T1-weighted Motion Mitigation in Abdominal MRI: Technical Principles, Clinical Applications, Current Limitations, and Future Prospects.

T1-weighted (T1W) pulse sequences are an indispensable component of clinical protocols in abdominal MRI but usually require multiple breath holds (BHs) during the examination, which not all patients can sustain. Patient motion can affect the quality of T1W imaging so that key diagnostic information, such as intrinsic signal intensity and contrast enhancement image patterns, cannot be determined. Patient motion also has a negative impact on examination efficiency, as multiple acquisition attempts prolong the duration of the examination and often remain noncontributory. Techniques for mitigation of motion-related artifacts at T1W imaging include multiple arterial acquisitions within one BH; free breathing with respiratory gating or respiratory triggering; and radial imaging acquisition techniques, such as golden-angle radial k-space acquisition (stack-of-stars). While each of these techniques has inherent strengths and limitations, the selection of a specific motion-mitigation technique is based on several factors, including the clinical task under investigation, downstream technical ramifications, patient condition, and user preference. The authors review the technical principles of free-breathing motion mitigation techniques in abdominal MRI with T1W sequences, offer an overview of the established clinical applications, and outline the existing limitations of these techniques. In addition, practical guidance for abdominal MRI protocol strategies commonly encountered in clinical scenarios involving patients with limited BH abilities is rendered. Future prospects of free-breathing T1W imaging in abdominal MRI are also discussed. ©RSNA, 2024 See the invited commentary by Fraum and An in this issue.

Application of apical myocardial perfusion quantitative analysis by contrast-enhanced ultrasound utilizing high-frequency linear probe.

BACKGROUND: Due to insufficient near-field resolution and artifacts, it is challenging to evaluate the left ventricular apical perfusion with phased-array probes. By combining high-frequency linear probe and contrast-enhanced ultrasound (CEUS), imaging of apical myocardial perfusion could be improved. The study aims to evaluate the preliminary application of CEUS by high-frequency linear probes to assess the apical perfusion. METHODS: The study enrolled retrospectively 91 patients to test the feasibility of the novel method. In protocol 1, patients were stratified into a group with left anterior descending artery (LAD) stenosis (N = 40) and a group without LAD stenosis or coronary artery disease (N = 41) based on the degree of coronary artery narrowing, quantified by >50% stenosis in coronary angiography. Receiver operating characteristics (ROC) analysis was performed to test the diagnostic value of perfusion parameters. In protocol 2, the reproducibility of high-frequency linear probe in apical perfusion analysis was compared with the conventional phased-array probe in 30 patients. RESULTS: (1) The novel method is feasible in 81(89.01%) patients. (2) In protocol 1, to detect LAD stenosis, the best cut-off of ß, T, A, and MBF were 10.32, 3.28, 9.39, and 4.99, respectively. Area under the curve of ß, T, A, and MBF were .880, .881, .761, and .880, respectively. (3) In protocol 2, compared with phased-array probe, the quantitative analysis of high-frequency linear probe is of high reproducibility and could get good curve fitting (R2 = .29 vs. R2 = .71, P < .01). CONCLUSION: Observation of apical perfusion using this method is feasible and quantitative analysis allows an accurate and convenient identification of LAD stenosis. This method provides an alternative for patients who have difficulties in visualizing the apical region with a phased-array probe.

Intraindividual Comparison of Different Methods for Automated BPE Assessment at Breast MRI: A Call for Standardization.

Background The level of background parenchymal enhancement (BPE) at breast MRI provides predictive and prognostic information and can have diagnostic implications. However, there is a lack of standardization regarding BPE assessment. Purpose To investigate how well results of quantitative BPE assessment methods correlate among themselves and with assessments made by radiologists experienced in breast MRI. Materials and Methods In this pseudoprospective analysis of 5773 breast MRI examinations from 3207 patients (mean age, 60 years ± 10 [SD]), the level of BPE was prospectively categorized according to the Breast Imaging Reporting and Data System by radiologists experienced in breast MRI. For automated extraction of BPE, fibroglandular tissue (FGT) was segmented in an automated pipeline. Four different published methods for automated quantitative BPE extractions were used: two methods (A and B) based on enhancement intensity and two methods (C and D) based on the volume of enhanced FGT. The results from all methods were correlated, and agreement was investigated in comparison with the respective radiologist-based categorization. For surrogate validation of BPE assessment, how accurately the methods distinguished premenopausal women with (n = 50) versus without (n = 896) antihormonal treatment was determined. Results Intensity-based methods (A and B) exhibited a correlation with radiologist-based categorization of 0.56 ± 0.01 and 0.55 ± 0.01, respectively, and volume-based methods (C and D) had a correlation of 0.52 ± 0.01 and 0.50 ± 0.01 (P < .001). There were notable correlation differences (P < .001) between the BPE determined with the four methods. Among the four quantitation methods, method D offered the highest accuracy for distinguishing women with versus without antihormonal therapy (P = .01). Conclusion Results of different methods for quantitative BPE assessment agree only moderately among themselves or with visual categories reported by experienced radiologists; intensity-based methods correlate more closely with radiologists' ratings than volume-based methods. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Mann in this issue.

Conventional and Contrast-enhanced US of the Lung: From Performance to Diagnosis.

In recent years, lung US has evolved from a marginal tool to an integral component of diagnostic chest imaging. Contrast-enhanced US (CEUS) can improve routine gray-scale imaging of the lung and chest, particularly in diagnosis of peripheral lung diseases (PLDs). Although an underused tool in many centers, and despite inherent limitations in evaluation of central lung disease caused by high acoustic impedance between air and soft tissues, lung CEUS has emerged as a valuable tool in diagnosis of PLDs. Owing to the dual arterial supply to the lungs via pulmonary and bronchial (systemic) arteries, different enhancement patterns can be observed at lung CEUS, thereby enabling accurate differential diagnoses in various PLDs. Lung CEUS also assists in identifying patients who may benefit from complementary diagnostic tests, including image-guided percutaneous biopsy. Moreover, lung CEUS-guided percutaneous biopsy has shown feasibility in accessible subpleural lesions, enabling higher histopathologic performance without significantly increasing either imaging time or expenses compared with conventional US. The authors discuss the technique of and basic normal and pathologic findings at conventional lung US, followed by a more detailed discussion of lung CEUS applications, emphasizing specific aspects of pulmonary physiology, basic concepts in lung US enhancement, and the most commonly encountered enhancement patterns of different PLDs. Finally, they discuss the benefits of lung CEUS in planning and guidance of US-guided lung biopsy. ©RSNA, 2024 Supplemental material is available for this article.

[Multiparametric MRI in hepatocellular carcinoma, part 2 : Diffusion-weighted imaging in the primary diagnostics and treatment monitoring]. / Multiparametrische MRT-Bildgebung beim hepatozellulären Karzinom, Teil 2 : Diffusionsbildgebung in der Primärdiagnostik und Therapieverlaufskontrolle.

In addition to morphology and tissue perfusion, diffusion-weighted imaging (DWI) is the third pillar of multiparametric diagnostics in oncology. Due to the strong correlation between the apparent diffusion coefficient (ADC) and cell count in hepatocellular carcinoma (HCC), it can be used as a surrogate marker for tumor cell quantity. Therefore, ADC effectively reflects the effects of cytoreductive treatment, such as transarterial chemoembolization (TACE) and systemic chemotherapy and becomes an important clinical marker for treatment response. The DWI should remain an integral part of a magnetic resonance imaging (MRI) protocol in primary HCC diagnostics and treatment monitoring but is of secondary clinical importance compared to contrast-enhanced MRI perfusion sequences and the use of liver-specific contrast agents. For the future, standardization of DWI sequences for better comparability of various study protocols would be desirable.

Comparison of a Whole-Brain Contrast-Enhanced 3D TSE T1WI versus Orbits Contrast-Enhanced 2D Coronal T1WI at 3T MRI for the Detection of Optic Nerve Enhancement in Patients with Acute Loss of Visual Acuity.

BACKGROUND AND PURPOSE: MR imaging is the technique of choice for patients presenting with acute loss of visual acuity with no obvious ophthalmologic cause. The goal of our study was to compare orbits contrast-enhanced 2D coronal T1WI with a whole-brain contrast-enhanced 3D (WBCE-3D) TSE T1WI at 3T for the detection of optic nerve enhancement. MATERIALS AND METHODS: This institutional review board-approved retrospective single-center study included patients presenting with acute loss of vision who underwent 3T MR imaging from November 2014 to February 2020. Two radiologists, blinded to all data, individually assessed the presence of enhancement of the optic nerve on orbits contrast-enhanced 2D T1WI and WBCE-3D T1WI separately and in random order. A McNemar test and a Cohen κ method were used for comparing the 2 MR imaging sequences. RESULTS: One thousand twenty-three patients (638 women and 385 men; mean age, 42 [SD, 18.3] years) were included. There was a strong concordance between WBCE-3D T1WI and orbits contrast-enhanced 2D T1WI when detecting enhancement of the optic nerve: κ = 0.87 (95% CI, 0.84-0.90). WBCE-3D T1WI was significantly more likely to detect canalicular enhancement compared with orbits contrast-enhanced 2D T1WI: 178/1023 (17.4%) versus 138/1023 (13.5%) (P < .001) and 108/1023 (10.6%) versus 90/1023 (8.8%) (P = .04), respectively. The WBCE-3D T1WI sequence detected 27/1023 (3%) instances of optic disc enhancement versus 0/1023 (0%) on orbits contrast-enhanced 2D T1WI. There were significantly fewer severe artifacts on WBCE-3D T1WI compared with orbits contrast-enhanced 2D T1WI: 68/1023 (6.6%) versus 101/1023 (9.8%) (P < .001). The median reader-reported confidence was significantly higher with coronal T1WI compared with 3D TSE T1WI: 5 (95% CI, 4-5) versus 3 (95% CI, 1-4; P < .001). CONCLUSIONS: Our study showed that there was a strong concordance between WBCE-3D T1WI and orbits contrast-enhanced 2D T1WI when detecting enhancement of the optic nerve in patients with acute loss of visual acuity with no obvious ophthalmologic cause. WBCE-3D T1WI demonstrated higher sensitivity and specificity in diagnosing optic neuritis, particularly in cases involving the canalicular segments.

Multispectral breast image grayscale and quality enhancement by repeated pair image registration & accumulation method.

Nowadays, for detecting breast cancer in its early stages, the focus is on multispectral transmission imaging. Frame accumulation is a promising technique to enhance the grayscale level of the multispectral transmission images. Still, during the image acquisition process, human respiration or camera jitter causes the displacement of the frame's sequence which leads to the loss of accuracy and image quality of the frame accumulated image is reduced. In this article, we have proposed a new method named "repeated pair image registration and accumulation "to resolve the issue. In this method first pair of images from the sequence is first registered and accumulated followed by the next pair to be registered and accumulated. Then these two accumulated frames are registered and accumulated again. This process is repeated until all the frames from the sequence are processed and the final image is obtained. This method is tested on the sequence of breast frames taken at 600 nm, 620 nm, 670 nm, and 760 nm wavelength of light and proved the enhancement of quality, accuracy, and grayscale by various mathematical assessments. Furthermore, the processing time of our proposed method is very low because descent gradient optimization algorithm is used here for image registration purpose. This optimization algorithm has high speed as compared to other methods and is verified by registering a single image of each wavelength by three different methods. It has laid the foundations of early detection of breast cancer using multispectral transmission imaging.

Comparison of the Ability of Gadobenate Dimeglumine and Gadolinium Ethoxybenzyl Dimeglumine to Display the major Features for Noninvasively Diagnosing Hepatocellular Carcinoma According to the LI-RADS 2018v.

OBJECTIVE: To compare the ability of gadolinium ethoxybenzyl dimeglumine (Gd-EOB-DTPA) and gadobenate dimeglumine (Gd-BOPTA) to display the 3 major features recommended by the Liver Imaging Reporting and Data System (LI-RADS 2018v) for diagnosing hepatocellular carcinoma (HCC). MATERIALS AND METHODS: In this retrospective study, we included 98 HCC lesions that were scanned with either Gd-EOB-DTPA-MR or Gd-BOPTA-M.For each lesion, we collected multiple variables, including size and enhancement pattern in the arterial phase (AP), portal venous phase (PVP), transitional phase (TP), delayed phase (DP), and hepatobiliary phase (HBP). The lesion-to-liver contrast (LLC) was measured and calculated for each phase and then compared between the 2 contrast agents. A P value < .05 was considered statistically significant. The display efficiency of the LLC between Gd-BOPTA and Gd-EOB-DTPA for HCC features was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: Between Gd-BOPTA and Gd-EOB-DTPA, significant differences were observed regarding the display efficiency for capsule enhancement and the LLC in the AP/PVP/DP (P < .05), but there was no significant difference regarding the LLC in the TP/HBP. Both Gd-BOPTA and Gd-EOB-DTPA had good display efficiency in each phase (AUCmin > 0.750). When conducting a total evaluation of the combined data across the 5 phases, the display efficiency was excellent (AUC > 0.950). CONCLUSION: Gd-BOPTA and Gd-EOB-DTPA are liver-specific contrast agents widely used in clinical practice. They have their own characteristics in displaying the 3 main signs of HCC. For accurate noninvasive diagnosis, the choice of agent should be made according to the specific situation.

Optical enhancement mode 2 improves the detection rate of gastric neoplastic lesion in high-risk populations: A multicenter randomized controlled clinical study.

OBJECTIVES: Detection of early neoplastic lesions is crucial for improving the survival rates of patients with gastric cancer. Optical enhancement mode 2 is a new image-enhanced endoscopic technique that offers bright images and can improve the visibility of neoplastic lesions. This study aimed to compare the detection of neoplastic lesions with optical enhancement mode 2 and white-light imaging (WLI) in a high-risk population. METHODS: In this prospective multicenter randomized controlled trial, patients were randomly assigned to optical enhancement mode 2 or WLI groups. Detection of suspicious neoplastic lesions during the examinations was recorded, and pathological diagnoses served as the gold standard. RESULTS: A total of 1211 and 1219 individuals were included in the optical enhancement mode 2 and WLI groups, respectively. The detection rate of neoplastic lesions was significantly higher in the optical enhancement mode 2 group (5.1% vs. 1.9%; risk ratio, 2.656 [95% confidence interval, 1.630-4.330]; p < 0.001). The detection rate of neoplastic lesions with an atrophic gastritis background was significantly higher in the optical enhancement mode 2 group (8.6% vs. 2.6%, p < 0.001). The optical enhancement mode 2 group also had a higher detection rate among endoscopists with different experiences. CONCLUSIONS: Optical enhancement mode 2 was more effective than WLI for detecting neoplastic lesions in the stomach, and can serve as a new method for screening early gastric cancer in clinical practice. CLINICAL REGISTRY: United States National Library of Medicine (https://www. CLINICALTRIALS: gov), ID: NCT040720521.

Self-supervised anatomical continuity enhancement network for 7T SWI synthesis from 3T SWI.

Synthesizing 7T Susceptibility Weighted Imaging (SWI) from 3T SWI could offer significant clinical benefits by combining the high sensitivity of 7T SWI for neurological disorders with the widespread availability of 3T SWI in diagnostic routines. Although methods exist for synthesizing 7T Magnetic Resonance Imaging (MRI), they primarily focus on traditional MRI modalities like T1-weighted imaging, rather than SWI. SWI poses unique challenges, including limited data availability and the invisibility of certain tissues in individual 3T SWI slices. To address these challenges, we propose a Self-supervised Anatomical Continuity Enhancement (SACE) network to synthesize 7T SWI from 3T SWI using plentiful 3T SWI data and limited 3T-7T paired data. The SACE employs two specifically designed pretext tasks to utilize low-level representations from abundant 3T SWI data for assisting 7T SWI synthesis in a downstream task with limited paired data. One pretext task emphasizes input-specific morphology by balancing the elimination of redundant patterns with the preservation of essential morphology, preventing the blurring of synthetic 7T SWI images. The other task improves the synthesis of tissues that are invisible in a single 3T SWI slice by aligning adjacent slices with the current slice and predicting their difference fields. The downstream task innovatively combines clinical knowledge with brain substructure diagrams to selectively enhance clinically relevant features. When evaluated on a dataset comprising 97 cases (5495 slices), the proposed method achieved a Peak Signal-to-Noise Ratio (PSNR) of 23.05 dB and a Structural Similarity Index (SSIM) of 0.688. Due to the absence of specific methods for 7T SWI, our method was compared with existing enhancement techniques for general 7T MRI synthesis, outperforming these techniques in the context of 7T SWI synthesis. Clinical evaluations have shown that our synthetic 7T SWI is clinically effective, demonstrating its potential as a clinical tool.

Optimizing Image Enhancement: Feature Engineering for Improved Classification in AI-Assisted Artificial Retinas.

Artificial retinas have revolutionized the lives of many blind people by enabling their ability to perceive vision via an implanted chip. Despite significant advancements, there are some limitations that cannot be ignored. Presenting all objects captured in a scene makes their identification difficult. Addressing this limitation is necessary because the artificial retina can utilize a very limited number of pixels to represent vision information. This problem in a multi-object scenario can be mitigated by enhancing images such that only the major objects are considered to be shown in vision. Although simple techniques like edge detection are used, they fall short in representing identifiable objects in complex scenarios, suggesting the idea of integrating primary object edges. To support this idea, the proposed classification model aims at identifying the primary objects based on a suggested set of selective features. The proposed classification model can then be equipped into the artificial retina system for filtering multiple primary objects to enhance vision. The suitability of handling multi-objects enables the system to cope with real-world complex scenarios. The proposed classification model is based on a multi-label deep neural network, specifically designed to leverage from the selective feature set. Initially, the enhanced images proposed in this research are compared with the ones that utilize an edge detection technique for single, dual, and multi-object images. These enhancements are also verified through an intensity profile analysis. Subsequently, the proposed classification model's performance is evaluated to show the significance of utilizing the suggested features. This includes evaluating the model's ability to correctly classify the top five, four, three, two, and one object(s), with respective accuracies of up to 84.8%, 85.2%, 86.8%, 91.8%, and 96.4%. Several comparisons such as training/validation loss and accuracies, precision, recall, specificity, and area under a curve indicate reliable results. Based on the overall evaluation of this study, it is concluded that using the suggested set of selective features not only improves the classification model's performance, but aligns with the specific problem to address the challenge of correctly identifying objects in multi-object scenarios. Therefore, the proposed classification model designed on the basis of selective features is considered to be a very useful tool in supporting the idea of optimizing image enhancement.

A Retinex-based network for image enhancement in low-light environments.

Most of the existing low-light image enhancement methods suffer from the problems of detail loss, color distortion and excessive noise. To address the above-mentioned issues, this paper proposes a neural network-based low-light image enhancement network. The network is divided into three parts: decomposition network, reflection component denoising network, and illumination component enhancement network. In the decomposition network, the input image is decomposed into a reflection image and an illumination image. In the reflection component denoising network, the Unet3+ network improved by fusion CA attention is adopted to denoise the reflection image. In the illumination component enhancement network, the adaptive mapping curve is adopted to enhance the illumination image iteratively. Finally, the processed illumination and reflection images are fused based on Retinex theory to obtain the final enhanced image. The experimental results show that the proposed network achieves excellent visual effects in subjective evaluation. Additionally, it shows a significant improvement in objective evaluation metrics, including PSNR, SSIM, NIQE, and so on, when compared to the results in several public datasets.

Based on Gadolinium Ethoxybenzyl DTPA-Enhanced MRI: Diagnostic Performance of the Category-Modified LR-5 Criteria in Patients At Risk for Hepatocellular Carcinoma.

Introduction: We aimed to modify the LR-5 strategy to improve the diagnostic sensitivity for hepatocellular carcinoma (HCC) in high-risk patients while maintaining specificity. Methods: This study retrospectively analyzed 412 patients with 445 liver observations who underwent preoperative gadolinium ethoxybenzyl DTPA (GD-EOB-DTPA)-enhanced MRI followed by surgical procedures or biopsies. All observations were classified according to LI-RADS v2018, and the classifications were adjusted by modifying major features (MF)(substituting threshold growth with a more HCC-specific ancillary features (AF): presence of blood products within the mass, arterial phase hyperenhancement (APHE) was interpreted with hypointensity on precontrast imaging- isointensity in arterial phase (AP) and extending washout to transitional phase (TP)(2 min)). The specificity, sensitivity, and positive predictive value (PPV) were assessed to compare LR-5 (definitely HCC) diagnostic efficacy between LI-RADS version 2018 and modified LI-RADS. Results: Apart from nonenhancing "capsule", the interreader agreement of MFs and HCC-specific AFs between the two readers reached substantial or excellent ranges (κ values ranging from 0.631 to 0.911). According to LI-5 v2018, the specificity, sensitivity and PPV of HCC were 90.74%, 82.35%, and 98.17%, respectively. Based on a more HCC-specific AF, signal intensity in AP and TP (2 min), the sensitivity of the three modified strategies were 86.19%, 93.09%, 96.67% (P < .05)), while maintaining high specificity and PPV rates at 88.89% and 98.25% (P > .05) Conclusion: Further investigation into the efficacy of threshold growth as a MF is warranted. By utilizing GD-EOB-DTPA-enhanced MRI, enhancing the sensitivity of the modified LR-5 category may be achieved without compromising specificity and PPV in diagnosing HCC among high-risk patients.

Low illumination fog noise image denoising method based on ACE-GPM.

The Perona-Malik (P-M) model exhibits deficiencies such as noise amplification, new noise introduction, and significant gradient effects when processing noisy images. To address these issues, this paper proposes an image-denoising algorithm, ACE-GPM, which integrates an Automatic Color Equalization (ACE) algorithm with a gradient-adjusted P-M model. Initially, the ACE algorithm is employed to enhance the contrast of low-light images obscured by fog and noise. Subsequently, the Otsu method, a technique to find the optimal threshold based on between-class variance, is applied for precise segmentation, enabling more accurate identification of different regions within the image. After that, distinct gradients enhance the image's foreground and background via an enhancement function that accentuates edge and detailed information. The denoising process is finalized by applying the gradient P-M model, employing a gradient descent approach to further emphasize image edges and details. Experimental evidence indicates that the proposed ACE-GPM algorithm not only elevates image contrast and eliminates noise more effectively than other denoising methods but also preserves image details and texture information, evidenced by an average increase of 0.42 in the information entropy value. Moreover, the proposed solution achieves these outcomes with reduced computational resource expenditures while maintaining high image quality.

[Muscle injuries: the importance of high-resolution dynamic sonography in diagnostics, treatment and monitoring]. / Muskelverletzungen: Stellenwert der hochauflösenden dynamischen Sonographie in der Diagnostik, Therapie und im Monitoring.

BACKGROUND: Muscle injuries are common in football. Imaging diagnostics have a major role in establishing a diagnosis. The main diagnostic procedures are MRI and ultrasound. Both diagnostics have advantages and disadvantages, which should be balanced against each other. NEW ULTRASONIC TECHNIQUES: The role of MRI as the gold standard is increasingly being replaced by high-resolution ultrasound techniques, and MRI imaging is not always useful. To detect complications in the early stages it is advised to perform regular ultrasound-imaging check-ups. The healing process can be monitored, and it offers additional options for ultrasound-guided interventions such as hematoma punctures and targeted infiltrations. ADVANTAGES AND DISADVANTAGES: However, ultrasound imaging is highly user dependent. Experienced operators can eliminate this disadvantage, which makes ultrasound a superior imaging system in many areas, especially for dynamic examinations. Nevertheless, MRI imaging remains a necessary imaging method in certain areas.

A Prospective Multicenter Study on the Additive Value of Contrast-Enhanced Ultrasound for Biopsy Decision of Ultrasound BI-RADS 4 Breast Lesions.

OBJECTIVE: The main aim of this study was to determine whether the use of contrast-enhanced ultrasound (CEUS) could improve the categorization of suspicious breast lesions based on the Breast Imaging Reporting and Data System (BI-RADS), thereby reducing the number of benign breast lesions referred for biopsy. METHODS: This prospective study, conducted between January 2017 and December 2018, enrolled consenting patients from eight teaching hospitals in China, who had been diagnosed with solid breast lesions classified as BI-RADS 4 using conventional ultrasound. CEUS was performed within 1 wk of diagnosis for reclassification of breast lesions. Histopathological results obtained from core needle biopsies or surgical excision samples served as the reference standard. The simulated biopsy rate and cancer-to-biopsy yield were used to compare the accuracy of CEUS and conventional ultrasound (US). RESULTS: Among the 1490 lesions diagnosed as BI-RADS 4 with conventional ultrasound, 486 malignant and 1004 benign lesions were confirmed based on histology. Following CEUS, 2, 395, and 211 lesions were reclassified as CEUS-based BI-RADS 2, 3, and 5, respectively, while 882 (59%) remained as BI-RADS 4. The actual cancer-to-biopsy yield based on US was 32.6%, which increased to 43.4% when CEUS-based BI-RADS 4A was used as the cut-off point to recommend biopsy. The simulated biopsy rate decreased to 73.4%. Overall, in this preselected BI-RADS 4 population, only 2.5% (12/486) of malignant lesions would have been miscategorized as BI-RADS 3 using CEUS-based reclassification. The diagnostic accuracy, sensitivity, and specificity of contrast-enhanced ultrasound reclassification were 57.65%, 97.53%, and 38.35%, respectively. CONCLUSION: Our collective findings indicate that CEUS is a valuable tool in further triage of BI-RADS category 4 lesions and facilitates a reduction in the number of biopsies while increasing the cancer-to-biopsy yield.

Quantitative analysis from ultrafast dynamic contrast-enhanced breast MRI using population-based versus individual arterial input functions, and comparison with semi-quantitative analysis.

PURPOSE: To evaluate the value of inline quantitative analysis of ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a population-based arterial input function (P-AIF) compared with offline quantitative analysis with an individual AIF (I-AIF) and semi-quantitative analysis for diagnosing breast cancer. METHODS: This prospective study included 99 consecutive patients with 109 lesions (85 malignant and 24 benign). Model-based parameters (Ktrans, kep, and ve) and model-free parameters (washin and washout) were derived from CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) DCE-MRI. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. The AUC and F1 score were assessed for semi-quantitative and two quantitative analyses. RESULTS: kep from inline quantitative analysis with P-AIF for diagnosing breast cancer provided an AUC similar to kep from offline quantitative analysis with I-AIF (0.782 vs 0.779, p = 0.954), higher compared to washin from semi-quantitative analysis (0.782 vs 0.630, p = 0.034). Furthermore, the inline quantitative analysis with P-AIF achieved the larger F1 score (0.920) compared with offline quantitative analysis with I-AIF (0.780) and semi-quantitative analysis (0.480). There were no statistically significant differences for kep values between the two quantitative analysis schemes (p = 0.944). CONCLUSION: The inline quantitative analysis with P-AIF from CDTV in characterizing breast lesions could offer similar diagnostic accuracy to offline quantitative analysis with I-AIF, and higher diagnostic accuracy to semi-quantitative analysis.