34). With the use of the AGC modeling scheme to characterize the structural information in CT images and of residual AGC-attention blocks with long and short skip connections to ease the network training, the proposed AGC-LSRED method achieves satisfactory results in preserving fine anatomical structures and suppressing noise in LDCT images.

Influence of Body Mass Index and Abdominal Circumference on Radiation Dose During Abdominopelvic Computed Tomography

Background: Contrast-enhanced examinations of the abdomen region have a greater radiation exposure due to the multiphase abdominal computed tomography (CT) protocols. The use of automatic tube current modulation is known to reduce radiation dose and maintain or improve image quality for abdominal CT. However, using automatic tube current modulation can increase radiation dose for individuals with a larger body habitus. Objectives: The study aimed to assess the influence of body mass index and abdominal circumference on the effective dose for routinely performed contrast-enhanced abdomen and pelvis scans. Methods: A total of 160 subjects referred for routine CT abdomen and pelvis were included in the study and categorised into three groups according to their body mass index (BMI) [underweight: &lt;18.5 kg/m2, normal: 18.5-24.9 kg/m2, overweight: 25-29.9 kg/m2 and obese: (≥30 kg/m2]. All the scans were performed on a 128 MDCT scanner by Philips. The effective dose was calculated from the dose length product using region-specific conversion factors. Results: The effective dose was found to be 21.47 ± 2 mSv for the underweight group, 22.75± 2.3 mSv for the normal group, 25.02 ± 2.8 for the overweight group, and 29.7 ± 6.7 mSv for the obese group. Conclusion: The study reported a 32.39 % increase in effective dose for obese patients. The study also reported a significant increase in effective dose as BMI and abdominal circumference increased.

Image-Guided IORT after Surgical Resection of Brain Malignancies: A Feasibility Study

Intraoperative radiation therapy (IORT) can be used to locally apply radiation doses to surgical cavities of resected brain metastases and glioblastoma. The miniaturized linear accelerator INTRABEAM 600® offers a low-energy system of 50kV X-rays. To date, placing the spherical applicator in the surgical bed has been done manually and without final positioning verification. Ultimately, without positional control, it must be assumed that the applicator is optimally positioned against the tissue in the tumor cavity, without any air interference or fluid underlying. Misplacements could lead to incomplete dose delivery, potentially yielding an increased risk of recurrence. Therefore, we deemed relevant to assess intraoperative imaging as a novel option for easing the applicator positioning. A surgical environment was mimicked in two test runs with an interdisciplinary team, accounting for operating and anesthesia times. The IORT planning software Radiance (GMV Innovating Solutions, Madrid, Spain) was previously calibrated with a "cheese" phantom, using twelve different density levels from 0.3 to 1.842 g/cm3, yielding a value-to-density table with -600 to +600 HU range. Furthermore, two inserts were placed in the phantom to evaluate the quality and spatial resolution of the cone-beam CT (CBCT) imaging. The O-Arm System (Medtronic, Minneapolis, MN, USA) was employed for acquiring tomographic intraoperative imaging, once the applicator was placed in a skull-shaped phantom. Two expert radiation oncologists contoured the acquired dataset and performed the treatment calculations, based on a 30 Gy prescription dose. We achieved the best possible CBCT imaging quality with the 120kV and 20mA setting parameters. This corresponds to the "HD3D (large)" O-Arm setting mode. With this, the image quality with artifact reduction, minimization of image noise and consideration of a low radiation exposure of only 0.5mSv for the patients is feasible. Approximately 15 additional minutes were accounted for contouring and planning times. Good-quality intraoperative imaging can be obtained when performing IORT, easing the applicator positioning and resulting in a more precise target irradiation. Operating times would be minimally extended with this procedure. A clinical study will initiate based on these findings.

Deep learning imaging reconstruction of reduced-dose 40 keV virtual monoenergetic imaging for early detection of colorectal cancer liver metastases

ObjectiveTo explore whether reduced-dose (RD) gemstone spectral imaging (GSI) and deep learning image reconstruction (DLIR) of 40 keV virtual monoenergetic image (VMI) enhanced the early detection and diagnosis of colorectal cancer liver metastases (CRLM). MethodsThirty-five participants with pathologically confirmed colorectal cancer were prospectively enrolled from March to August 2022 after routine care abdominal computed tomography (CT). GSI mode was used for contrast-enhanced CT, and two portal venous phase CT images were obtained [standard-dose (SD) CT dose index (CTDIvol) = 15.51 mGy, RD CTDIvol = 7.95 mGy]. The 40 keV-VMI were reconstructed via filtered back projection (FBP) and iterative reconstruction (ASIR-V 60 %, AV60) of both SD and RD images. RD medium-strength deep learning image reconstruction (DLIR-M) and RD high-strength deep learning image reconstruction (DLIR-H) were used to reconstruct the 40 keV-VMI. The contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the liver and the lesions were objectively evaluated. The overall image quality, lesion conspicuity, and diagnostic confidence were subjectively evaluated, to compare the differences in evaluation results among the different images. ResultsAll 35 participants (mean age: 59.51 ± 11.01 years; 14 females) underwent SD and RD GSI portal venous-phase CT scans. The dose-length product of the RD GSI scan was reduced by 49–53 % lower than that of the SD GSI scan (420.22 ± 31.95) vs (817.58 ± 60.56). A total of 219 lesions were identified, including 55 benign lesions and 164 metastases, with an average size of 7.37 ± 4.14 mm. SD-FBP detected 207 lesions, SD-AV60 detected 201 lesions, and DLIR-M and DLIR-H detected 199 and 190 lesions, respectively. For lesions ≤ 5 mm, there was no statistical difference between SD-FBP vs DLIR-M (χ2McNemar = 1.00, P = 0.32) and SD-AV60 vs DLIR-M (χ2McNemar = 0.33, P = 0.56) in the detection rate. The CNR, SNR, and noise of DLIR-M and DLIR-H 40 keV-VMI images were better than those of SD-FBP images (P < 0.01) but did not differ significantly from those of SD-AV60 images (P > 0.05). When the lesions ≤ 5 mm, there were statistical differences in the overall diagnostic sensitivity of lesions compared with SD-FBP, SD-AV60, DLIR-M and DLIR-H (P＜0.01). There were no statistical differences in the sensitivity of lesions diagnosis between SD-FBP, SD-AV60 and DLIR-M (both P＞0.05). However, the DLIR-M subjective image quality and lesion diagnostic confidence were higher for SD-FBP (both P < 0.01). ConclusionReduced dose DLIR-M of 40 keV-VMI can be used for routine follow-up care of colorectal cancer patients, to optimize evaluations and ensure CT image quality. Meanwhile, the detection rate and diagnostic sensitivity and specificity of small lesions, early liver metastases is not obviously reduced.

Three-dimensional CT imaging in extensor tendons using deep learning reconstruction: optimal reconstruction parameters and the influence of dose.

The purpose of this study was to assess the optimal reconstruction parameters and the influence of tube current in extensor tendons three-dimensional computed tomography (3D CT) using deep learning reconstruction, using iterative reconstruction as a reference. In the phantom study, a cylindrical phantom with a 3mm rod simulated an extensor tendon was used. The phantom images were acquired at tube current of 50, 100, 150, 200, and 250 mA. In the clinical study, CT scans of hand tendons were performed on nine hands from eight patients. All images were reconstructed using advanced intelligent clear-IQ engine (AiCE) parameters (body, body sharp, brain CTA, and brain LCD) and adaptive iterative dose reduction three dimensional (AIDR 3D). The objective image quality for tendon detectability was evaluated by calculating the low-contrast object specific contrast-to-noise ratio (CNRLO) in the phantom study and CNR and coefficient of variation (CV) in the clinical study. In the phantom study, CNRLO (at 200 mA) of AiCE parameters (body, body sharp, brain CTA, and brain LCD) and AIDR 3D were 5.2, 5.3, 5.3, 5.8, and 5.0, respectively. In the clinical study, AiCE brain CTA was higher CNR and lower CV values compared to other reconstruction parameters. AiCE without dose reduction may be an effective strategy for further improving the image quality of extensor tendons 3D CT. Our study suggests that the AiCE brain CTA is more suitable for extensor tendons 3D CT compared to other AiCE parameters.

Ambulatory sedation for children under 6 years with CHD in MRI and CT.

In infants and young children, good image quality in MRI and CT requires sedation or general anesthesia to prevent motion artefacts. This study aims to determine the safety of ambulatory sedation for children with CHD in an outpatient setting as a feasible alternative to in-hospital management. We recorded 91 consecutive MRI and CT examinations of patients with CHD younger than 6 years with ambulatory sedation. CHD diagnoses, vital signs, applied sedatives, and adverse events during or after ambulatory sedation were investigated. We analysed 91 patients under 72 months (6 years) of age (median 26.0, range 1-70 months; 36% female). Sixty-eight per cent were classified as ASA IV, 25% as ASA III, and 7% as ASA II (American Society of Anesthesiologists Physical Status Classification). Ambulatory sedation was performed by using midazolam, propofol, and/or S-ketamine. The median sedation time for MRI was 90 minutes (range 35-235 minutes) and 65 minutes for CT (range 40-280 minutes). Two male patients (age 1.5 months, ASA II, and age 17 months, ASA IV) were admitted for in-hospital observation due to unexpected severe airway obstruction. The patients were discharged without sequelae after 1 and 3 days, respectively. All other patients were sent home on the day of examination. In infants and young children with CHD, MRI or CT imaging can be performed under sedation in an outpatient setting by a well-experienced team. In-hospital backup should be available for unexpected events.