Diagnostic Acceptability Research Articles

Deep learning image reconstruction for low-kiloelectron volt virtual monoenergetic images in abdominal dual-energy CT: medium strength provides higher lesion conspicuity.

The best settings of deep learning image reconstruction (DLIR) algorithm for abdominal low-kiloelectron volt (keV) virtual monoenergetic imaging (VMI) have not been determined. To determine the optimal settings of the DLIR algorithm for abdominal low-keV VMI. The portal-venous phase computed tomography (CT) scans of 109 participants with 152 lesions were reconstructed into four image series: VMI at 50 keV using adaptive statistical iterative reconstruction (Asir-V) at 50% blending (AV-50); and VMI at 40 keV using AV-50 and DLIR at medium (DLIR-M) and high strength (DLIR-H). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of nine anatomical sites were calculated. Noise power spectrum (NPS) using homogenous region of liver, and edge rise slope (ERS) at five edges were measured. Five radiologists rated image quality and diagnostic acceptability, and evaluated the lesion conspicuity. The SNR and CNR values, and noise and noise peak in NPS measurements, were significantly lower in DLIR images than AV-50 images in all anatomical sites (all P < 0.001). The ERS values were significantly higher in 40-keV images than 50-keV images at all edges (all P < 0.001). The differences of the peak and average spatial frequency among the four reconstruction algorithms were significant but relatively small. The 40-keV images were rated higher with DLIR-M than DLIR-H for diagnostic acceptance (P < 0.001) and lesion conspicuity (P = 0.010). DLIR provides lower noise, higher sharpness, and more natural texture to allow 40 keV to be a new standard for routine VMI reconstruction for the abdomen and DLIR-M gains higher diagnostic acceptance and lesion conspicuity rating than DLIR-H.

Deep learning image reconstruction generates thinner slice iodine maps with improved image quality to increase diagnostic acceptance and lesion conspicuity: a prospective study on abdominal dual-energy CT

BackgroundTo assess the improvement of image quality and diagnostic acceptance of thinner slice iodine maps enabled by deep learning image reconstruction (DLIR) in abdominal dual-energy CT (DECT).MethodsThis study prospectively included 104 participants with 136 lesions. Four series of iodine maps were generated based on portal-venous scans of contrast-enhanced abdominal DECT: 5-mm and 1.25-mm using adaptive statistical iterative reconstruction-V (Asir-V) with 50% blending (AV-50), and 1.25-mm using DLIR with medium (DLIR-M), and high strength (DLIR-H). The iodine concentrations (IC) and their standard deviations of nine anatomical sites were measured, and the corresponding coefficient of variations (CV) were calculated. Noise-power-spectrum (NPS) and edge-rise-slope (ERS) were measured. Five radiologists rated image quality in terms of image noise, contrast, sharpness, texture, and small structure visibility, and evaluated overall diagnostic acceptability of images and lesion conspicuity.ResultsThe four reconstructions maintained the IC values unchanged in nine anatomical sites (all p > 0.999). Compared to 1.25-mm AV-50, 1.25-mm DLIR-M and DLIR-H significantly reduced CV values (all p < 0.001) and presented lower noise and noise peak (both p < 0.001). Compared to 5-mm AV-50, 1.25-mm images had higher ERS (all p < 0.001). The difference of the peak and average spatial frequency among the four reconstructions was relatively small but statistically significant (both p < 0.001). The 1.25-mm DLIR-M images were rated higher than the 5-mm and 1.25-mm AV-50 images for diagnostic acceptability and lesion conspicuity (all P < 0.001).ConclusionsDLIR may facilitate the thinner slice thickness iodine maps in abdominal DECT for improvement of image quality, diagnostic acceptability, and lesion conspicuity.

Improved vascular depiction and image quality through deep learning reconstruction of CT hepatic arteriography during transcatheter arterial chemoembolization

PurposeTo evaluate the effect of deep learning reconstruction (DLR) on vascular depiction, tumor enhancement, and image quality of computed tomography hepatic arteriography (CTHA) images acquired during transcatheter arterial chemoembolization (TACE).MethodsInstitutional review board approval was obtained. Twenty-seven patients (18 men and 9 women, mean age, 75.7 years) who underwent CTHA immediately before TACE were enrolled. All images were reconstructed using three reconstruction algorithms: hybrid-iterative reconstruction (hybrid-IR), DLR with mild strength (DLR-M), and DLR with strong strength (DLR-S). Vascular depiction, tumor enhancement, feeder visualization, and image quality of CTHA were quantitatively and qualitatively assessed by two radiologists and compared between the three reconstruction algorithms.ResultsThe mean signal-to-noise ratios (SNR) of sub-segmental arteries and sub-sub-segmental arteries, and the contrast-to-noise ratio (CNR) of tumors, were significantly higher on DLR-S than on DLR-M and hybrid-IR (P < 0.001). The mean qualitative score for sharpness of sub-segmental and sub-sub-segmental arteries was significantly better on DLR-S than on DLR-M and hybrid-IR (P < 0.001). There was no significant difference in the feeder artery detection rate of automated feeder artery detection software among three reconstruction algorithms (P = 0.102). The contrast, continuity, and confidence level of feeder artery detection was significantly better on DLR-S than on DLR-M (P = 0.013, 0.005, and 0.001) and hybrid-IR (P < 0.001, P = 0.002, and P < 0.001). The weighted kappa values between two readers for qualitative scores of feeder artery visualization were 0.807–0.874. The mean qualitative scores for sharpness, granulation, and diagnostic acceptability of CTHA were better on DLR-S than on DLR-M and hybrid-IR (P < 0.001).ConclusionsDLR significantly improved the SNR of small hepatic arteries, the CNR of tumor, and feeder artery visualization on CTHA images. DLR-S seems to be better suited to routine CTHA in TACE than does hybrid-IR.

Super-resolution Deep Learning Reconstruction for 3D Brain MR Imaging: Improvement of Cranial Nerve Depiction and Interobserver Agreement in Evaluations of Neurovascular Conflict

Rationale and ObjectivesTo determine if super-resolution deep learning reconstruction (SR-DLR) improves the depiction of cranial nerves and interobserver agreement when assessing neurovascular conflict in 3D fast asymmetric spin echo (3D FASE) brain MR images, as compared to deep learning reconstruction (DLR). Materials and MethodsThis retrospective study involved reconstructing 3D FASE MR images of the brain for 37 patients using SR-DLR and DLR. Three blinded readers conducted qualitative image analyses, evaluating the degree of neurovascular conflict, structure depiction, sharpness, noise, and diagnostic acceptability. Quantitative analyses included measuring edge rise distance (ERD), edge rise slope (ERS), and full width at half maximum (FWHM) using the signal intensity profile along a linear region of interest across the center of the basilar artery. ResultsInterobserver agreement on the degree of neurovascular conflict of the facial nerve was generally higher with SR-DLR (0.429–0.923) compared to DLR (0.175–0.689). SR-DLR exhibited increased subjective image noise compared to DLR (p ≤ 0.008). However, all three readers found SR-DLR significantly superior in terms of sharpness (p < 0.001); cranial nerve depiction, particularly of facial and acoustic nerves, as well as the osseous spiral lamina (p < 0.001); and diagnostic acceptability (p ≤ 0.002). The FWHM (mm)/ERD (mm)/ERS (mm-1) for SR-DLR and DLR was 3.1–4.3/0.9–1.1/8,795.5–10,703.5 and 3.3–4.8/1.4–2.1/5,157.9–7,705.8, respectively, with SR-DLR's image sharpness being significantly superior (p ≤ 0.001). ConclusionsSR-DLR enhances image sharpness, leading to improved cranial nerve depiction and a tendency for greater interobserver agreement regarding facial nerve neurovascular conflict.

Super-resolution Deep Learning Reconstruction Cervical Spine 1.5T MRI: Improved Interobserver Agreement in Evaluations of Neuroforaminal Stenosis Compared to Conventional Deep Learning Reconstruction.

The aim of this study was to investigate whether super-resolution deep learning reconstruction (SR-DLR) is superior to conventional deep learning reconstruction (DLR) with respect to interobserver agreement in the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI. This retrospective study included 39 patients who underwent 1.5T cervical spine MRI. T2-weighted sagittal images were reconstructed with SR-DLR and DLR. Three blinded radiologists independently evaluated the images in terms of the degree of neuroforaminal stenosis, depictions of the vertebrae, spinal cord and neural foramina, sharpness, noise, artefacts and diagnostic acceptability. In quantitative image analyses, a fourth radiologist evaluated the signal-to-noise ratio (SNR) by placing a circular or ovoid region of interest on the spinal cord, and the edge slope based on a linear region of interest placed across the surface of the spinal cord. Interobserver agreement in the evaluations of neuroforaminal stenosis using SR-DLR and DLR was 0.422-0.571 and 0.410-0.542, respectively. The kappa values between reader 1 vs. reader 2 and reader 2 vs. reader 3 significantly differed. Two of the three readers rated depictions of the spinal cord, sharpness, and diagnostic acceptability as significantly better with SR-DLR than with DLR. Both SNR and edge slope (/mm) were also significantly better with SR-DLR (12.9 and 6031, respectively) than with DLR (11.5 and 3741, respectively) (p < 0.001 for both). In conclusion, compared to DLR, SR-DLR improved interobserver agreement in the evaluations of neuroforaminal stenosis using 1.5T cervical spine MRI.

Deep Learning Imaging Reconstruction Algorithm for Carotid Dual Energy CT Angiography: Opportunistic Evaluation of Cervical Intervertebral Discs—A Preliminary Study

Thus, the aim of this study is to evaluate the performance of deep learning imaging reconstruction (DLIR) algorithm in different image sets derived from carotid dual-energy computed tomography angiography (DECTA) for evaluating cervical intervertebral discs (IVDs) and compare them with those reconstructed using adaptive statistical iterative reconstruction-Veo (ASiR-V). Forty-two patients who underwent carotid DECTA were included in this retrospective analysis. Three types of image sets (70 keV, water-iodine, and water-calcium) were reconstructed using 50% ASiR-V and DLIR at medium and high levels (DLIR-M and DLIR-H). The diagnostic acceptability and conspicuity of IVDs were assessed using a 5-point scale. Hounsfield Units (HU) and water concentration (WC) values of the IVDs; standard deviation (SD); and coefficient of variation (CV) were calculated. Measurement parameters of the 50% ASIR-V, DLIR-M, and DLIR-H groups were compared. The DLIR-H group showed higher scores for diagnostic acceptability and conspicuity, as well as lower SD values for HU and WC than the ASiR-V and DLIR-M groups for the 70 keV and water-iodine image sets (all p < .001). However, there was no significant difference in scores and SD among the three groups for the water-calcium image set (all p > .005). The water-calcium image set showed better diagnostic accuracy for evaluating IVDs compared to the other image sets. The inter-rater agreement using ASiR-V, DLIR-M, and DLIR-H was good for the 70 keV image set, excellent for the water-iodine and water-calcium image sets. DLIR improved the visualization of IVDs in the 70 keV and water-iodine image sets. However, its improvement on color-coded water-calcium image set was limited.

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.

Safety and Diagnostic Efficacy of Image-Guided Biopsy of Small Renal Masses.

Image-guided renal mass biopsy is gaining increased diagnostic acceptance, but there are limited data concerning the safety and diagnostic yield of biopsy for small renal masses (≤4 cm). This study evaluated the safety, diagnostic yield, and management after image-guided percutaneous biopsy for small renal masses. A retrospective IRB-approved study was conducted on patients who underwent renal mass biopsy for histopathologic diagnosis at a single center from 2015 to 2021. Patients with a prior history of malignancy or a renal mass >4 cm were excluded. Descriptive statistics were used to summarize patient demographics, tumor size, the imaging modality used for biopsy, procedure details, complications, pathological diagnosis, and post-biopsy management. A biopsy was considered successful when the specimen was sufficient for diagnosis without need for a repeat biopsy. Complications were graded according to the SIR classification of adverse events. A chi-squared test (significance level set at p ≤ 0.05) was used to compare the success rate of biopsies in different lesion size groups. A total of 167 patients met the inclusion criteria. The median age was 65 years (range: 26-87) and 51% were male. The median renal mass size was 2.6 cm (range: one-four). Ultrasound was solely employed in 60% of procedures, CT in 33%, a combination of US/CT in 6%, and MRI in one case. With on-site cytopathology, the median number of specimens obtained per procedure was four (range: one-nine). The overall complication rate was 5%. Grade A complications were seen in 4% (n = 7), consisting of perinephric hematoma (n = 6) and retroperitoneal hematoma (n = 1). There was one grade B complication (0.5%; pain) and one grade D complication (0.5%; pyelonephritis). There was no patient mortality within 30 days post-biopsy. Biopsy was successful in 88% of cases. A sub-group analysis showed a success rate of 85% in tumors <3 cm and 93% in tumors ≥3 cm (p = 0.01). Pathological diagnoses included renal cell carcinoma (65%), oncocytoma (18%), clear cell papillary renal cell tumors (9%), angiomyolipoma (4%), xanthogranulomatous pyelonephritis (1%), lymphoma (1%), high-grade papillary urothelial carcinoma (1%), and metanephric adenoma (1%), revealing benign diagnosis in 30% of cases. The most common treatment was surgery (40%), followed by percutaneous cryoablation (22%). In total, 37% of patients were managed conservatively, and one patient received chemotherapy. This study demonstrates the safety and diagnostic efficacy of image-guided biopsy of small renal masses. The diagnostic yield was significantly higher for masses 3-4 cm in size compared to those <3 cm. The biopsy results showed a high percentage of benign diagnoses and informed treatment decisions in most patients.

Low-tube-voltage whole-body CT angiography with extremely low iodine dose: a comparison between hybrid-iterative reconstruction and deep-learning image-reconstruction algorithms

To evaluate arterial enhancement, its depiction, and image quality in low-tube potential whole-body computed tomography (CT) angiography (CTA) with extremely low iodine dose and compare the results with those obtained by hybrid-iterative reconstruction (IR) and deep-learning image-reconstruction (DLIR) methods. This prospective study included 34 consecutive participants (27 men; mean age, 74.2 years) who underwent whole-body CTA at 80 kVp for evaluating aortic diseases between January and July 2020. Contrast material (240 mg iodine/ml) with simultaneous administration of its quarter volume of saline, which corresponded to 192 mg iodine/ml, was administered. CT raw data were reconstructed using adaptive statistical IR-Veo of 40% (hybrid-IR), DLIR with medium- (DLIR-M), and high-strength level (DLIR-H). A radiologist measured CT attenuation of the arteries and background noise, and the signal-to-noise ratio (SNR) was then calculated. Two reviewers qualitatively evaluated the arterial depictions and diagnostic acceptability on axial, multiplanar-reformatted (MPR), and volume-rendered (VR) images. Mean contrast material volume and iodine weight administered were 64.1 ml and 15.4 g, respectively. The SNRs of the arteries were significantly higher in the following order of the DLIR-H, DLIR-M, and hybrid-IR (p<0.001). Depictions of six arteries on axial, three arteries on MPR, and four arteries on VR images were significantly superior in the DLIR-M or hybrid-IR than in the DLIR-H (p≤0.009 for each). Diagnostic acceptability was significantly better in the DLIR-M and DLIR-H than in the hybrid-IR (p<0.001-0.005). DLIR-M showed well-balanced arterial depictions and image quality compared with the hybrid-IR and DLIR-H.

Analysis of Artefacts in Endodontically Treated Teeth on Cone Beam Computed (CBCT) Images and their Impact on Diagnostic Value

The aim of this paper was to investigate the incidence of artefacts produced by endodontically treated teeth on CBCT images and their impacts the diagnostic value of the image for endodontic purposes. A retrospective analysis of 57 root filled teeth from 40 CBCT scans from UCLAN dental clinic between 2016-2020 was undertaken. Each tooth was split into crown and root section with the incidence of all known artefacts recorded by the binary yes/no variable. Subjective assessment into the diagnostic acceptability of the image in relation to endodontic indications stated in the European Society of Endodontics guidance was done. Age, gender, field of view, tooth type, location, coronal restoration of subject tooth and adjacent teeth were recorded. Statistical analysis (SPSS v28, IBM system) was used to assess the relationship between study factors, artefact expression and the effects on diagnostic quality. Scatter artefacts were present in 100% of samples with Beam Hardening being present in 94.7% of samples. Image Noise, Motion and Aliasing (distortion) artefacts reported an incidence ranging between 0-17.5%. The significance level was set at p&lt;0.05. Of the study characteristics Beam Hardening was affected by tooth type/location, coronal restoration of subject tooth and adjacent tooth whereas Motion and Aliasing artefacts were affected by the number of roots and tooth type/location respectively. Beam Hardening, Image Noise, Motion and Aliasing artefacts were shown to have a statistically significant association with the diagnostic acceptability of the scans of root filled teeth for endodontic purposes especially in the axial and sagittal views. Beam Hardening had the most significant impact on diagnostic quality. This is the first study to show artefact expression is much more prevalent than previously reported and has strong ability to affect the diagnostic acceptability of CBCT scans of root filled teeth taken for endodontic purpose.

Somatic Symptom and Related Disorders in Youth: Diagnostic Acceptance, Reduced Overmedicalization, and Capture of Medical Comorbidity, Through Enhanced Communication and Restructured Care

Somatic Symptom and Related Disorders in Youth: Diagnostic Acceptance, Reduced Overmedicalization, and Capture of Medical Comorbidity, Through Enhanced Communication and Restructured Care

Comparison of image quality, arterial depiction, and radiation dose between two rapid kVp-switching dual-energy CT scanners in CT angiography at 40-keV

PurposeTo compare the quantitative parameters and qualitative image quality of dual-energy CT angiography (CTA) between two rapid kVp-switching dual-energy CT scanners.Materials and methodsBetween May 2021 and March 2022, 79 participants underwent whole-body CTA using either Discovery CT750 HD (Group A, n = 38) or Revolution CT Apex (Group B, n = 41). All data were reconstructed at 40-keV and with adaptive statistical iterative reconstruction-Veo of 40%. The two groups were compared in terms of CT numbers of the thoracic and abdominal aorta, and the iliac artery, background noise, signal-to-noise ratio (SNR) of the artery, CT dose-index volume (CTDIvol), and qualitative scores for image noise, sharpness, diagnostic acceptability, and arterial depictions.ResultsThe median CT number of the abdominal aorta (p = 0.04) and SNR of the thoracic aorta (p = 0.02) were higher in Group B than in Group A, while no difference was observed in the other CT numbers and SNRs of the artery (p = 0.09–0.23). The background noises at the thoracic (p = 0.11), abdominal (p = 0.85), and pelvic (p = 0.85) regions were comparable between the two groups. CTDIvol was lower in Group B than in Group A (p = 0.006). All qualitative scores were higher in Group B than in Group A (p < 0.001–0.04). The arterial depictions were nearly identical in both two groups (p = 0.005–1.0).ConclusionIn dual-energy CTA at 40-keV, Revolution CT Apex improved qualitative image quality and reduced radiation dose.

Individualized scan protocols for CT angiography: an animal study for contrast media or radiation dose optimization

BackgroundWe investigated about optimization of contrast media (CM) dose or radiation dose in thoracoabdominal computed tomography angiography (CTA) by automated tube voltage selection (ATVS) system configuration and CM protocol adaption.MethodsIn six minipigs, CTA-optimized protocols were evaluated regarding objective (contrast-to-noise ratio, CNR) and subjective (6 criteria assessed by Likert scale) image quality. Scan parameters were automatically adapted by the ATVS system operating at 90-kV semi-mode and configured for standard, CM saving, or radiation dose saving (image task, quality settings). Injection protocols (dose, flow rate) were adapted manually. This approach was tested for normal and simulated obese conditions.ResultsRadiation exposure (volume-weighted CT dose index) for normal (obese) conditions was 2.4 ± 0.7 (5.0 ± 0.7) mGy (standard), 4.3 ± 1.1 (9.0 ± 1.3) mGy (CM reduced), and 1.7 ± 0.5 (3.5 ± 0.5) mGy (radiation reduced). The respective CM doses for normal (obese) settings were 210 (240) mgI/kg, 155 (177) mgI/kg, and 252 (288) mgI/kg. No significant differences in CNR (normal; obese) were observed between standard (17.8 ± 3.0; 19.2 ± 4.0), CM-reduced (18.2 ± 3.3; 20.5 ± 4.9), and radiation-saving CTAs (16.0 ± 3.4; 18.4 ± 4.1). Subjective analysis showed similar values for optimized and standard CTAs. Only the parameter diagnostic acceptability was significantly lower for radiation-saving CTA compared to the standard CTA.ConclusionsThe CM dose (-26%) or radiation dose (-30%) for thoracoabdominal CTA can be reduced while maintaining objective and subjective image quality, demonstrating the feasibility of the personalization of CTA scan protocols.Key points• Computed tomography angiography protocols could be adapted to individual patient requirements using an automated tube voltage selection system combined with adjusted contrast media injection.• Using an adapted automated tube voltage selection system, a contrast media dose reduction (-26%) or radiation dose reduction (-30%) could be possible

Ultra-High-Resolution CT of the Head and Neck with Deep Learning Reconstruction-Assessment of Image Quality and Radiation Exposure and Intraindividual Comparison with Normal-Resolution CT.

To assess the benefits of ultra-high-resolution CT (UHR-CT) with deep learning-based image reconstruction engine (AiCE) regarding image quality and radiation dose and intraindividually compare it to normal-resolution CT (NR-CT). Forty consecutive patients with head and neck UHR-CT with AiCE for diagnosed head and neck malignancies and available prior NR-CT of a different scanner were retrospectively evaluated. Two readers evaluated subjective image quality using a 5-point Likert scale regarding image noise, image sharpness, artifacts, diagnostic acceptability, and assessability of various anatomic regions. For reproducibility, inter-reader agreement was analyzed. Furthermore, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and slope of the gray-value transition between different tissues were calculated. Radiation dose was evaluated by comparing CTDIvol, DLP, and mean effective dose values. UHR-CT with AiCE reconstruction led to significant improvement in subjective (image noise and diagnostic acceptability: p < 0.000; ICC ≥ 0.91) and objective image quality (SNR: p < 0.000; CNR: p < 0.025) at significantly lower radiation doses (NR-CT 2.03 ± 0.14 mSv; UHR-CT 1.45 ± 0.11 mSv; p < 0.0001) compared to NR-CT. Compared to NR-CT, UHR-CT combined with AiCE provides superior image quality at a markedly lower radiation dose. With improved soft tissue assessment and potentially improved tumor detection, UHR-CT may add further value to the role of CT in the assessment of head and neck pathologies.

Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers.

To evaluate image quality, diagnostic acceptability, and lesion conspicuity in abdominal dual-energy CT (DECT) using deep learning image reconstruction (DLIR) compared to those using adaptive statistical iterative reconstruction-V (Asir-V) at 50% blending (AV-50), and to identify potential factors impacting lesion conspicuity. The portal-venous phase scans in abdominal DECT of 47 participants with 84 lesions were prospectively included. The raw data were reconstructed to virtual monoenergetic image (VMI) at 50keV using filtered back-projection (FBP), AV-50, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H). A noise power spectrum (NPS) was generated. CT number and standard deviation values of eight anatomical sites were measured. Signal-to-noise (SNR), and contrast-to-noise ratio (CNR) values were calculated. Five radiologists assessed image quality in terms of image contrast, image noise, image sharpness, artificial sensation, and diagnostic acceptability, and evaluated the lesion conspicuity. DLIR further reduced image noise (p < 0.001) compared to AV-50 while better preserved the average NPS frequency (p < 0.001). DLIR maintained CT number values (p > 0.99) and improved SNR and CNR values compared to AV-50 (p < 0.001). DLIR-H and DLIR-M showed higher ratings in all image quality analyses than AV-50 (p < 0.001). DLIR-H provided significantly better lesion conspicuity than AV-50 and DLIR-M regardless of lesion size, relative CT attenuation to surrounding tissue, or clinical purpose (p < 0.05). DLIR-H could be safely recommended for routine low-keV VMI reconstruction in daily contrast-enhanced abdominal DECT to improve image quality, diagnostic acceptability, and lesion conspicuity. • DLIR is superior to AV-50 in noise reduction, with less shifts of the average spatial frequency of NPS towards low frequency, and larger improvements of NPS noise, noise peak, SNR, and CNR values. • DLIR-M and DLIR-H generate better image quality in terms of image contrast, noise, sharpness, artificial sensation, and diagnostic acceptability than AV-50, while DLIR-H provides better lesion conspicuity than AV-50 and DLIR-M. • DLIR-H could be safely recommended as a new standard for routine low-keV VMI reconstruction in contrast-enhanced abdominal DECT to provide better lesion conspicuity and better image quality than the standard AV-50.

Unpaired low-dose computed tomography image denoising using a progressive cyclical convolutional neural network.

Reducing the radiation dose from computed tomography (CT) can significantly reduce the radiation risk to patients. However, low-dose CT (LDCT) suffers from severe and complex noise interference that affects subsequent diagnosis and analysis. Recently, deep learning-based methods have shown superior performance in LDCT image-denoising tasks. However, most methods require many normal-dose and low-dose CT image pairs, which are difficult to obtain in clinical applications. Unsupervised methods, on the other hand, are more general. Deep learning methods based on GAN networks have been widely used for unsupervised LDCT denoising, but the additional memory requirements of the model also hinder its further clinical application. To this end, we propose a simpler multi-stage denoising framework trained using unpaired data, the progressive cyclical convolutional neural network (PCCNN), which can remove the noise from CT images in latent space. Our proposed PCCNN introduces a noise transfer model that transfers noise from LDCT to normal-dose CT (NDCT), denoised CT images generated from unpaired CT images, and noisy CT images. The denoising framework also contains a progressive module that effectively removes noise through multi-stage wavelet transforms without sacrificing high-frequency components such as edges and details. Compared with seven LDCT denoising algorithms, we perform a quantitative and qualitative evaluation of the experimental results and perform ablation experiments on each network module and loss function. On the AAPM dataset, compared with the contrasted unsupervised methods, our denoising framework has excellent denoising performance increasing the peak signal-to-noise ratio (PSNR) from 29.622 to 30.671, and the structural similarity index (SSIM) was increased from 0.8544 to 0.9199. The PCCNN denoising results were relatively optimal and statistically significant. In the qualitative result comparison, PCCNN without introducing additional blurring and artifacts, the resulting image has higher resolution and complete detail preservation, and the overall structural texture of the image is closer to NDCT. In visual assessments, PCCNN achieves a relatively balanced result in noise suppression, contrast retention, and lesion discrimination. Extensive experimental validation shows that our scheme achieves reconstruction results comparable to supervised learning methods and has performed well in image quality and medical diagnostic acceptability.

Acceleration of knee magnetic resonance imaging using a combination of compressed sensing and commercially available deep learning reconstruction: a preliminary study

PurposeTo evaluate whether deep learning reconstruction (DLR) accelerates the acquisition of 1.5-T magnetic resonance imaging (MRI) knee data without image deterioration.Materials and methodsTwenty-one healthy volunteers underwent MRI of the right knee on a 1.5-T MRI scanner. Proton-density-weighted images with one or four numbers of signal averages (NSAs) were obtained via compressed sensing, and DLR was applied to the images with 1 NSA to obtain 1NSA-DLR images. The 1NSA-DLR and 4NSA images were compared objectively (by deriving the signal-to-noise ratios of the lateral and the medial menisci and the contrast-to-noise ratios of the lateral and the medial menisci and articular cartilages) and subjectively (in terms of the visibility of the anterior cruciate ligament, the medial collateral ligament, the medial and lateral menisci, and bone) and in terms of image noise, artifacts, and overall diagnostic acceptability. The paired t-test and Wilcoxon signed-rank test were used for statistical analyses.ResultsThe 1NSA-DLR images were obtained within 100 s. The signal-to-noise ratios (lateral: 3.27 ± 0.30 vs. 1.90 ± 0.13, medial: 2.71 ± 0.24 vs. 1.80 ± 0.15, both p < 0.001) and contrast-to-noise ratios (lateral: 2.61 ± 0.51 vs. 2.18 ± 0.58, medial 2.19 ± 0.32 vs. 1.97 ± 0.36, both p < 0.001) were significantly higher for 1NSA-DLR than 4NSA images. Subjectively, all anatomical structures (except bone) were significantly clearer on the 1NSA-DLR than on the 4NSA images. Also, in the former images, the noise was lower, and the overall diagnostic acceptability was higher.ConclusionCompared with the 4NSA images, the 1NSA-DLR images exhibited less noise, higher overall image quality, and allowed more precise visualization of the menisci and ligaments.

Commercially Available Deep-learning-reconstruction of MR Imaging of the Knee at 1.5T Has Higher Image Quality Than Conventionally-reconstructed Imaging at 3T: A Normal Volunteer Study.

This study aimed to evaluate whether the image quality of 1.5T magnetic resonance imaging (MRI) of the knee is equal to or higher than that of 3T MRI by applying deep learning reconstruction (DLR). Proton density-weighted images of the right knee of 27 healthy volunteers were obtained by 3T and 1.5T MRI scanners using similar imaging parameters (21 for high resolution image and 6 for normal resolution image). Commercially available DLR was applied to the 1.5T images to obtain 1.5T/DLR images. The 3T and 1.5T/DLR images were compared subjectively for visibility of structures, image noise, artifacts, and overall diagnostic acceptability and objectively. One-way ANOVA and Friedman tests were used for the statistical analyses. For the high resolution images, all of the anatomical structures, except for bone, were depicted significantly better on the 1.5T/DLR compared with 3T images. Image noise scored statistically lower and overall diagnostic acceptability scored higher on the 1.5T/DLR images. The contrast between lateral meniscus and articular cartilage of the 1.5T/DLR images was significantly higher (5.89 ± 1.30 vs. 4.34 ± 0.87, P < 0.001), and also the contrast between medial meniscus and articular cartilage of the 1.5T/DLR images was significantly higher (5.12 ± 0.93 vs. 3.87 ± 0.56, P < 0.001). Similar image quality improvement by DLR was observed for the normal resolution images. The 1.5T/DLR images can achieve less noise, more precise visualization of the meniscus and ligaments, and higher overall image quality compared with the 3T images acquired using a similar protocol.

Compete or Cooperate with 'Dr. Google'? Small Animal Veterinarians' Attitudes towards Clients' Use of Internet Resources-A Comparative Study across Austria, Denmark and the UK.

Simple SummaryOwners of dogs, cats, and other companion animals increasingly make use of the internet to find out how to best care for their animals. This may affect owners’ relations with veterinarians in both positive and negative ways. A positive consequence could be that owners are better informed when they approach a veterinarian. However, there can also be challenging situations in which the owners may question veterinarians’ professional advice based on online information. Using a questionnaire, we found that a majority of Austrian, Danish, and UK veterinarians (n = 641) surveyed were occasionally confronted with clients who question their medical advice based on information obtained from the internet. In addition, the veterinarians were concerned about the potential for clients to misunderstand information found on the internet, or to develop unrealistic expectations of what is possible in small animal practices. As internet use becomes ever more widespread, we suggest that the types of resources that are available and used by animal owners should be further explored.Veterinary medicine is increasingly affected by animal owners having the opportunity to become better informed on pet health issues by using various internet resources. Using an online questionnaire including a section on clients’ use of internet resources to obtain medical information, this study aimed to investigate veterinarians’ estimates of the percentage of clients using internet resources, how often clients question veterinarians’ professional medical advice based on online information, and veterinarians’ attitudes towards clients’ use of internet resources, across Austrian, Danish, and UK veterinarians (n = 641). The results show that 48.8% of respondents estimated that 40–79% of their clients use internet resources to find medical information. Further, 70–80% of respondents stated that they are occasionally challenged by clients questioning their advice based on online information. Although veterinarians recognized the potential advantages related to clients’ use of internet resources, such as an increased acceptance of advanced diagnostics and treatments, they also highlighted clients’ increased expectations or false impressions of small animal practices as potentially negative aspects in this context. As internet use increases, it seems likely that these issues will become increasingly important in the future.

A Dual-Encoder-Single-Decoder Based Low-Dose CT Denoising Network.

Generative adversarial networks (GAN) have shown great potential for image quality improvement in low-dose CT (LDCT). In general, the shallow features of generator include more shallow visual information such as edges and texture, while the deep features of generator contain more deep semantic information such as organization structure. To improve the network's ability to categorically deal with different kinds of information, this paper proposes a new type of GAN with dual-encoder- single-decoder structure. In the structure of the generator, firstly, a pyramid non-local attention module in the main encoder channel is designed to improve the feature extraction effectiveness by enhancing the features with self-similarity; Secondly, another encoder with shallow feature processing module and deep feature processing module is proposed to improve the encoding capabilities of the generator; Finally, the final denoised CT image is generated by fusing main encoder's features, shallow visual features, and deep semantic features. The quality of the generated images is improved due to the use of feature complementation in the generator. In order to improve the adversarial training ability of discriminator, a hierarchical-split ResNet structure is proposed, which improves the feature's richness and reduces the feature's redundancy in discriminator. The experimental results show that compared with the traditional single-encoder- single-decoder based GAN, the proposed method performs better in both image quality and medical diagnostic acceptability. Code is available in https://github.com/hanzefang/DESDGAN.