CT imaging, increasing accuracy

PURPOSE: Although microsatellite stability/Epithelial-mesenchymal transition (MSS/EMT) subtypes have been reported in multiple cancer prognosis studies, strong confounding factors between MSS/EMT (usually with Lauren's diffuse phenotype) and diffuse gastric cancer (GC) may obscure the independent prognostic value of diffuse GC. Additionally, recent studies suggest a strong correlation between mural stratification based on CT and diffuse GC. This study aims to investigate potential prognostic factors of MSS diffuse GC using mural stratification and to develop a risk assessment model...

PURPOSE: Multiple clinical visits are necessary to determine progression of keratoconus before offering corneal cross-linking. The purpose of this study was to develop a neural network that can potentially predict progression during the initial visit using tomography images and other clinical risk factors. METHODS: The neural network's development depended on data from 570 keratoconus eyes. During the initial visit, numerical risk factors and posterior elevation maps from Scheimpflug imaging were collected...

BACKGROUND: The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations...

PURPOSE: Bone quality is one of the most important clinical factors for the primary stability and successful osseointegration of dental implants. This preliminary pilot study aimed to evaluate the clinical applicability of deep learning (DL) for assessing bone quality using panoramic (PA) radiographs compared with an implant surgeon's subjective tactile sense and cone-beam computed tomography (CBCT) values. METHODS: In total, PA images of 2,270 edentulous sites for implant placement were selected, and the corresponding CBCT relative gray value measurements and bone quality classification were performed using 3-dimensional dental image analysis software...

Transthyretin cardiac amyloidosis (ATTR CA) is increasingly recognized as a cause of heart failure in older patients, with 99m Tc-pyrophosphate imaging frequently used to establish the diagnosis. Visual interpretation of SPECT images is the gold standard for interpretation but is inherently subjective. Manual quantitation of SPECT myocardial 99m Tc-pyrophosphate activity is time-consuming and not performed clinically. We evaluated a deep learning approach for fully automated volumetric quantitation of 99m Tc-pyrophosphate using segmentation of coregistered anatomic structures from CT attenuation maps...

BACKGROUND: Spinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD. PURPOSE: We present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi-parametric freeze-and-grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts...

INTRODUCTION: Endoleaks represent one of the main complications after endovascular aortic repair (EVAR) and can lead to increased re-intervention rates and secondary rupture. Serial lifelong surveillance is required and traditionally involves cross-sectional imaging with manual axial measurements. Artificial intelligence (AI)-based imaging analysis has been developed and may provide a more precise and faster assessment. This study aims to evaluate the ability of an AI-based software to assess post-EVAR morphological changes over time, detect endoleaks, and associate them with EVAR-related adverse events...

INTRODUCTION: Aneurysmal subarachnoid hemorrhage (SAH) constitutes a life-threatening condition, and identifying the ruptured aneurysm is essential for further therapy. This study aimed to evaluate the diagnostic accuracy of hypo-attenuating berry sign (HBS) observed on computed tomography (CT) scan in distinguishing ruptured aneurysms. METHODS: In this diagnostic accuracy study, patients who had SAH and underwent non-enhanced brain CT scan were recruited. The HBS was defined as a hypo-attenuating area with an identifiable border in the blood-filled hyper-dense subarachnoid space...

BACKGROUND: This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduced radiation doses. This is essential in the context of low-dose CT lung cancer screening where accurate volumetry and characterization of pulmonary nodules in repeated CT scanning are indispensable. MATERIALS AND METHODS: A standardized CT dataset was established using an anthropomorphic chest phantom (Lungman, Kyoto Kaguku Inc...

Osteoporosis is the most common chronic metabolic bone disease worldwide. Vertebral compression fracture (VCF) is the most common type of osteoporotic fracture. Approximately 700,000 osteoporotic VCFs are diagnosed annually in the USA alone, resulting in an annual economic burden of ~$13.8B. With an aging population, the rate of osteoporotic VCFs and their associated burdens are expected to rise. Those burdens include pain, functional impairment, and increased medical expenditure. Therefore, it is of utmost importance to develop an analytical tool to aid in the identification of VCFs...

Background: Concern may exist that pulmonary lesions associated with cystic airspaces are at risk of increased biopsy complications or lower biopsy accuracy given challenges in targeting tissue abutting or intermingled with the cystic airspaces. Objective: To evaluate the safety and diagnostic performance of CT-guided core-needle biopsy (CNB) of pulmonary lesions with cystic airspaces. Methods: This retrospective study included 90 patients (median age, 69.5 years; 28 female, 62 male) who underwent CT-guided CNB of pulmonary lesions associated with cystic airspaces (based on review of procedural images) from February 2010 to December 2022 and a matched control group (2:1 ratio) of 180 patients (median age, 68...

Accurate training labels are a key component for multi-class medical image segmentation. Their annotation is costly and time-consuming because it requires domain expertise. This work aims to develop a dual-branch network and automatically improve training labels for multi-class image segmentation. Transfer learning is used to train the network and improve inaccurate weak labels sequentially. The dual-branch network is first trained by weak labels alone to initialize model parameters. After the network is stabilized, the shared encoder is frozen, and strong and weak decoders are fine-tuned by strong and weak labels together...

BACKGROUND: Preclinical low-count positron emission tomography (LC-PET) imaging offers numerous advantages such as facilitating imaging logistics, enabling longitudinal studies of long- and short-lived isotopes as well as increasing scanner throughput. However, LC-PET is characterized by reduced photon-count levels resulting in low signal-to-noise ratio (SNR), segmentation difficulties, and quantification uncertainties. PURPOSE: We developed and evaluated a novel deep-learning (DL) architecture-Attention based Residual-Dilated Net (ARD-Net)-to generate standard-count PET (SC-PET) images from LC-PET images...

Biopsies play a crucial role in diagnosis of various diseases including cancers. In this study, we developed an augmented reality (AR) system to improve biopsy procedures and increase targeting accuracy. Our AR-guided biopsy system uses a high-speed motion tracking technology and an AR headset to display a holographic representation of the organ, lesions, and other structures of interest superimposed on real physical objects. The first application of our AR system is prostate biopsy. By incorporating preoperative scans, such as computed tomography (CT) or magnetic resonance imaging (MRI), into real-time ultrasound-guided procedures, this innovative AR-guided system enables clinicians to see the lesion as well as the organs in real time...

BACKGROUND: Prebiopsy magnetic resonance imaging (MRI) increases the detection rate of clinically significant prostate cancer (csPCa). Prostate-specific membrane antigen-positron emission tomography/computed tomography (PSMA PET/CT) maximum standardized uptake value (SUVmax) of the prostate may offer additional value in predicting the likelihood of csPCa in biopsy. METHODS: A single-center cohort study involving patients with biopsy-proven PCa who underwent both MRI and PSMA PET/CT between 2020 and 2021...

BACKGROUND: Lumbar disc herniation was regarded as an age-related degenerative disease. Nevertheless, emerging reports highlight a discernible shift, illustrating the prevalence of these conditions among younger individuals. METHODS: This study introduces a novel deep learning methodology tailored for spinal canal segmentation and disease diagnosis, emphasizing image processing techniques that delve into essential image attributes such as gray levels, texture, and statistical structures to refine segmentation accuracy...

Endoscopic transgastric necrosectomy is crucial in the management of complications resulting from necrotizing pancreatitis. However, both real-time and visual-spatial information is lacking during the procedure, thereby jeopardizing a precise positioning of the endoscope. We conducted a proof-of-concept study with the aim of overcoming these technical difficulties. For this purpose, a three-dimensional (3D) phantom of a stomach and pancreatic necroses was 3D-printed based on spatial information from individual patient CT scans and subsequently integrated into a silicone torso...

BACKGROUND: As advancements in surgical instruments and techniques continue to evolve, minimally invasive surgery has become increasingly preferred as a means of reducing patient pain and recovery time. However, one major challenge in performing minimally invasive surgery for early gastrointestinal cancer is accurately identifying the location of the lesion. This is particularly difficult when the lesion is confined to the lumen of the intestine and cannot be visually confirmed from the outside during surgery...

Purpose The increasing use of computed tomography (CT) imaging has led to the detection of more ground-glass nodules (GGNs) and subsolid nodules (SSNs), which may be malignant and require a biopsy for proper diagnosis. Approximately 75% of persistent GGNs can be attributed to adenocarcinoma in situ or minimally invasive adenocarcinoma. A CT-guided biopsy has been proven to be a reliable procedure with high diagnostic performance. However, the diagnostic accuracy and safety of a CT-guided biopsy for GGNs and SSNs with solid components ≤6 mm are still uncertain...

AIM: The comparison between chest x-ray (CXR) and computed tomography (CT) images is commonly required in clinical practice to assess the evolution of chest pathological manifestations. Intrinsic differences between the two techniques, however, limit reader confidence in such a comparison. CT average intensity projection (AIP) reconstruction allows obtaining "synthetic" CXR (s-CXR) images, which are thought to have the potential to increase the accuracy of comparison between CXR and CT imaging...