Deep Learning Assessment of Small Renal Masses at Contrast-enhanced Multiphase CT.

Background Accurate characterization of suspicious small renal masses is crucial for optimized management. Deep learning (DL) algorithms may assist with this effort. Purpose To develop and validate a DL algorithm for identifying benign small renal masses at contrast-enhanced multiphase CT. Materials and Methods Surgically resected renal masses measuring 3 cm or less in diameter at contrast-enhanced CT were included. The DL algorithm was developed by using retrospective data from one hospital between 2009 and 2021, with patients randomly allocated in a training and internal test set ratio of 8:2. Between 2013 and 2021, external testing was performed on data from five independent hospitals. A prospective test set was obtained between 2021 and 2022 from one hospital. Algorithm performance was evaluated by using the area under the receiver operating characteristic curve (AUC) and compared with the results of seven clinicians using the DeLong test. Results A total of 1703 patients (mean age, 56 years ± 12 [SD]; 619 female) with a single renal mass per patient were evaluated. The retrospective data set included 1063 lesions (874 in training set, 189 internal test set); the multicenter external test set included 537 lesions (12.3%, 66 benign) with 89 subcentimeter (≤1 cm) lesions (16.6%); and the prospective test set included 103 lesions (13.6%, 14 benign) with 20 (19.4%) subcentimeter lesions. The DL algorithm performance was comparable with that of urological radiologists: for the external test set, AUC was 0.80 (95% CI: 0.75, 0.85) versus 0.84 (95% CI: 0.78, 0.88) (P = .61); for the prospective test set, AUC was 0.87 (95% CI: 0.79, 0.93) versus 0.92 (95% CI: 0.86, 0.96) (P = .70). For subcentimeter lesions in the external test set, the algorithm and urological radiologists had similar AUC of 0.74 (95% CI: 0.63, 0.83) and 0.81 (95% CI: 0.68, 0.92) (P = .78), respectively. Conclusion The multiphase CT-based DL algorithm showed comparable performance with that of radiologists for identifying benign small renal masses, including lesions of 1 cm or less. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Quantum-enhanced hybrid feature engineering in thoracic CT image analysis for state-of-the-art nodule classification: an advanced lung cancer assessment.

The intricate nature of lung cancer treatment poses considerable challenges upon diagnosis. Early detection plays a pivotal role in mitigating its escalating global mortality rates. Consequently, there are pressing demands for robust and dependable early detection and diagnostic systems. However, the technological limitations and complexity of the disease make it challenging to implement an efficient lung cancer screening system. AI-based CT image analysis techniques are showing significant contributions to the development of computer-assisted detection (CAD) systems for lung cancer screening. Various existing research groups are working on implementing CT image analysis systems for assessing and classifying lung cancer. However, the complexity of different structures inside the CT image is high and comprehension of significant information inherited by them is more complex even after applying advanced feature extraction and feature selection techniques. Traditional and classical feature selection techniques may struggle to capture complex interdependencies between features. They may get stuck in local optima and sometimes require additional exploration strategies. Traditional techniques may also struggle with combinatorial optimization problems when applied to a prominent feature space. This paper proposed a methodology to overcome the existing challenges by applying feature extraction using Vision Transformer (FexViT) and Feature selection using the Quantum Computing based Quadratic unconstrained binary optimization (QC-FSelQUBO) technique. This algorithm shows better performance when compared with other existing techniques. The proposed methodology showed better performance as compared to other existing techniques when evaluated by applying necessary output measures, such as accuracy, Area under roc (receiver operating characteristics) curve, precision, sensitivity, and specificity, obtained as 94.28%, 99.10%, 96.17%, 90.16% and 97.46%. The further advancement of CAD systems is essential to meet the demand for more reliable detection and diagnosis of cancer, which can be addressed by leading the proposed quantum computation and growing AI-based technology ahead.

Multi-instance learning based lung nodule system for assessment of CT quality after small-field-of-view reconstruction.

Small-field-of-view reconstruction CT images (sFOV-CT) increase the pixel density across airway structures and reduce partial volume effects. Multi-instance learning (MIL) is proposed as a weakly supervised machine learning method, which can automatically assess the image quality. The aim of this study was to evaluate the disparities between conventional CT (c-CT) and sFOV-CT images using a lung nodule system based on MIL and assessments from radiologists. 112 patients who underwent chest CT were retrospectively enrolled in this study between July 2021 to March 2022. After undergoing c-CT examinations, sFOV-CT images with small-field-of-view were reconstructed. Two radiologists analyzed all c-CT and sFOV-CT images, including features such as location, nodule type, size, CT values, and shape signs. Then, an MIL-based lung nodule system objectively analyzed the c-CT (c-MIL) and sFOV-CT (sFOV-MIL) to explore their differences. The signal-to-noise ratio of lungs (SNR-lung) and contrast-to-noise ratio of nodules (CNR-nodule) were calculated to evaluate the quality of CT images from another perspective. The subjective evaluation by radiologists showed that feature of minimal CT value (p = 0.019) had statistical significance between c-CT and sFOV-CT. However, most features (all with p < 0.05), except for nodule type, location, volume, mean CT value, and vacuole sign (p = 0.056-1.000), had statistical differences between c-MIL and sFOV-MIL by MIL system. The SNR-lung between c-CT and sFOV-CT had no statistical significance, while the CNR-nodule showed statistical difference (p = 0.007), and the CNR of sFOV-CT was higher than that of c-CT. In detecting the difference between c-CT and sFOV-CT, features extracted by the MIL system had more statistical differences than those evaluated by radiologists. The image quality of those two CT images was different, and the CNR-nodule of sFOV-CT was higher than that of c-CT.

Deep Learning Image Reconstruction Algorithm for CCTA: Image Quality Assessment and Clinical Application.

OBJECTIVE: The increasing number of coronary computed tomography angiography (CCTA) requests raised concerns about dose exposure. New dose reduction strategies based on artificial intelligence have been proposed to overcome limitations of iterative reconstruction (IR) algorithms. Our prospective study sought to explore the added value of deep-learning image reconstruction (DLIR) in comparison with a hybrid IR algorithm (adaptive statistical iterative reconstruction-veo [ASiR-V]) in CCTA, even in clinical challenging scenarios, as obesity, heavily calcified vessels and coronary stents. METHODS: We prospectively included 103 consecutive patients who underwent CCTA. Data sets were reconstructed with ASiR-V and DLIR. For each reconstruction signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) was calculated, and qualitative assessment was made with a four-point Likert scale by two independent and blinded radiologists with different expertise. RESULTS: Both SNR and CNR were significantly higher in DLIR (SNR-DLIR median value [interquartile range] of 13.89 [11.06-16.35] and SNR-ASiR-V 25.42 [22.46-32.22], P < 0.001; CNR-DLIR 16.84 [9.83-27.08] vs CNR-ASiR-V 10.09 [5.69-13.5], P < 0.001).Median qualitative score was 4 for DLIR images versus 3 for ASiR-V ( P < 0.001), with a good interreader reliability [intraclass correlation coefficient(2,1)e intraclass correlation coefficient(3,1) 0.60 for DLIR and 0.62 and 0.73 for ASiR-V].In the obese and in the "calcifications and stents" groups, DLIR showed significantly higher values of SNR (24.23 vs 11.11, P < 0.001 and 24.55 vs 14.09, P < 0.001, respectively) and CNR (16.08 vs 8.04, P = 0.008 and 17.31 vs 10.14, P = 0.003) and image quality. CONCLUSIONS: Deep-learning image reconstruction in CCTA allows better SNR, CNR, and qualitative assessment than ASiR-V, with an added value in the most challenging clinical scenarios.

Value of deep learning reconstruction of chest low-dose CT for image quality improvement and lung parenchyma assessment on lung window.

OBJECTIVES: To explore the performance of low-dose computed tomography (LDCT) with deep learning reconstruction (DLR) for the improvement of image quality and assessment of lung parenchyma. METHODS: Sixty patients underwent chest regular-dose CT (RDCT) followed by LDCT during the same examination. RDCT images were reconstructed with hybrid iterative reconstruction (HIR) and LDCT images were reconstructed with HIR and DLR, both using lung algorithm. Radiation exposure was recorded. Image noise, signal-to-noise ratio, and subjective image quality of normal and abnormal CT features were evaluated and compared using the Kruskal-Wallis test with Bonferroni correction. RESULTS: The effective radiation dose of LDCT was significantly lower than that of RDCT (0.29 ± 0.03 vs 2.05 ± 0.65 mSv, p < 0.001). The mean image noise ± standard deviation was 33.9 ± 4.7, 39.6 ± 4.3, and 31.1 ± 3.2 HU in RDCT, LDCT HIR-Strong, and LDCT DLR-Strong, respectively (p < 0.001). The overall image quality of LDCT DLR-Strong was significantly better than that of LDCT HIR-Strong (p < 0.001) and comparable to that of RDCT (p > 0.05). LDCT DLR-Strong was comparable to RDCT in evaluating solid nodules, increased attenuation, linear opacity, and airway lesions (all p > 0.05). The visualization of subsolid nodules and decreased attenuation was better with DLR than with HIR in LDCT but inferior to RDCT (all p < 0.05). CONCLUSION: LDCT DLR can effectively reduce image noise and improve image quality. LDCT DLR provides good performance for evaluating pulmonary lesions, except for subsolid nodules and decreased lung attenuation, compared to RDCT-HIR. CLINICAL RELEVANCE STATEMENT: The study prospectively evaluated the contribution of DLR applied to chest low-dose CT for image quality improvement and lung parenchyma assessment. DLR can be used to reduce radiation dose and keep image quality for several indications. KEY POINTS: • DLR enables LDCT maintaining image quality even with very low radiation doses. • Chest LDCT with DLR can be used to evaluate lung parenchymal lesions except for subsolid nodules and decreased lung attenuation. • Diagnosis of pulmonary emphysema or subsolid nodules may require higher radiation doses.

Value of the virtual monoenergetic image from dual-layer dual-energy computed tomography enterography in the preoperative assessment of the internal penetrating complication of Crohn's disease.

BACKGROUND: To determine the utility of virtual-monoenergetic imaging (VMI) at low energy levels from contrast-enhanced dual-layer dual-energy (DLDE) computed tomography enterography (CTE) in the preoperative assessment of internal penetrating lesions of Crohn's disease (CD). MATERIALS AND METHODS: Thirty-eight patients with penetrating lesions of CD by surgery undergoing contrast-enhanced DLDE CTE were retrospectively included. Polyenergetic imaging (PEI) and VMIs at low energy levels [40-70 kiloelectron volts (keV)] with 10 keV intervals were reconstructed. The objective parameters of image quality [noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR)] and the subjective parameter of image quality [diagnostic performance of lesions (DPL), overall image quality(OIQ)] of PEI and all VMIs at the low energy level were compared to determine the VMI on the optimal energy level. The lesion detection capability between PEI and the optimal VMI was compared. RESULTS: VMI40 was determined to be the optimal VMI among all VMIs at the low energy level for owning the best image quality. No significant difference was found in the detecting capability in penetrating lesions between VMI40 and PEI (p = 1.0), whereas a significant difference was found in the detecting capability in the bowel origin of the penetrating lesions (p = 0.004), the involved organ or structure by the fistula (p = 0.016) and the orifice of the fistula connected to the involved organ or structure ( p = 0.031) between them. CONCLUSIONS: Compared to conventional PEI, VMI40 improves the detection capability in anatomical details of penetrating lesions of CD, helping colorectal surgeons rationalizing preoperative plans of internal penetrating lesions of CD.

A semantic fidelity interpretable-assisted decision model for lung nodule classification.

PURPOSE: Early diagnosis of lung nodules is important for the treatment of lung cancer patients, existing capsule network-based assisted diagnostic models for lung nodule classification have shown promising prospects in terms of interpretability. However, these models lack the ability to draw features robustly at shallow networks, which in turn limits the performance of the models. Therefore, we propose a semantic fidelity capsule encoding and interpretable (SFCEI)-assisted decision model for lung nodule multi-class classification. METHODS: First, we propose multilevel receptive field feature encoding block to capture multi-scale features of lung nodules of different sizes. Second, we embed multilevel receptive field feature encoding blocks in the residual code-and-decode attention layer to extract fine-grained context features. Integrating multi-scale features and contextual features to form semantic fidelity lung nodule attribute capsule representations, which consequently enhances the performance of the model. RESULTS: We implemented comprehensive experiments on the dataset (LIDC-IDRI) to validate the superiority of the model. The stratified fivefold cross-validation results show that the accuracy (94.17%) of our method exceeds existing advanced approaches in the multi-class classification of malignancy scores for lung nodules. CONCLUSION: The experiments confirm that the methodology proposed can effectively capture the multi-scale features and contextual features of lung nodules. It enhances the capability of shallow structure drawing features in capsule networks, which in turn improves the classification performance of malignancy scores. The interpretable model can support the physicians' confidence in clinical decision-making.

Pancreas CT assessment for pancreatic ductal adenocarcinoma resectability: effect of tube voltage and slice thickness on image quality and diagnostic performance.

OBJECTIVES: To assess the resectability of pancreatic ductal adenocarcinoma (PDAC), the evaluation of tumor vascular contact holds paramount significance. This study aimed to compare the image quality and diagnostic performance of high-resolution (HR) pancreas computed tomography (CT) using an 80 kVp tube voltage and a thin slice (1 mm) for assessing PDAC resectability, in comparison with the standard protocol CT using 120 kVp. METHODS: This research constitutes a secondary analysis originating from a multicenter prospective study. All participants underwent both the standard protocol pancreas CT using 120 kVp with 3 mm slice thickness (ST) and HR-CT utilizing an 80 kVp tube voltage and 1 mm ST. The contrast-to-noise ratio (CNR) between parenchyma and tumor, along with the degree of enhancement of the abdominal aorta and main portal vein (MPV), were measured and subsequently compared. Additionally, the likelihood of margin-negative resection (R0) was evaluated using a five-point scale. The diagnostic performance of both CT protocols in predicting R0 resection was assessed through the area under the receiver operating characteristic curve (AUC). RESULTS: A total of 69 patients (37 males and 32 females; median age, 66.5 years) were included in the study. The median CNR of PDAC was 10.4 in HR-CT, which was significantly higher than the 7.1 in the standard CT (P=0.006). Furthermore, HR-CT demonstrated notably higher median attenuation values for both the abdominal aorta (579.5 HU vs. 327.2 HU; P=0.002) and the MPV (263.0 HU vs. 175.6 HU; P=0.004) in comparison with standard CT. Following surgery, R0 resection was achieved in 51 patients. The pooled AUC for HR-CT in predicting R0 resection was 0.727, slightly exceeding the 0.699 of standard CT, albeit lacking a significant statistical distinction (P=0.128). CONCLUSION: While HR pancreas CT using 80 kVp offered a notably greater degree of contrast enhancement in vessels and a higher CNR for PDAC compared to standard CT, its diagnostic performance in predicting R0 resection remained statistically comparable.

Hyperparameter optimization and development of an advanced CNN-based technique for lung nodule assessment.

Objective. This paper aims to propose an advanced methodology for assessing lung nodules using automated techniques with computed tomography (CT) images to detect lung cancer at an early stage.Approach. The proposed methodology utilizes a fixed-size 3 × 3 kernel in a convolution neural network (CNN) for relevant feature extraction. The network architecture comprises 13 layers, including six convolution layers for deep local and global feature extraction. The nodule detection architecture is enhanced by incorporating a transfer learning-based EfficientNetV_2 network (TLEV2N) to improve training performance. The classification of nodules is achieved by integrating the EfficientNet_V2 architecture of CNN for more accurate benign and malignant classification. The network architecture is fine-tuned to extract relevant features using a deep network while maintaining performance through suitable hyperparameters.Main results. The proposed method significantly reduces the false-negative rate, with the network achieving an accuracy of 97.56% and a specificity of 98.4%. Using the 3 × 3 kernel provides valuable insights into minute pixel variation and enables the extraction of information at a broader morphological level. The continuous responsiveness of the network to fine-tune initial values allows for further optimization possibilities, leading to the design of a standardized system capable of assessing diversified thoracic CT datasets.Significance. This paper highlights the potential of non-invasive techniques for the early detection of lung cancer through the analysis of low-dose CT images. The proposed methodology offers improved accuracy in detecting lung nodules and has the potential to enhance the overall performance of early lung cancer detection. By reconfiguring the proposed method, further advancements can be made to optimize outcomes and contribute to developing a standardized system for assessing diverse thoracic CT datasets.

Assessment of dual-energy computed tomography derived virtual monoenergetic imaging for target volume delineation of brain metastases.

BACKGROUND: Objective and subjective assessment of image quality of brain metastases on dual-energy computed tomography (DECT) virtual monoenergetic imaging (VMI) and its impact on target volume delineation. MATERIALS AND METHODS: 26 patients with 37 brain metastases receiving Magnetic Resonance Imaging (MRI) and DECT for stereotactic radiotherapy planning were included in this retrospective analysis. Lesion contrast (LC), contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were assessed for reconstructed VMI at 63 keV and artificial 120 kV Computed Tomography (CT). Image contrast and demarcation of metastases between 120 kV CT, VMI and MRI were subjectively assessed. Brain metastases were delineated by four radiation oncologists on VMI with a fixed or free brain window and contours were compared to solely MRI-based delineation using the Dice similarity coefficient. RESULTS: LC, CNR and SNR were significantly higher in VMI than in 120 kV CT (p < 0.0001). Image contrast and lesion demarcation were significantly better on VMI compared to 120 kV CT (p < 0.0001). Mean gross tumor volume (GTV)/planning target volume (PTV) Dice similarity coefficients were 0.87/0.9 for metastases without imaging uncertainties (no artifacts, calcification or impaired visibility with MRI) but worse for metastases with imaging uncertainties (0.71/0.74). Target volumes delineated on VMI were around 5-10% smaller compared to MRI. CONCLUSION: Image quality of VMI is objectively and subjectively superior to conventional CT. VMI provides significant advantages in stereotactic radiotherapy planning with improved visibility of brain metastases and geometrically distortion-free representation of brain metastases. Beside a plausibility check of MRI-based target volume delineation, VMI might improve reliability and accuracy in target volume definition particularly in cases with imaging uncertainties with MRI.

Visual impression and texture analysis of advanced modeled iterative reconstruction (ADMIRE): improved assessment of image quality in CT for better estimation of dose reduction potential.

To evaluate the image quality (IQ) of advanced modeled iterative reconstruction (ADMIRE; Siemens Healthcare, Forchheim, Germany) applying image texture and image visual impression as a supplement to physical parameters such as noise level and spatial resolution. An ACR-phantom with four modules was examined at different radiation dose levels. To characterise the image texture, two Haralick texture parameters, contrast and entropy, were assessed at different dose levels and reconstruction algorithms. The visual impression of images and the low-contrast detectability were evaluated by the structural similarity index (SSIM). The spatial resolution was determined by the modulation transfer functions and the line spread function. The Haralick texture parameters, contrast and entropy, decreased with increasing ADMIRE levels. ADMIRE III, IV and V offered a comparable contrast and entropy to those calculated by filtered back projection (FBP) with a radiation dose reduction up to 50%. SSIM (low-contrast detectability) improved with increasing ADMIRE levels. SSIM calculated by ADMIRE IV and V revealed comparable IQ to FBP with a decreased CTDIvolup to 50%. Spatial resolution was retained up to 90% dose reduction. Compared to FBP at the same dose level, the image noise decreased up to 61% with higher ADMIRE levels (σFBP= 17.3 HU andσADMIREV= 10.6 HU at 6.65 mGy). Taking texture analysis and visual perception into account, a more realistic assessment of the dose reduction potential of ADMIRE can be achieved than quality metrics based alone on physical measurements.

Deep learning image reconstruction for quality assessment of iodine concentration in computed tomography: A phantom study.

BACKGROUND: Recently, deep learning reconstruction (DLR) technology aiming to improve image quality with minimal radiation dose has been applied not only to pediatric scans, but also to computed tomography angiography (CTA). OBJECTIVE: To evaluate image quality characteristics of filtered back projection (FBP), hybrid iterative reconstruction [Adaptive Iterative Dose Reduction 3D (AIDR 3D)], and DLR (AiCE) using different iodine concentrations and scan parameters. METHODS: Phantoms with eight iodine concentrations (ranging from 1.2 to 25.9 mg/mL) located at the edge of a cylindrical water phantom with a diameter of 19 cm were scanned. Data were reconstructed with FBP, AIDR 3D, and AiCE using various scan parameters of tube current and voltage using a 320 row-detector CT scanner. Data obtained using different reconstruction techniques were quantitatively compared by analyzing Hounsfield units (HU), noise, and contrast-to-noise ratios (CNRs). RESULTS: HU values of FBP and AIDR 3D were constant even when the iodine concentration was changed, whereas AiCE showed the highest HU value when the iodine concentration was low, but the HU value reversed when the iodine concentration exceeded a certain value. In the AIDR 3D and AiCE, the noise decreased as the tube current increased, and the change in noise when the iodine concentration was inconsistent. AIDR 3D and AiCE yielded better noise reduction rates than with FBP at a low tube current. The noise reduction rate of AIDR 3D and AiCE compared to that of FBP showed characteristics ranging from 7% to 35%, and the noise reduction rate of AiCE compared to that of AIDR 3D ranged from 2.0% to 13.3%. CONCLUSIONS: The evaluated reconstruction techniques showed different image quality characteristics (HU value, noise, and CNR) according to dose and scan parameters, and users must consider these results and characteristics before performing patient scans.

Systematic assessment of coronary calcium detectability and quantification on four generations of CT reconstruction techniques: a patient and phantom study.

In computed tomography, coronary artery calcium (CAC) scores are influenced by image reconstruction. The effect of a newly introduced deep learning-based reconstruction (DLR) on CAC scoring in relation to other algorithms is unknown. The aim of this study was to evaluate the effect of four generations of image reconstruction techniques (filtered back projection (FBP), hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), and DLR) on CAC detectability, quantification, and risk classification. First, CAC detectability was assessed with a dedicated static phantom containing 100 small calcifications varying in size and density. Second, CAC quantification was assessed with a dynamic coronary phantom with velocities equivalent to heart rates of 60-75 bpm. Both phantoms were scanned and reconstructed with four techniques. Last, scans of fifty patients were included and the Agatston calcium score was calculated for all four reconstruction techniques. FBP was used as a reference. In the phantom studies, all reconstruction techniques resulted in less detected small calcifications, up to 22%. No clinically relevant quantification changes occurred with different reconstruction techniques (less than 10%). In the patient study, the cardiovascular risk classification resulted, for all reconstruction techniques, in excellent agreement with the reference (κ = 0.96-0.97). However, MBIR resulted in significantly higher Agatston scores (61 (5.5-435.0) vs. 81.5 (9.25-435.0); p < 0.001) and 6% reclassification rate. In conclusion, HIR and DLR reconstructed scans resulted in similar Agatston scores with excellent agreement and low-risk reclassification rate compared with routine reconstructed scans (FBP). However, caution should be taken with low Agatston scores, as based on phantom study, detectability of small calcifications varies with the used reconstruction algorithm, especially with MBIR and DLR.

Assessment of a single-pass venous phase CT chest, abdomen and pelvis and dual-energy CT in general oncology outpatients.

INTRODUCTION: This study assessed replacing traditional protocol CT-arterial chest and venous abdomen and pelvis, with a single-pass, single-bolus, venous phase CT chest, abdomen and pelvis (CAP) protocol in general oncology outpatients at a single centre. METHODS: A traditional protocol is an arterial phase chest followed by venous phase abdomen and pelvis. A venous CAP (vCAP) protocol is a single acquisition 60 s after contrast injection, with optional arterial phase upper abdomen based on the primary tumour. Consecutive eligible patients were assessed, using each patient's prior study as a comparator. Attenuation for various structures, lesion conspicuity and dose were compared. Subset analysis of dual-energy (DE) CT scans in the vCAP protocol performed for lesion conspicuity on 50 keV virtual monoenergetic (VME) images. RESULTS: One hundred and eleven patients were assessed with both protocols. Forty-six patients had their vCAP scans using DECT. The vCAP protocol had no significant difference in the attenuation of abdominal structures, with reduced attenuation of mediastinal structures. There was a significant improvement in the visibility of pleural lesions (p < 0.001), a trend for improved mediastinal nodes assessment, and no significant difference for abdominal lesions. A significant increase in liver lesion conspicuity on 50 keV VME reconstructions was noted for both readers (p < 0.001). There were significant dose reductions with the vCAP protocol. CONCLUSION: A single-pass vCAP protocol offered an improved thoracic assessment with no loss of abdominal diagnostic confidence and significant dose reductions compared to traditional protocol. Improved liver lesion conspicuity on 50 keV VME images across a range of cancers is promising.

Dual-layer spectral detector CT arthrography of the shoulder: assessment of image quality and value in differentiating calcium from iodine.

BACKGROUND: Dual-layer spectral detector computed tomography (DLCT) may potentially improve CT arthrography through enhanced image quality and analysis of the chemical composition of tissue. PURPOSE: To evaluate the image quality of monoenergetic reconstructions from DLCT arthrography of the shoulder and assess the additional diagnostic value in differentiating calcium from iodine. MATERIAL AND METHODS: Images from consecutive shoulder DLCT arthrography examinations performed between December 2016 and February 2018 were retrospectively reviewed for hyperattenuating lesions within the labrum and tendons. The mean attenuation of the target lesion, noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of the virtual monoenergetic images obtained at 40-200 keV were compared with conventional 140-kVp images. Two evaluators independently classified each target lesion as contrast media or calcification, without and with DLCT spectral data. Receiver operating curve (ROC) analysis was performed to assess the diagnostic performance of shoulder DLCT arthrography, without and with the aid of spectral data. RESULTS: The study included 20 target lesions (18 DLCT arthrography examinations of 17 patients). The SNRs of the monoenergetic images at 40-60 keV were significantly higher than those of conventional images (P < 0.05). The CNRs of the monoenergetic images at 40-70 keV were significantly higher than those of conventional images (P < 0.001). The ability to differentiate calcium from iodine, without and with DLCT spectral data, did not significantly differ (P = 0.441 and P = 0.257 for reviewers 1 and 2, respectively). CONCLUSION: DLCT had no additive value in differentiating calcium from iodine in small, hyperattenuating lesions in the labrum and tendons.

Comparative assessment of noise properties for two deep learning CT image reconstruction techniques and filtered back projection.

BACKGROUND: Two deep learning image reconstruction (DLIR) techniques from two different computed tomography (CT) vendors have recently been introduced into clinical practice. PURPOSE: To characterize the noise properties of two DLIR techniques with different training methods, using a phantom containing a simple uniform and a complex non-uniform region. METHODS: A water-bath phantom with a diameter of 300 mm was used as a base phantom. A textured phantom with a diameter of 128 mm, which was made of two materials, one equivalent to water and the other being 12 mg/ml diluted iodine, irregularly mixed to create a complex texture (non-uniform region), was placed in the base phantom. Thirty repeated phantom scans were performed using two CT scanners (Revolution CT with Apex Edition, GE Healthcare; Aquilion One PRISM Edition, Canon Medical Systems) at two dose levels (CT dose index: 5 and 15 mGy). Images were reconstructed with each CT system's filtered back projection (FBP) and DLIR [TrueFidelity (TF), GE Healthcare; Advanced intelligent Clear-IQ Engine Body Sharp (AC), Canon Medical Systems] for three process strengths. For basic characteristics of noise, the standard deviation (SD) and noise power spectrum (NPS) were measured for the uniform (water) region. A noise magnitude map was generated by calculating the inter-image SD at each pixel position across the 30 images. Then, a noise reduction map (NRM), which visualizes the relative differences in noise magnitude between FBP and DLIR, was calculated. The NRM values ranged from 0.0 to 1.0. A low NRM value represents a less aggressive noise reduction. The histograms of the NRM value were analyzed for the uniform and non-uniform regions. RESULTS: The reduction in noise magnitude compared with FBP tended to be greater with AC (45%-85%) than with TF (32%-65%). The average NPS frequencies of TF and AC were almost comparable to those of FBP, except for the low-dose condition and the high noise reduction strength for AC. The NRM values of TF and AC were higher in the uniform region than in the non-uniform region. In the non-uniform region, TF's average NRM values (0.21-0.48) tended to be lower than AC's (0.39-0.78). The histograms for TF showed a small overlap between the uniform and the non-uniform regions; in contrast, those for AC showed a greater overlap. This difference seems to indicate that TF processes the uniform and non-uniform regions more differently than AC does. CONCLUSION: This study has revealed a distinct difference in characteristics between the two DLIR techniques: TF tends to offer less aggressive noise reduction in non-uniform regions and preserve the original signals, whereas AC tends to prioritize noise filtering over edge-preservation, especially at the low-dose condition and with the high noise reduction strength.

iQMetrix-CT: New software for task-based image quality assessment of phantom CT images.

The purpose of this article was to explain how the new iQMetrix-CT software works, as well as to describe its current potential and discuss its future applications. iQMetrix-CT was developed by a working group from the French Society of Medical Physicists (SFPM). This software calculates three advanced metrics, which have been adapted to take into account the non-linear and non-stationary properties of iterative reconstruction algorithms. Noise power spectrum is calculated to assess noise texture and noise magnitude in the frequency domain. The task-based transfer function is computed to assess the spatial resolution under conditions of contrast and noise similar to the lesions encountered in clinical practice. Finally, the detectability index is used to estimate the radiologist's ability to perform a given task such as detecting a simulated lesion. These metrics are very useful to evaluate the performance of reconstruction algorithms in term of image quality and optimize the doses of a given protocol or to evaluate and compare a new technology.

Ultralow-dose CT of the petrous bone using iterative reconstruction technique, tin filter and high resolution detectors allows an adequate assessment of the petrous bone structures.

OBJECTIVES: To assess image quality and radiation dose in computed tomography (CT) studies of the petrous bone done with a scanner using a tin filter, high-resolution detectors, and iterative reconstruction, and to compare versus in studies done with another scanner without a tin filter using filtered back projection reconstruction. MATERIAL AND METHODS: Thirty two patients (group 1) were acquired with an ultra-low dose CT (32-MDCT, 130kV, tin filter and iterative reconstruction). Images and radiation doses were compared to 36 patients (group 2) acquired in a 16-MDCT (120kV and filtered back-projection). Muscle density, bone density, and background noise were measured. Signal-to-noise ratio (SNR) was calculated. To assess image quality, two independent radiologists subjectively evaluated the visualization of the different structures of the middle and inner ear (0=not visualized, 3=perfectly identified and delimited). Interobserver agreement was calculated. Effective dose at different anatomical levels with the dose-length product was recorded. RESULTS: In the quantitative analysis, there were no significant differences in image noise between the two groups. In the qualitative analysis, a similar or slightly lower subjective score was obtained in the delimitation of different structures of the ossicular chain and cochlea in the 32-MDCT, compared to 16-MDCT, with statistically significant differences. Mean effective dose (±standard deviation) was 0.16±0.04mSv for the 32-MDCT and 1.25±0.30mSv for the 16-MDCT. CONCLUSIONS: The use of scanners with tin filters, high-resolution detectors, and iterative reconstruction allows to obtain images with adequate quality for the evaluation of the petrous bone structures with ultralow doses of radiation (0.16±0.04mSv).

Assessment of low-dose paranasal sinus CT imaging using a new deep learning image reconstruction technique in children compared to adaptive statistical iterative reconstruction V (ASiR-V).

PURPOSE: To compare the effects of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction V (ASiR-V) on image quality in low-dose computed tomography (CT) of paranasal sinuses in children. METHODS: Low-dose CT scans of the paranasal sinuses in 25 pediatric patients were retrospectively evaluated. The raw data were reconstructed with three levels of DLIR (high, H; medium, M; and low, L), filtered back projection (FBP), and ASiR-V (30% and 50%). Image noise was measured in both soft tissue and bone windows, and the signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) of the images were calculated. Subjective image quality at the ethmoid sinus and nasal cavity levels of the six groups of reconstructed images was assessed by two doctors using a five-point Likert scale in a double-blind manner. RESULTS: The patients' mean dose-length product and effective dose were 36.65 ± 2.44 mGy·cm and 0.17 ± 0.03 mSv, respectively. (1) Objective evaluation: 1. Soft tissue window: The difference among groups in each parameter was significant (P < 0.05). Pairwise comparisons showed that the H group' s parameters were significantly better (P < 0.05) than those of the 50% post-ASiR-V group. 2. Bone window: No significant between-group differences were found in the noise of the petrous portion of the temporal bone or its SNR or in the noise of the pterygoid processes of the sphenoids or their SNRs (P > 0.05). Significant differences were observed in the background noise and CNR (P < 0.05). As the DLIR intensity increased, image noise decreased and the CNR improved. The H group exhibited the best image quality. (2) Subjective evaluation: Scores for images of the ethmoid sinuses were not significantly different among groups (P > 0.05). Scores for images of the nasal cavity were significantly different among groups (P < 0.05) and were ranked in descending order as follows: H, M, L, 50% post-ASiR-V, 30% post-ASiR-V, and FBP. CONCLUSION: DLIR was superior to FBP and post-ASiR-V in low-dose CT scans of pediatric paranasal sinuses. At high intensity (H), DLIR provided the best reconstruction effects.

Automated quantitative assessment of amorphous calcifications: Towards improved malignancy risk stratification.

BACKGROUND: Amorphous calcifications noted on mammograms (i.e., small and indistinct calcifications that are difficult to characterize) are associated with high diagnostic uncertainty, often leading to biopsies. Yet, only 20% of biopsied amorphous calcifications are cancer. We present a quantitative approach for distinguishing between benign and actionable (high-risk and malignant) amorphous calcifications using a combination of local textures, global spatial relationships, and interpretable handcrafted expert features. METHOD: Our approach was trained and validated on a set of 168 2D full-field digital mammography exams (248 images) from 168 patients. Within these 248 images, we identified 276 image regions with segmented amorphous calcifications and a biopsy-confirmed diagnosis. A set of local (radiomic and region measurements) and global features (distribution and expert-defined) were extracted from each image. Local features were grouped using an unsupervised k-means clustering algorithm. All global features were concatenated with clustered local features and used to train a LightGBM classifier to distinguish benign from actionable cases. RESULTS: On the held-out test set of 60 images, our approach achieved a sensitivity of 100%, specificity of 35%, and a positive predictive value of 38% when the decision threshold was set to 0.4. Given that all of the images in our test set resulted in a recommendation of a biopsy, the use of our algorithm would have identified 15 images (25%) that were benign, potentially reducing the number of breast biopsies. CONCLUSIONS: Quantitative analysis of full-field digital mammograms can extract subtle shape, texture, and distribution features that may help to distinguish between benign and actionable amorphous calcifications.