Frontiers in radiology最新文献

Recent advancements in artificial intelligence (AI) and machine learning offer numerous opportunities in musculoskeletal radiology to potentially bolster diagnostic accuracy, workflow efficiency, and predictive modeling. AI tools have the capability to assist radiologists in many tasks ranging from image segmentation, lesion detection, and more. In bone and soft tissue tumor imaging, radiomics and deep learning show promise for malignancy stratification, grading, prognostication, and treatment planning. However, challenges such as standardization, data integration, and ethical concerns regarding patient data need to be addressed ahead of clinical translation. In the realm of musculoskeletal oncology, AI also faces obstacles in robust algorithm development due to limited disease incidence. While many initiatives aim to develop multitasking AI systems, multidisciplinary collaboration is crucial for successful AI integration into clinical practice. Robust approaches addressing challenges and embodying ethical practices are warranted to fully realize AI's potential for enhancing diagnostic accuracy and advancing patient care.

Sinonasal tumors are often malignant and comprise approximately 3% of all head and neck malignancies. Half of these tumors arise in the nasal cavity, and other common locations of origin include the ethmoid and maxillary sinuses. Some unique clinical features are anosmia and altered phonation but the most common general features include headache, epistaxis, and diplopia. CT and MRI may be used to assess tumor location, invasion of adjacent tissue, presence of metastasis, internal tumor heterogeneity, and contrast enhancement. Local invasion of the tumor beyond the sinonasal tract can impact adjacent structures such as the cranial nerves, skull base, branches of the internal carotid artery, and orbit leading to neurologic signs, facial pain, and diplopia. Imaging is used in the diagnosis, staging, and treatment planning of sinonasal tumors. This collection of benign and malignant sinonasal tumors will include some rare and unique cases with an emphasis on imaging features demonstrating a wide variety of pathologies.

Introduction: The reconstruction of PET images involves converting sinograms, which represent the measured counts of radioactive emissions using detector rings encircling the patient, into meaningful images. However, the quality of PET data acquisition is impacted by physical factors, photon count statistics and detector characteristics, which affect the signal-to-noise ratio, resolution and quantitative accuracy of the resulting images. To address these influences, correction methods have been developed to mitigate each of these issues separately. Recently, generative adversarial networks (GANs) based on machine learning have shown promise in learning the complex mapping between acquired PET data and reconstructed tomographic images. This study aims to investigate the properties of training images that contribute to GAN performance when non-clinical images are used for training. Additionally, we describe a method to correct common PET imaging artefacts without relying on patient-specific anatomical images.

Methods: The modular GAN framework includes two GANs. Module 1, resembling Pix2pix architecture, is trained on non-clinical sinogram-image pairs. Training data are optimised by considering image properties defined by metrics. The second module utilises adaptive instance normalisation and style embedding to enhance the quality of images from Module 1. Additional perceptual and patch-based loss functions are employed in training both modules. The performance of the new framework was compared with that of existing methods, (filtered backprojection (FBP) and ordered subset expectation maximisation (OSEM) without and with point spread function (OSEM-PSF)) with respect to correction for attenuation, patient motion and noise in simulated, NEMA phantom and human imaging data. Evaluation metrics included structural similarity (SSIM), peak-signal-to-noise ratio (PSNR), relative root mean squared error (rRMSE) for simulated data, and contrast-to-noise ratio (CNR) for NEMA phantom and human data.

Results: For simulated test data, the performance of the proposed framework was both qualitatively and quantitatively superior to that of FBP and OSEM. In the presence of noise, Module 1 generated images with a SSIM of 0.48 and higher. These images exhibited coarse structures that were subsequently refined by Module 2, yielding images with an SSIM higher than 0.71 (at least 22% higher than OSEM). The proposed method was robust against noise and motion. For NEMA phantoms, it achieved higher CNR values than OSEM. For human images, the CNR in brain regions was significantly higher than that of FBP and OSEM (p < 0.05, paired t-test). The CNR of images reconstructed with OSEM-PSF was similar to those reconstructed using the proposed method.

Conclusion: The proposed image reconstruction method can produce PET images with artefact correction.

Background: Cardiac magnetic resonance is a useful clinical tool to identify late gadolinium enhancement in heart failure patients with implantable electronic devices. Identification of LGE in patients with CIED is limited by artifact, which can be improved with a wide band radiofrequency pulse sequence.

Objective: The authors hypothesize that image quality of LGE images produced using wide-band pulse sequence in patients with devices is comparable to image quality produced using standard LGE sequences in patients without devices.

Methods: Two independent readers reviewed LGE images of 16 patients with CIED and 7 patients without intracardiac devices to assess for image quality, device-related artifact, and presence of LGE using the American Society of Echocardiography/American Heart Association 17 segment model of the heart on a 4-point Likert scale. The mean and standard deviation for image quality and artifact rating were determined. Inter-observer reliability was determined by calculating Cohen's kappa coefficient. Statistical significance was determined by T-test as a p {less than or equal to} 0.05 with a 95% confidence interval.

Results: All patients underwent CMR without any adverse events. Overall IQ of WB LGE images was significantly better in patients with devices compared to standard LGE in patients without devices (p = 0.001) with reduction in overall artifact rating (p = 0.05).

Conclusion: Our study suggests wide-band pulse sequence for LGE can be applied safely to heart failure patients with devices in detection of LV myocardial scar while maintaining image quality, reducing artifact, and following routine imaging protocol after intravenous gadolinium contrast administration.

Purpose: The study aimed to (1) assess the feasibility constrained spherical deconvolution (CSD) tractography to reconstruct crossing fiber bundles with unsedated neonatal diffusion MRI (dMRI), and (2) demonstrate the impact of spatial and angular resolution and processing settings on tractography and derived quantitative measures.

Methods: For the purpose of this study, the term-equivalent dMRIs (single-shell b800, and b2000, both 5 b0, and 45 gradient directions) of two moderate-late preterm infants (with and without motion artifacts) from a local cohort [Brain Imaging in Moderate-late Preterm infants (BIMP) study; Calgary, Canada] and one infant from the developing human connectome project with high-quality dMRI (using the b2600 shell, comprising 20 b0 and 128 gradient directions, from the multi-shell dataset) were selected. Diffusion tensor imaging (DTI) and CSD tractography were compared on b800 and b2000 dMRI. Varying image resolution modifications, (pre-)processing and tractography settings were tested to assess their impact on tractography. Each experiment involved visualizing local modeling and tractography for the corpus callosum and corticospinal tracts, and assessment of morphological and diffusion measures.

Results: Contrary to DTI, CSD enabled reconstruction of crossing fibers. Tractography was susceptible to image resolution, (pre-) processing and tractography settings. In addition to visual variations, settings were found to affect streamline count, length, and diffusion measures (fractional anisotropy and mean diffusivity). Diffusion measures exhibited variations of up to 23%.

Conclusion: Reconstruction of crossing fiber bundles using CSD tractography with unsedated neonatal dMRI data is feasible. Tractography settings affected streamline reconstruction, warranting careful documentation of methods for reproducibility and comparison of cohorts.

Aortofemoral bypass surgery is a common procedure for treating aortoiliac occlusive disease, also known as Leriche syndrome, which can cause lower extremity ischemic symptoms. Diagnostic imaging techniques play a crucial role in managing pseudoaneurysms (PSAs), with Duplex ultrasound and Computed Tomography-angiography (CTA) being effective tools for early diagnosis. Pseudoaneurysms (PSAs) present as pulsating masses with various symptoms, and prompt intervention is essential to avoid complications. A case report is presented involving an 82-year-old male who underwent aorto-bifemoral bypass surgery and later developed a pseudoaneurysm (PSA) of the left branch. Surgical treatment involved the removal of the pseudoaneurysm (PSA) and graft replacement. Other cases from the literature are also described, emphasizing the rarity and potential severity of non-anastomotic pseudoaneurysms (PSAs) in reconstructive vascular surgery. Periodic screening of patients who undergo reconstructive vascular surgery is crucial to detect pseudoaneurysms (PSAs) early and prevent complications. Asymptomatic pseudoaneurysms (PSAs) can grow significantly and become life-threatening if not identified in a timely manner. Regular post-operative imaging, such as annual Computed Tomography-angiography (CTA) and/or Duplex ultrasound, is recommended to ensure early diagnosis and appropriate management of complications.