A simplified and accelerated implementation of SVD for filtering ultrafast power Doppler images.

Background And Objective: Ultrafast Power Doppler (UPD) is a growing ultrasound modality for imaging and diagnosing microvasculature disease. A key element of UPD is using singular value decomposition (SVD) as a highly selective filter for tissue and electronic noise. However, two significant drawbacks of SVD are its computational burden and the complexity of its algorithms.

A Channel-Dimensional Feature-Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b-Value Diffusion-Weighted MRI.

Background: Dynamic contrast-enhanced (DCE) MRI commonly outperforms diffusion-weighted (DW) MRI in breast cancer discrimination. However, the side effects of contrast agents limit the use of DCE-MRI, particularly in patients with chronic kidney disease.

Purpose: To develop a novel deep learning model to fully exploit the potential of overall b-value DW-MRI without the need for a contrast agent in predicting breast cancer molecular subtypes and to evaluate its performance in comparison with DCE-MRI.

Early Detection of Underlying Cavernomas in Patients with Spontaneous Acute Intracerebral Hematomas.

Background And Purpose: Early identification of the etiology of spontaneous acute intracerebral hemorrhage is essential for appropriate management. This study aimed to develop an imaging model to identify cavernoma-related hematomas.

Materials And Methods: Patients 1-55 years of age with acute (≤7 days) spontaneous intracerebral hemorrhage were included.

The INFN proton computed tomography system for relative stopping power measurements: calibration and verification.

. This paper describes the procedure to calibrate the three-dimensional (3D) proton stopping power relative to water (SPR) maps measured by the proton computed tomography (pCT) apparatus of the Istituto Nazionale di Fisica Nucleare (INFN, Italy). Measurements performed on water phantoms are used to validate the method.

Vascular hyperintensities on baseline FLAIR images are associated with functional outcome in stroke patients with successful recanalization after mechanical thrombectomy.

Purpose: The purpose of this study was to assess the prognostic value of vascular hyperintensities on FLAIR images (VHF) at admission MRI in patients with acute ischemic stroke (AIS) achieving successful recanalization after mechanical thrombectomy.

Materials And Methods: Patients with AIS treated by mechanical thrombectomy following admission MRI from the single-center HIBISCUS-STROKE cohort were assessed for eligibility. VHF were categorized using a four-scale classification and were considered poor when grade < 3 (i.

Characterization of high-grade prostate cancer at multiparametric MRI using a radiomic-based computer-aided diagnosis system as standalone and second reader.

Purpose: The purpose of this study was to develop and test across various scanners a zone-specific region-of-interest (ROI)-based computer-aided diagnosis system (CAD) aimed at characterizing, on MRI, International Society of Urological Pathology (ISUP) grade≥2 prostate cancers.

Materials And Methods: ROI-based quantitative models were selected in multi-vendor training (265 pre-prostatectomy MRIs) and pre-test (112 pre-biopsy MRIs) datasets. The best peripheral and transition zone models were combined and retrospectively assessed in internal (158 pre-biopsy MRIs) and external (104 pre-biopsy MRIs) test datasets.

Annihilation photon GAN source model for PET Monte Carlo simulation.

Following previous works on virtual sources model with Generative Adversarial Network (GAN), we extend the proof of concept for generating back-to-back pairs of gammas with timing information, typically for Monte Carlo simulation of Positron Emission Tomography(PET) imaging.A conditional GAN is trained once from a low statistic simulation in a given attenuation phantom and enables the generation of various activity source distributions. GAN training input is a set of gammas exiting a phantom, tracked from a source of positron emitters, described by position, direction and energy.

ORRN: An ODE-Based Recursive Registration Network for Deformable Respiratory Motion Estimation With Lung 4DCT Images.

Objective: Deformable Image Registration (DIR) plays a significant role in quantifying deformation in medical data. Recent Deep Learning methods have shown promising accuracy and speedup for registering a pair of medical images. However, in 4D (3D + time) medical data, organ motion, such as respiratory motion and heart beating, can not be effectively modeled by pair-wise methods as they were optimized for image pairs but did not consider the organ motion patterns necessary when considering 4D data.

Simultaneous imaging of ultrasonic relative backscatter and attenuation coefficients for quantitative liver steatosis assessment.

Prevalence of liver disease is continuously increasing and nonalcoholic fatty liver disease (NAFLD) is the most common etiology. We present an approach to detect the progression of liver steatosis based on quantitative ultrasound (QUS) imaging. This study was performed on a group of 55 rats that were subjected to a control or methionine and choline deficient (MCD) diet known to induce NAFLD.

Gadolinium K-edge angiography with a spectral photon counting CT in atherosclerotic rabbits.

Purpose: The purpose of this study was to investigate the feasibility of gadolinium-K-edge-angiography (angio-Gd-K-edge) with gadolinium-based contrast agents (GBCAs) as obtained with spectral photon counting CT (SPCCT) in atherosclerotic rabbits.

Materials And Methods: Seven atherosclerotic rabbits underwent angio-SPCCT acquisitions with two GBCAs, with similar intravenous injection protocol. Conventional and angio-Gd-K-edge images were reconstructed with the same parameters.

Diffusion-Weighted MRI of the Liver in Patients With Chronic Liver Disease: A Comparative Study Between Different Fitting Approaches and Diffusion Models.

Background: Diffusion-weighted imaging (DWI) has been considered for chronic liver disease (CLD) characterization. Grading of liver fibrosis is important for disease management.

Purpose: To investigate the relationship between DWI's parameters and CLD-related features (particularly regarding fibrosis assessment).

A Novel Longitudinal Phenotype-Genotype Association Study Based on Deep Feature Extraction and Hypergraph Models for Alzheimer's Disease.

Traditional image genetics primarily uses linear models to investigate the relationship between brain image data and genetic data for Alzheimer's disease (AD) and does not take into account the dynamic changes in brain phenotype and connectivity data across time between different brain areas. In this work, we proposed a novel method that combined Deep Subspace reconstruction with Hypergraph-Based Temporally-constrained Group Sparse Canonical Correlation Analysis (DS-HBTGSCCA) to discover the deep association between longitudinal phenotypes and genotypes. The proposed method made full use of dynamic high-order correlation between brain regions.

Contour-guided deep learning based deformable image registration for dose monitoring during CBCT-guided radiotherapy of prostate cancer.

Purpose: To evaluate deep learning (DL)-based deformable image registration (DIR) for dose accumulation during radiotherapy of prostate cancer patients.

Methods And Materials: Data including 341 CBCTs (209 daily, 132 weekly) and 23 planning CTs from 23 patients was retrospectively analyzed. Anatomical deformation during treatment was estimated using free-form deformation (FFD) method from Elastix and DL-based VoxelMorph approaches.

Regularization-Based 2D Strain Tensor Imaging in Quasi-Static Ultrasound Elastography .

Accurately estimating all strain components in quasi-static ultrasound elastography is crucial for the full analysis of biological media. In this study, 2D strain tensor imaging was investigated, focusing on the use of a regularization method to improve strain images. This method enforces the tissue property of (quasi-) incompressibility, while penalizing strong field variations, to smooth the displacement fields and reduce the noise in the strain components.

msQSM: Morphology-based self-supervised deep learning for quantitative susceptibility mapping.

Quantitative susceptibility mapping (QSM) has been applied to the measurement of iron deposition and the auxiliary diagnosis of neurodegenerative disease. There still exists a dipole inversion problem in QSM reconstruction. Recently, deep learning approaches have been proposed to resolve this problem.

Stereotactic body radiation therapy in unresectable stage III non-small cell lung cancer: A systematic review.

In unresectable stage III non-small cell lung cancer (NSCLC), the standard of care for most fit patients is concurrent chemotherapy with normofractionated radiotherapy (NFRT), followed by durvalumab consolidation. Nevertheless, almost half of patients will present locoregional or metastatic intrathoracic relapse. Improving locoregional control thus remains an important objective.

Characterization of the INFN proton CT scanner for cross-calibration of x-ray CT.

. The goal of this study was to assess the imaging performances of the pCT system developed in the framework of INFN-funded (Italian National Institute of Nuclear Physics) research projects. The spatial resolution, noise power spectrum (NPS) and RSP accuracy has been investigated, as a preliminary step to implement a new cross-calibration method for x-ray CT (xCT).

Reversibility of Diffusion-Weighted Imaging Lesions in Patients With Ischemic Stroke in the WAKE-UP Trial.

Background: Reversibility of the diffusion-weighted imaging (DWI) lesion means that not all of the DWI lesion represents permanently injured tissue. We investigated DWI reversibility and the association with thrombolysis, reperfusion and functional outcome in patients from the WAKE-UP trial (Efficacy and Safety of Magnetic Resonance Imaging-Based Thrombolysis in Wake-Up Stroke).

Methods: In this retrospective analysis of WAKE-UP, a randomized controlled trial (RCT) between September 2012 and June 2017 in Belgium, Denmark, France, Germany, Spain and United Kingdom, a convolutional neural network segmented the DWI lesions (b=1000 s/mm) at baseline and follow-up (24 hours).

Transarterial chemoembolization followed by moderately hypofractionated radiotherapy in hepatocellular carcinoma: Long-term results of RTF3 regimen.

Purpose: In early-stage hepatocellular carcinoma (HCC) patients merely fit for surgery, transarterial chemoembolization (TACE) achieve low long-term disease control. We evaluated the efficacy and safety of its combination with moderately hypofractionated radiotherapy (hRT) using RTF3 regimen.

Material And Methods: Between 2006 and 2016, 61 consecutive patients treated in our single expert center for a Barcelona Clinic Liver Cancer (BCLC) A HCC by TACE followed by hRT 3Gy/fraction were retrospectively included.