A Prospective Evaluation of Chemokine Receptor-4 (CXCR4) Overexpression in High-grade Glioma Using Ga-Pentixafor (Pars-Cixafor™) PET/CT Imaging.

Background: While magnetic resonance imaging (MRI) remains the gold standard for morphological imaging, its ability to differentiate between tumor tissue and treatment-induced changes on the cellular level is insufficient. Notably, glioma cells, particularly glioblastoma multiforme (GBM), demonstrate overexpression of chemokine receptor-4 (CXCR4). This study aims to evaluate the feasibility of non-invasive Ga-Cixafor™ PET/CT as a tool to improve diagnostic accuracy in patients with high-grade glioma.

Validation of dosimetry programs (Olinda & IDAC) for evaluation of absorbed dose in LuPSMA therapy of metastatic castration-resistant prostate cancer (mCRPC) using Monte Carlo simulation.

Purpose: Clinical trials have yielded promising results for Lutetium Prostate Specific Membrane Antigen (Lu-PSMA) therapy in metastatic castration resistant prostate cancer (mCRPC) patients. However, the development of precise methods for internal dosimetry and accurate dose estimation has been considered ongoing research. This study aimed to calculate the absorbed dose to the critical organs and metastasis regions using GATE 9.

Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis.

Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically F-FDG PET/CT, hold significant importance in the diagnosis of lymphoma, prognostication, and assessment of treatment response, they still face significant challenges.

Evaluation of the prostate cancer and its metastases in the [ 68 Ga]Ga-PSMA PET/CT images: deep learning method vs. conventional PET/CT processing.

Purpose: This study demonstrates the feasibility and benefits of using a deep learning-based approach for attenuation correction in [ 68 Ga]Ga-PSMA PET scans.

Methods: A dataset of 700 prostate cancer patients (mean age: 67.6 ± 5.

Joint Brain Tumor Segmentation from Multi-magnetic Resonance Sequences through a Deep Convolutional Neural Network.

Background: Brain tumor segmentation is highly contributive in diagnosing and treatment planning. Manual brain tumor delineation is a time-consuming and tedious task and varies depending on the radiologist's skill. Automated brain tumor segmentation is of high importance and does not depend on either inter- or intra-observation.

Contrastive Learning vs. Self-Learning vs. Deformable Data Augmentation in Semantic Segmentation of Medical Images.

To develop a robust segmentation model, encoding the underlying features/structures of the input data is essential to discriminate the target structure from the background. To enrich the extracted feature maps, contrastive learning and self-learning techniques are employed, particularly when the size of the training dataset is limited. In this work, we set out to investigate the impact of contrastive learning and self-learning on the performance of the deep learning-based semantic segmentation.

Innovations in dedicated PET instrumentation: from the operating room to specimen imaging.

This review casts a spotlight on intraoperative positron emission tomography (PET) scanners and the distinctive challenges they confront. Specifically, these systems contend with the necessity of partial coverage geometry, essential for ensuring adequate access to the patient. This inherently leans them towards limited-angle PET imaging, bringing along its array of reconstruction and geometrical sensitivity challenges.

A rare case of carcino-sarcoma of the esophagus with huge intrathoracic metastasis.

Carcinosarcoma of the esophagus constitutes only 0.5%-2.8% of all malignant esophageal cancers.

An Update on the Role of mpMRI and Ga-PSMA PET Imaging in Primary and Recurrent Prostate Cancer.

The objective of this work was to review comparisons of the efficacy of Ga-PSMA-11 (prostate-specific membrane antigen) PET/CT and multiparametric magnetic resonance imaging (mpMRI) in the detection of prostate cancer among patients undergoing initial staging prior to radical prostatectomy or experiencing recurrent prostate cancer, based on histopathological data. A comprehensive search was conducted in PubMed and Web of Science, and relevant articles were analyzed with various parameters, including year of publication, study design, patient count, age, PSA (prostate-specific antigen) value, Gleason score, standardized uptake value (SUV), detection rate, treatment history, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and PI-RADS (prostate imaging reporting and data system) scores. Only studies directly comparing PSMA-PET and mpMRI were considered, while those examining combined accuracy or focusing on either modality alone were excluded.

Deep learning-based partial volume correction in standard and low-dose positron emission tomography-computed tomography imaging.

Background: Positron emission tomography (PET) imaging encounters the obstacle of partial volume effects, arising from its limited intrinsic resolution, giving rise to (I) considerable bias, particularly for structures comparable in size to the point spread function (PSF) of the system; and (II) blurred image edges and blending of textures along the borders. We set out to build a deep learning-based framework for predicting partial volume corrected full-dose (FD + PVC) images from either standard or low-dose (LD) PET images without requiring any anatomical data in order to provide a joint solution for partial volume correction and de-noise LD PET images.

Methods: We trained a modified encoder-decoder U-Net network with standard of care or LD PET images as the input and FD + PVC images by six different PVC methods as the target.

Attention-based deep neural network for partial volume correction in brain F-FDG PET imaging.

Purpose: This work set out to propose an attention-based deep neural network to predict partial volume corrected images from PET data not utilizing anatomical information.

Methods: An attention-based convolutional neural network (ATB-Net) is developed to predict PVE-corrected images in brain PET imaging by concentrating on anatomical areas of the brain. The performance of the deep neural network for performing PVC without using anatomical images was evaluated for two PVC methods, including iterative Yang (IY) and reblurred Van-Cittert (RVC) approaches.

Imaging Patterns in Breast Cancer for Women Under 40 Years: A Descriptive Cohort Study.

Background And Aim: Breast cancer is the most frequently occurring malignant disease in women and remains the leading cause of cancer-related deaths among females worldwide. The aim of this study is to evaluate the imaging findings of breast cancer in women under the age of 40 and analyze their pathological patterns.

Method: A retrospective study was conducted from 2013 to 2019, involving 120 patients below 40 years of age with pathologically confirmed primary epithelial breast cancers.

Strategies for deep learning-based attenuation and scatter correction of brain F-FDG PET images in the image domain.

Background: Attenuation and scatter correction is crucial for quantitative positron emission tomography (PET) imaging. Direct attenuation correction (AC) in the image domain using deep learning approaches has been recently proposed for combined PET/MR and standalone PET modalities lacking transmission scanning devices or anatomical imaging.

Purpose: In this study, different input settings were considered in the model training to investigate deep learning-based AC in the image space.

Hierarchical approach for pulmonary-nodule identification from CT images using YOLO model and a 3D neural network classifier.

This study aimed to assist doctors in detecting early-stage lung cancer. To achieve this, a hierarchical system that can detect nodules in the lungs using computed tomography (CT) images was developed. In the initial phase, a preexisting model (YOLOv5s) was used to detect lung nodules.

The quest for multifunctional and dedicated PET instrumentation with irregular geometries.

We focus on reviewing state-of-the-art developments of dedicated PET scanners with irregular geometries and the potential of different aspects of multifunctional PET imaging. First, we discuss advances in non-conventional PET detector geometries. Then, we present innovative designs of organ-specific dedicated PET scanners for breast, brain, prostate, and cardiac imaging.

Recent Advances in Positron Emission Tomography/Magnetic Resonance Imaging Technology.

More than a decade has passed since the clinical deployment of the first commercial whole-body hybrid PET/MR scanner in the clinic. The major advantages and limitations of this technology have been investigated from technical and medical perspectives. Despite the remarkable advantages associated with hybrid PET/MR imaging, such as reduced radiation dose and fully simultaneous functional and structural imaging, this technology faced major challenges in terms of mutual interference between MRI and PET components, in addition to the complexity of achieving quantitative imaging owing to the intricate MRI-guided attenuation correction in PET/MRI.

Long-term outcomes of the classic Konno-Rastan procedure in paediatric and adult patients: impact of aortic annulus size on patient outcomes.

Objectives: The classic Konno-Rastan procedure may yield different outcomes regarding aortic annulus diameters ≤15 mm and larger. Focusing on the effect of the diameter of the aortic annulus, we described the long-term outcomes of our patients.

Methods: The outcomes of paediatric and adult patients who underwent surgery from 2000 to 2021 were studied retrospectively.

An active learning approach to train a deep learning algorithm for tumor segmentation from brain MR images.

Purpose: This study focuses on assessing the performance of active learning techniques to train a brain MRI glioma segmentation model.

Methods: The publicly available training dataset provided for the 2021 RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge was used in this study, consisting of 1251 multi-institutional, multi-parametric MR images. Post-contrast T1, T2, and T2 FLAIR images as well as ground truth manual segmentation were used as input for the model.

Quantitative Analysis of Image Quality in Low-Dose Computed Tomography Imaging for COVID-19 Patients.

Background: Computed tomography (CT) scan is one of the main tools to diagnose and grade COVID-19 progression. To avoid the side effects of CT imaging, low-dose CT imaging is of crucial importance to reduce population absorbed dose. However, this approach introduces considerable noise levels in CT images.

Does the postoperative quality of reduction, regardless of the surgical method used in treating a calcaneal fracture, influence patients' functional outcomes?

Background: The extensile lateral approach (ELA) and sinus tarsi approach (STA) are commonly utilized for surgically treating calcaneal fractures. This study compared the outcomes of ELA and STA in the management of calcaneal fractures and assessed the influence of postoperative quality of reduction on functional and pain scores.

Methods: The study included 68 adults with Sanders type-II and type-III calcaneal fractures who underwent either ELA or STA surgery.

Assessing the Correlation of Rate of Pathological Complete Response and Outcome in Post Neoadjuvant Chemotherapy Setting and Molecular Subtypes of Breast Cancer.

Background Neoadjuvant chemotherapy (NAC) is being widely used in treating breast cancer (BC). This study aimed to analyze the correlation between clinicopathological features, immunohistochemistry (IHC)-based molecular subtypes, and the pathological response to NAC and its relationship with disease-free survival (DFS) and overall survival (OS). Materials and methods A retrospective analysis of 211 breast cancer patients who received NAC between 2008 and 2018 was performed.

Employing Multiple Low-Dose PET Images (at Different Dose Levels) as Prior Knowledge to Predict Standard-Dose PET Images.

The existing deep learning-based denoising methods predicting standard-dose PET images (S-PET) from the low-dose versions (L-PET) solely rely on a single-dose level of PET images as the input of deep learning network. In this work, we exploited the prior knowledge in the form of multiple low-dose levels of PET images to estimate the S-PET images. To this end, a high-resolution ResNet architecture was utilized to predict S-PET images from 6 to 4% L-PET images.

A cycle-consistent adversarial network for brain PET partial volume correction without prior anatomical information.

Purpose: Partial volume effect (PVE) is a consequence of the limited spatial resolution of PET scanners. PVE can cause the intensity values of a particular voxel to be underestimated or overestimated due to the effect of surrounding tracer uptake. We propose a novel partial volume correction (PVC) technique to overcome the adverse effects of PVE on PET images.