Investigative Radiology最新文献

Introduction: Atherosclerosis is the underlying cause of multiple cardiovascular pathologies. The present-day clinical imaging modalities do not offer sufficient information on plaque composition or rupture risk. A disintegrin and metalloproteinase with thrombospondin motifs 4 (ADAMTS4) is a strongly upregulated proteoglycan-cleaving enzyme that is specific to cardiovascular diseases, inter alia, atherosclerosis.

Materials and methods: Male apolipoprotein E-deficient mice received a high-fat diet for 2 (n = 11) or 4 months (n = 11). Additionally, a group (n = 11) receiving pravastatin by drinking water for 4 months alongside the high-fat diet was examined. The control group (n = 10) consisted of C57BL/6J mice on standard chow. Molecular magnetic resonance imaging was performed prior to and after administration of the gadolinium (Gd)-based ADAMTS4-specific probe, followed by ex vivo analyses of the aortic arch, brachiocephalic arteries, and carotid arteries. A P value <0.05 was considered to indicate a statistically significant difference.

Results: With advancing atherosclerosis, a significant increase in the contrast-to-noise ratio was measured after intravenous application of the probe (mean precontrast = 2.25; mean postcontrast = 11.47, P < 0.001 in the 4-month group). The pravastatin group presented decreased ADAMTS4 expression. A strong correlation between ADAMTS4 content measured via immunofluorescence staining and an increase in the contrast-to-noise ratio was detected ( R2 = 0.69). Microdissection analysis revealed that ADAMTS4 gene expression in the plaque area was significantly greater than that in the arterial wall of a control mouse ( P < 0.001). Laser ablation-inductively coupled plasma-mass spectrometry confirmed strong colocalization of areas positive for ADAMTS4 and Gd.

Conclusions: Magnetic resonance imaging using an ADAMTS4-specific agent is a promising method for characterizing atherosclerotic plaques and could improve plaque assessment in the diagnosis and treatment of atherosclerosis.

Objectives: Impaired image quality and long scan times frequently occur in respiratory-triggered sequences in liver magnetic resonance imaging (MRI). We evaluated the impact of an in-bore active breathing guidance (BG) application on image quality and scan time of respiratory-triggered T2-weighted (T2) and diffusion-weighted imaging (DWI) by comparing sequences with standard triggering (T2S and DWIS) and with BG (T2BG and DWIBG).

Materials and methods: In this prospective study, random patients with clinical indications for liver MRI underwent 3 T MRI with standard and BG acquisitions. The audiovisual BG application received the respiratory signal from the scanner, and animated breathing instructions were displayed using a mirror and screen behind the MRI bore. Prior to the DWIBG and T2BG acquisition, patients received a short video instruction about MRI with BG. Suitable parameters for desired breathing pattern for T2BG and DWIBG were set individually for each patient based on the patient's physical respiratory ability (ie, 4 seconds breathing followed by 4.5 seconds breath holding). Artifacts, sharpness, lesion conspicuity, and overall image quality were assessed using a Likert scale from 1 (nondiagnostic) to 5 (excellent). Scan time, apparent contrast-to-noise ratio, and apparent signal-to-noise ratio (aSNR) for all sequences were analyzed. Paired t test and Wilcoxon test were used for statistical analysis.

Results: Thirty-two patients (mean age: 55 ± 13 years, 13 female) were included. T2BG showed less artifacts (4.5 ± 0.7 vs 4.1 ± 0.8, P < 0.001) and better sharpness, lesion conspicuity, and overall image quality (eg, overall image quality 4.6 ± 0.7 vs 4.4 ± 0.7, P = 0.004) compared with T2S. DWIBG demonstrated improved image quality in all categories compared with DWIS (eg, overall image quality 4.5 ± 0.5 vs 4.3 ± 0.5, P = 0.005) and less artifacts (4.1 ± 0.5 vs 3.8 ± 0.7, P = 0.007). Scan times of T2BG (286 ± 23 vs 345 ± 68 seconds, P < 0.001) and DWIBG (160 ± 4 vs 252 ± 70 seconds, P < 0.001) were reduced by 17% and 37%, respectively. aSNR and apparent contrast-to-noise ratio (eg, aSNR: 23.45 ± 11.31 [T2BG] vs 25.84 ± 10.76 [T2S]; P = 0.079) were similar for both sequences for both approaches.

Conclusions: Active BG for respiratory-triggered liver T2w and DWI sequences led to significant reduction of breathing artifacts, improved image quality, and shorter scan time compared with standard acquisitions.

Objectives: The aim of this study was to assess the impact of an iterative metal artifact reduction (iMAR) algorithm combined with virtual monoenergetic images (VMIs) for artifact reduction in photon-counting detector computed tomography (PCDCT) during interventions.

Materials and methods: Using an abdominal phantom, we conducted evaluations on the efficacy of iMAR and VMIs for mitigating image artifacts during interventions on a PCDCT. Four different puncture devices were employed under 2 scan modes (QuantumSn at 100 kV, Quantumplus at 140 kV) to simulate various clinical scenarios. Image reconstructions were initially performed without iMAR and subsequently with iMAR settings. The latter was tested with 7 different metal presets for each case. Furthermore, iMAR-reconstructed images were paired with VMIs at energy levels of 70 keV, 110 keV, 150 keV, and 190 keV. Qualitative assessments were conducted to evaluate image quality, artifact expression, and the emergence of new artifacts using a Likert scale. Image quality was rated on a scale of 1 (nondiagnostic) to 5 (excellent), whereas artifact severity was rated from 0 (none) to 5 (massive). Preferences for specific iMAR presets were documented. Quantitative analysis involved calculating Hounsfield unit (HU) differences between artifact-rich and artifact-free tissues.

Results: Overall, 96 different scanning series were evaluated. The optimal combination for artifact reduction was found to be iMAR neurocoils with VMIs at 150 keV and 190 keV, showcasing the most substantial reduction in artifacts with a median rating of 1 (standard: 4). VMIs at higher keV levels, such as 190 keV, resulted in reduced image quality, as indicated by a median rating of 3 (compared with 70 keV with a median of 5). Newly emerged artifact expression related to reconstructions varied among intervention devices, with iMAR thoracic coils exhibiting the least extent of artifacts (median: 2) and iMAR neurocoils displaying the most pronounced artifacts (median: 4). Qualitative analysis favored the combination of iMAR neurocoils with VMIs at 70 keV, showcasing the best results. Conversely, quantitative analysis revealed that the combination of iMAR neurocoils with VMIs at 190 keV yielded the best results, with an average artifact expression of 20.06 HU (standard: 167.98 HU; P < 0.0001).

Conclusions: The study underscores a substantial reduction in artifacts associated with intervention devices during PCDCT scans through the synergistic application of VMI and iMAR techniques. Specifically, the combination of VMIs at 70 keV with iMAR neurocoils was preferred, leading to enhanced diagnostic assessability of surrounding tissues and target lesions. The study demonstrates the potential of iMAR and VMIs for PCDCT-guided interventions. These advancements could improve accuracy, safety, efficiency, and patient outcomes in clinical practice.

Abstract: Local ablation therapy, encompassing radiofrequency ablation (RFA), microwave ablation, and cryoablation, has emerged as a crucial strategy for managing small hepatocellular carcinomas (HCCs), complementing liver resection and transplantation. This review delves into the clinical significance of tumor size, location, and biology in guiding treatment decisions for HCCs undergoing local ablation therapy, with a focus on tumors smaller than 3 cm. Tumor size significantly influences treatment outcomes, with larger tumors associated with poorer local tumor control due to challenges in creating sufficient ablative margins and the likelihood of microvascular invasion and peritumoral satellite nodules. Advanced ablation techniques such as centripetal or no-touch RFA using multiple electrodes, cryoablation using multiple cryoprobes, and microwave ablation offer diverse options for HCC treatment. Notably, no-touch RFA demonstrates superior local tumor control compared with conventional RFA by achieving sufficient ablative margins, making it particularly promising for hepatic dome lesions or tumors with aggressive biology. Laparoscopic RFA proves beneficial for treating anterior subphrenic HCCs, whereas artificial pleural effusion-assisted RFA is effective for controlling posterior subphrenic HCCs. However, surgical resection generally offers better survival outcomes for periportal HCCs compared with RFA. Cryoablation exhibits a lower incidence of vascular or biliary complications than RFA for HCCs adjacent to perivascular or periductal regions. Additionally, aggressive tumor biology, such as microvascular invasion, can be predicted using magnetic resonance imaging findings and serum tumor markers. Aggressive HCC subtypes frequently exhibit Liver Imaging Reporting and Data System M features on magnetic resonance imaging, aiding in prognosis. A comprehensive understanding of tumor size, location, and biology is imperative for optimizing the benefits of local ablation therapy in managing HCCs.

Abstract: Bladder cancer (BC) is a significant global health concern, with over 500,000 new cases and 200,000 deaths annually, emphasizing the need for accurate staging and effective management. Traditional diagnostic techniques, such as cystoscopy and transurethral resection, are fundamental but have limitations in accurately assessing the depth of invasion. These limitations include the possibility of understaging and procedural variability, which can significantly impact treatment decisions. This review focuses on the role of multiparametric magnetic resonance imaging (mpMRI) in the diagnosis and staging of BC, particularly emphasizing the Vesical Imaging-Reporting and Data System (VI-RADS) framework. By enhancing interpretive consistency and diagnostic accuracy, mpMRI and VI-RADS offer detailed visualization of tumor characteristics and depth of invasion, while reducing the need for more invasive traditional methods. These advancements not only improve staging accuracy but also enhance treatment planning, underscoring the importance of advanced imaging in evolving BC management and positively influencing patient outcomes.

Abstract: In children and adults, quantitative imaging examinations determine the effectiveness of treatment for liver disease. However, pediatric liver disease differs in presentation from liver disease in adults. Children also needed to be followed for a longer period from onset and have less control of their bodies, showing more movement than adults during imaging examinations, which leads to a greater need for sedation. Thus, it is essential to appropriately tailor and accurately perform noninvasive imaging tests in these younger patients. This article is an overview of updated imaging techniques used to assess liver disease quantitatively in children. The common initial imaging study for diffuse liver disease in pediatric patients is ultrasound. In addition to preexisting echo analysis, newly developed attenuation imaging techniques have been introduced to evaluate fatty liver. Ultrasound elastography is also now actively used to evaluate liver conditions, and the broad age spectrum of the pediatric population requires caution to be taken even in the selection of probes. Magnetic resonance imaging (MRI) is another important imaging tool used to evaluate liver disease despite requiring sedation or anesthesia in young children because it allows quantitative analysis with sequences such as fat analysis and MR elastography. In addition to ultrasound and MRI, we review quantitative imaging methods specifically for fatty liver, Wilson disease, biliary atresia, hepatic fibrosis, Fontan-associated liver disease, autoimmune hepatitis, sinusoidal obstruction syndrome, and the transplanted liver. Lastly, concerns such as growth and motion that need to be addressed specifically for children are summarized.

Abstract: Recent technological advancements have revolutionized routine brain magnetic resonance imaging (MRI) sequences, offering enhanced diagnostic capabilities in intracranial disease evaluation. This review explores 2 pivotal breakthrough areas: deep learning reconstruction (DLR) and quantitative MRI techniques beyond conventional structural imaging. DLR using deep neural networks facilitates accelerated imaging with improved signal-to-noise ratio and spatial resolution, enhancing image quality with short scan times. DLR focuses on supervised learning applied to clinical implementation and applications. Quantitative MRI techniques, exemplified by 2D multidynamic multiecho, 3D quantification using interleaved Look-Locker acquisition sequences with T2 preparation pulses, and magnetic resonance fingerprinting, enable precise calculation of brain-tissue parameters and further advance diagnostic accuracy and efficiency. Potential DLR instabilities and quantification and bias limitations will be discussed. This review underscores the synergistic potential of DLR and quantitative MRI, offering prospects for improved brain imaging beyond conventional methods.

Abstract: Immunotherapy is likely the most remarkable advancement in lung cancer treatment during the past decade. Although immunotherapy provides substantial benefits, their therapeutic responses differ from those of conventional chemotherapy and targeted therapy, and some patients present unique immunotherapy response patterns that cannot be judged under the current measurement standards. Therefore, the response monitoring of immunotherapy can be challenging, such as the differentiation between real response and pseudo-response. This review outlines the various tumor response patterns to immunotherapy and discusses methods for quantifying computed tomography (CT) and 18 F-fluorodeoxyglucose positron emission tomography (PET) in the field of lung cancer. Emerging technologies in magnetic resonance imaging (MRI) and non-FDG PET tracers are also explored. With immunotherapy responses, the role for imaging is essential in both anatomical radiological responses (CT/MRI) and molecular changes (PET imaging). Multiple aspects must be considered when assessing treatment responses using CT and PET. Finally, we introduce multimodal approaches that integrate imaging and nonimaging data, and we discuss future directions for the assessment and prediction of lung cancer responses to immunotherapy.

Abstract: Interstitial lung disease (ILD) encompasses a variety of lung disorders with varying degrees of inflammation or fibrosis, requiring a combination of clinical, imaging, and pathologic data for evaluation. Imaging is essential for the noninvasive diagnosis of the disease, as well as for assessing disease severity, monitoring its progression, and evaluating treatment response. However, traditional visual assessments of ILD with computed tomography (CT) suffer from reader variability. Automated quantitative CT offers a more objective approach by using computer-based analysis to consistently evaluate and measure ILD. Advancements in technology have significantly improved the accuracy and reliability of these measurements. Recently, interstitial lung abnormalities (ILAs), which represent potential preclinical ILD incidentally found on CT scans and are characterized by abnormalities in over 5% of any lung zone, have gained attention and clinical importance. The challenge lies in the accurate and consistent identification of ILA, given that its definition relies on a subjective threshold, making quantitative tools crucial for precise ILA evaluation. This review highlights the state of CT quantification of ILD and ILA, addressing clinical and research disparities while emphasizing how machine learning or deep learning in quantitative imaging can improve diagnosis and management by providing more accurate assessments, and finally, suggests the future directions of quantitative CT in this area.

Abstract: Artificial intelligence (AI) has made significant advances in radiology. Nonetheless, challenges in AI development, validation, and reproducibility persist, primarily due to the lack of high-quality, large-scale, standardized data across the world. Addressing these challenges requires comprehensive standardization of medical imaging data and seamless integration with structured medical data.Developed by the Observational Health Data Sciences and Informatics community, the OMOP Common Data Model enables large-scale international collaborations with structured medical data. It ensures syntactic and semantic interoperability, while supporting the privacy-protected distribution of research across borders. The recently proposed Medical Imaging Common Data Model is designed to encompass all DICOM-formatted medical imaging data and integrate imaging-derived features with clinical data, ensuring their provenance.The harmonization of medical imaging data and its seamless integration with structured clinical data at a global scale will pave the way for advanced AI research in radiology. This standardization will enable federated learning, ensuring privacy-preserving collaboration across institutions and promoting equitable AI through the inclusion of diverse patient populations. Moreover, it will facilitate the development of foundation models trained on large-scale, multimodal datasets, serving as powerful starting points for specialized AI applications. Objective and transparent algorithm validation on a standardized data infrastructure will enhance reproducibility and interoperability of AI systems, driving innovation and reliability in clinical applications.