Physics in Medicine and Biology

Pancreatic cancer is one of the most malignant tumours, demonstrating a poor prognosis and nearly identically high mortality and morbidity, mainly because of the difficulty of early diagnosis and timely treatment for localized stages. It is clinically important to develop a noncontrast CT (NCCT)-based pancreatic lesion detection model that could serve as an intelligent tool for diagnosing pancreatic cancer early. However, abdominal NCCT presents low contrast intensities for the pancreas and its lesions and complex anatomical structures, developing such a model is challenging...

The x-ray radiation dose in computed tomography (CT) examination has been a major concern for patients. Lowing the tube current and exposure time in data acquisition is a straightforward and cost-effective strategy to reduce the x-ray radiation dose. However, this will inevitably increase the noise fluctuations in measured projection data, and the corresponding CT image quality will be severely degraded if noise suppression is not performed during image reconstruction. To reconstruct high-quality low-dose CT image, we present a spatial-radon domain total generalized variation (SRDTGV) regularization for statistical iterative reconstruction (SIR) based on penalized weighted least-squares (PWLS) principle, which is called PWLS-SRDTGV for simplicity...

A novel X-ray field produced by an ultrathin conical target is described in the literature. However, the optimal design for an associated collimator remains ambiguous. Current optimization methods using Monte Carlo calculations restrict the efficiency and robustness of the design process. A more generic optimization method that reduces parameter constraints while minimizing computational load is necessary. A numerical method for optimizing the longitudinal collimator hole geometry for a cylindrically-symmetrical X-ray tube is demonstrated and compared to Monte Carlo calculations...

Objective 
Radio-opaque markers are recommended for image-guided radiotherapy in liver stereotactic ablative radiotherapy (SABR), but their implantation is invasive. We evaluate in this in-silico study the feasibility of cone-beam computed tomography-guided stereotactic online-adaptive radiotherapy (CBCT-STAR) to propagate the target volumes without implanting radio-opaque markers and assess its consequence on the margin that should be used in that context. 
Approach 
An emulator of a CBCT-STAR-dedicated treatment planning system was used to generate plans for 32 liver SABR patients...

OBJECTIVE: To evaluate the reduction in energy dependence and aging effect of the lithium salt of pentacosa-10,-12-diynoic acid (LiPCDA) films with additives including aluminum oxide (Al2O3), propyl gallate (PG), and disodium ethylenediaminetetracetate (EDTA). APPROACH: LiPCDA films exhibited energy dependence on kilovoltage (kV) and megavoltage (MV) photon energies and experienced deterioration over time. Evaluations were conducted with added Al₂O₃ and antioxidants to mitigate these issues, and films were produced with and without Al₂O₃ to assess energy dependence...

Conventional transarterial chemoembolization (cTACE) is a common treatment for hepatocellular carcinoma (HCC), often with unsatisfactory local controls. Combining cTACE with radiotherapy shows a promise for unresectable large HCC, with proton therapy preserving healthy liver tissue. However, the proton therapy benefits are subject to the accuracy of tissue relative stopping power (RSP) prediction. The RSP values are typically derived from computed tomography (CT) images using stoichiometric calibration. Lipiodol deposition significantly increases CT numbers in liver regions of post-cTACE...

Automated assessment of noise level in clinical CT images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical CT scans due to the fluctuation of x-ray quanta. Initially, a noise map is generated by sliding a 10x10 pixel for calculating HU standard deviation (SD) and the noise map is further combined with the gradient magnitude map...

Predicting the probability of having the plan approved by the physician is important for automatic treatment planning. Driven by the mathematical foundation of deep learning that can use a deep neural network to represent functions accurately and flexibly, we developed a deep-learning framework that learns the probability of plan approval for cervical cancer high-dose-rate brachytherapy (HDRBT).
Approach: The system consisted of a dose prediction network (DPN) and a plan-approval probability network (PPN)...

A Monte Carlo virtual source model that generates photons emitted in the complex decay chain process of alpha-emitter radionuclides is proposed, typically for use during simulation of SPECT image acquisition. 
Given an alpha-emitter radionuclide, the model extracts from Geant4 databases the photon emission lines from all decaying daughters for both isometric transition and atomic relaxation processes. According to a given time range, abundances and activities in the decay chain are considered thanks to the Bateman equations, taking into account the decay rates and the initial abundances...

Objective Up to this point, 1.5T linac-compatible coil array layouts have been restricted to one or two rows of coils because of the desire to place radiation-opaque circuitry adjacent to the coils and outside the radiolucent window through which the linac beam travels. Such layouts can limit parallel imaging performance. The purpose of this work was to design and build a three-row array in which remotely located circuits permitted a central row of coils while preserving the radiolucent window.
 Approach The remote circuits consisted of a phase shifter to cancel the phase introduced by the coaxial link between the circuit and coil, followed by standard components for tuning, matching, detuning, and preamplifier decoupling...

Personalized dose monitoring and risk management are of increasing significance with the growing number of computer tomography (CT) examinations. These require high-quality Monte Carlo (MC) simulations that are of the utmost importance for the new developments in personalized CT dosimetry. 
This work aims to extend the MC framework EGSnrc source code with a new particle source. This, in turn, allows CT-scanner-specific dose and image calculations for any CT scanner. The novel method can be used with all modern EGSnrc user codes, particularly for the simulation of the effective dose based on DICOM images and the calculation of CT images...

Cardiac magnetic resonance imaging (MRI) usually requires a long acquisition time. The movement of the patients during MRI acquisition will produce image artifacts. Previous studies have shown that clear MR image texture edges are of great significance for pathological diagnosis. In this paper, a motion artifact reduction method for cardiac MRI based on edge enhancement network is proposed. Firstly, the four-plane normal vector adaptive fractional differential mask is applied to extract the edge features of blurred images...

The in vivo evolution of radiotherapy necessitates innovative platforms for preclinical investigation, bridging the gap between bench research and clinical applications. Understanding the nuances of radiation response, specifically tailored to proton and photon therapies, is critical for optimizing treatment outcomes. Within this context, preclinical in vivo experimental setups incorporating image guidance for both photon and proton therapies are pivotal, enabling the translation of findings from small animal models to clinical settings...

OBJECTIVE: Accurate assessment of pleural line is crucial for the application of lung ultrasound (LUS) in monitoring lung diseases, thereby aim of this study is to develop a quantitative and qualitative analysis method for pleural line. APPROACH: The novel cascaded deep learning models based on convolution and multilayer perceptron was proposed to locate and segment the pleural line in LUS images, whose results were applied for quantitative analysis of textural and morphological features, respectively...


To use automation to facilitate the monitoring of each treatment fraction using an EPID based in-vivo dosimetry (IVD) system, allowing optimisation of breast radiotherapy delivery for individual patients and cohorts.

Approach:
A suite of in-house software was developed to reduce the number of manual interactions with the commercial IVD system, Dosimetry Check. An EPID specific pixel sensitivity map facilitated use of the EPID panel away from the central axis. Point dose difference and the change in standard deviation in dose were identified as useful dose metrics, with standard deviation used in preference to gamma in the presence of a systematic dose offset...

OBJECTIVE: Dynamic contrast-enhanced MRI (DCE-MRI) is a sensitive tool for assessing breast cancer by analyzing tumor blood flow, but it requires gadolinium-based contrast agents, which carry risks such as brain retention and astrocyte migration. Contrast-free MRI is thus preferable for patients with renal impairment or who are pregnant. This study aimed to investigate the feasibility of generating contrast-enhanced MR images from precontrast images and to evaluate the potential use of synthetic images in diagnosing breast cancer...

This review paper aims to serve as a comprehensive guide and instructional resource for researchers seeking to effectively implement language models in medical imaging research. First, we presented the fundamental principles and evolution of language models, dedicating particular attention to large language models. We then reviewed the current literature on how language models are being used to improve medical imaging, emphasizing a range of applications such as image captioning, report generation, report classification, findings extraction, visual question response systems, interpretable diagnosis and so on...

To optimize and ensure the safety of ultrasound brain therapy, personalized transcranial ultrasound simulations are very useful. They allow to predict the pressure field, depending on the patient skull and probe position. Most transcranial ultrasound simulations are based on numerical methods which have a long computation time and a high memory usage. The goal of this study is to develop a new semi-analytical field computation method that combines realism and computation speed.

Instead of the classic ray tracing, the ultrasonic paths are computed by time of flight minimization...

Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatment, and lead to higher radiation delivery. 
Approach: In this study, we propose a deep synthesis method to generate pseudo respiratory CT phases from static images for motion-aware treatment planning...

Accurate segmentation of different regions of gliomas from multimodal magnetic resonance (MR) images is crucial for glioma grading and precise diagnosis, but many existing segmentation methods are difficult to effectively utilize multimodal MR image information to recognize accurately the lesion regions with small size, low contrast and irregular shape. To address this issue, this work proposes a novel 3D glioma segmentation model DCL-MANet. DCL-MANet has an architecture of multiple encoders and one single decoder...