Quantitative Imaging in Medicine and Surgery最新文献

Background: Computed tomography (CT) and magnetic resonance imaging (MRI) are essential in clinical diagnosis and treatment planning, but their images are often compromised by limited contrast and insufficient detail, reducing diagnostic clarity. Traditional enhancement methods-such as histogram equalization (HE) can improve visibility but may introduce noise, over-enhancement, or structural distortion. Quantum-inspired computational techniques have recently emerged as promising tools for nonlinear and adaptive image processing. Building on the quantum signal processing (QSP) framework, this study proposes a quantum-inspired enhancement (QIE) algorithm designed to improve medical image contrast while preserving structural details.

Methods: We propose a QIE algorithm that embeds a three-pixel quantum-correlation system within a QSP framework. After normalizing grayscale values, each 3×3 neighborhood is mapped to superposition states; edge-sensitive basis states are selectively accumulated in four orientations to produce the enhanced output. The algorithm was evaluated using T2-weighted magnetic resonance (MR) brain images and CT lung images obtained from 10 different patients. Its performance was compared with four representative classical enhancement methods: HE, contrast-limited adaptive HE (CLAHE), fuzzy HE (FHE), and wavelet-based enhancement (WBE), employing quantitative metrics such as entropy, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and contrast-to-noise ratio (CNR). Paired two-sided t-tests (α=0.05) were used.

Results: QIE reached the highest mean entropy on both datasets (CT: 4.37±0.31; MR: 6.45±0.16) vs. HE 4.00±0.25 (P=2.8×10^-4) and 5.67±0.16 (P=2.3×10^-7) respectively, indicating superior information retention and detail enhancement. Its PSNR and SSIM were significantly better than HE, FHE, and WBE (all P<0.01), reflecting better signal fidelity and structural preservation; vs. CLAHE, QIE PSNR was -3.4 dB lower on CT and -3.3 dB lower on MR (both P<0.001), but SSIM differed by <0.001 (P≥0.13). CNR with QIE (CT: 4.00±3.54; MR: 3.66±2.81) was not statistically different from any method (P≥0.05).

Conclusions: The proposed QIE algorithm demonstrates superior performance in enhancing the contrast and preserving the structural details of medical images. By leveraging quantum-inspired mechanisms, the algorithm shows potential for improving diagnostic accuracy and supporting clinical treatment planning. Future work will explore the application of this algorithm to other imaging modalities, investigate its effectiveness as a preprocessing step for commercial artificial intelligence (AI) models, and study the integration with actual quantum computing platforms.

Background: Glutaric acidemia type 1 (GA-1) is a rare autosomal recessive metabolic disorder resulting from a deficiency in glutaryl-CoA dehydrogenase (GCDH). Current evidence indicates that GA-1 remains under-recognized by clinicians, a factor that may contribute to delayed diagnosis. The aim of this study was to retrospectively analyze the clinical manifestations and imaging characteristics of GA-1.

Methods: This study enrolled patients diagnosed with GA-1 at the Guangzhou Women and Children's Medical Center between April 2014 and April 2024. Clinical data related to GA-1 were retrieved through the electronic medical record system, and magnetic resonance imaging (MRI) scans were collected for all patients. Cranial MRI images were independently evaluated by two radiologists (with 10 and 6 years of experience in pediatric neuroimaging diagnosis, respectively) using a blinded approach. Blood acylcarnitine levels were analyzed using tandem mass spectrometry, urinary organic acid concentrations were quantified via gas chromatography-mass spectrometry, and GCDH gene analysis was performed in a subset of patients.

Results: This study enrolled 24 GA-1 children (8 males, 16 females) from Guangdong Province, China. Diagnosis was confirmed by elevated glutaric acid (GA), 3-hydroxyglutaric acid (3-HGA), and glutarylcarnitine (C5DC) levels, with increased C5DC/octanoylcarnitine (C8) and C5DC/propionylcarnitine (C3) ratios. Genetic analysis identified 12 GCDH mutations in 11 patients, including 5 novel variants (c.395G>A, c.271+1G>A, c.1156C>G, c.146_149delACTG, and c.1011A>G). Neuroimaging revealed abnormal brain MRI findings in all patients (100%), predominantly featuring frontotemporal extracerebral space widening (75.0%, 18/24) and symmetric basal ganglia hyperintensity (83.3%, 20/24). These findings align with the established GA-1 phenotypes.

Conclusions: This study underscores the need for heightened awareness of GA-1 among clinicians and radiologists, characterizes its MRI signature, and expands the GCDH mutation spectrum with five novel variants, thereby offering valuable guidance for imaging-based diagnosis and genetic counselling.

Background: Extramural venous invasion (EMVI), tumor deposits (TDs), and peripheral nerve invasion (PNI) are high-risk pathological features in patients with rectal cancer (RC), but their preoperative assessment with conventional computed tomography (CT) is limited. This study aimed to evaluate the value of dual-energy computed tomography (DECT)-based fractal analysis for predicting EMVI/TD and PNI in patients with RC.

Methods: A total of 130 patients with histologically confirmed RC who underwent preoperative DECT were consecutively enrolled and divided into a retrospective development cohort (n=85) and a prospective validation cohort (n=45). Fractal dimensions (FDs) from conventional mixed-energy images (FD-Con), iodine maps [FD-iodine concentration (FD-IC)], and effective atomic number (Zeff) maps (FD-Zeff) were calculated. Patients were classified as PNI-positive/-negative and EMVI/TD-positive/-negative according to surgical pathology. Group comparisons, receiver operating characteristic (ROC) curve analysis, and multivariable logistic regression were used to identify independent predictors and build combined models, which were tested in the validation cohort.

Results: Among the 130 patients, 59 were PNI-positive and 35 were EMVI/TD-positive. FD-IC and FD-Zeff were significantly higher in positive than in negative groups for both PNI and EMVI/TD (all P values ≤0.019). In the validation cohort, FD-IC and FD-Zeff achieved areas under the ROC curves (AUCs) of 0.752 and 0.771 for predicting PNI and 0.795 and 0.855 for predicting EMVI/TD, respectively. FD-IC was an independent risk factor for PNI [odds ratio (OR) =77.873; P=0.039], and FD-Zeff was an independent predictor of EMVI/TD (OR =1.109×10⁴; P=0.006). In the validation cohort, the combined model yielded AUCs of 0.771 and 0.879 for PNI and EMVI/TD, respectively.

Conclusions: DECT-based fractal analysis, particularly FD-IC and FD-Zeff, provides quantitative markers for preoperative prediction of PNI and EMVI/TD in patients with RC and improves diagnostic performance as compared with conventional CT-based fractal parameters.

Background: The prognostic value of left atrial (LA) parameters in patients with dilated cardiomyopathy (DCM) remains controversial. We aimed to assess the prognostic value of LA structural and functional parameters derived from cardiac magnetic resonance (CMR) in patients with DCM.

Methods: The PubMed, Web of Science, Medline, Embase, and Cochrane Library databases were systematically searched to retrieve original studies on the prognosis of patients with DCM undergoing LA assessment by CMR. The search period spanned from database establishment to December 12, 2024. The outcome was a composite of adverse cardiovascular events. Risk of bias was assessed using the Quality In Prognosis Studies (QUIPS) tool. The level of evidence was assessed based on meta-analyses of the hazard ratios (HRs) and 95% confidence intervals (CIs) of LA structure and function parameters for predicting adverse cardiovascular events.

Results: A total of 13 studies comprising 4,326 patients were included in meta-analysis. The meta-analysis results showed that the left atrial maximum volume index (LAVImax), left atrial minimum volume index (LAVImin), and LA strain parameters could be used to predict adverse cardiovascular events in DCM. Additionally, the left atrial reservoir strain (LARS) (HR: 0.93 per 1% increase; 95% CI: 0.90-0.96), left atrial conduit strain (LACS) (HR: 0.89 per 1% increase; 95% CI: 0.82-0.97), left atrial booster strain (LABS) (HR: 0.94 per 1% increase; 95% CI: 0.91-0.96) had great potential in predicting adverse cardiovascular events. The sensitivity analysis showed that the LAVImax, LARS, LACS and LABS results were stable. The funnel plot and Egger's test results revealed no significant publication bias among the LAVImax, LARS, and LACS studies (P>0.05).

Conclusions: Based on the existing literature, LA strain parameters show high predictive value for adverse cardiovascular events in DCM patients, while volume parameters show relatively low predictive value.

Background: Alcohol-related disorders affect 4.5-7.8% of Chinese adults and are known to precipitate alcoholic cardiomyopathy; nevertheless, conventional left ventricular ejection fraction (LVEF) often remains within the normal range in early disease, so a more sensitive marker of incipient systolic injury is needed. This study aims to quantify the diagnostic performance of two-dimensional speckle tracking imaging (2D-STI) for detecting sub-clinical left ventricular systolic dysfunction in patients with alcohol-related disorders.

Methods: Consecutive 50 in-patients (100% male, age 47.21±4.57 years) fulfilling diagnostic and statistical manual of mental disorders, fifth edition (DSM-5) criteria for alcohol-related disorder and 50 age- and sex-matched healthy controls were prospectively enrolled. All subjects underwent the same Philips EPIQ-7C scan (frame-rate ≥60 Hz). Global longitudinal (GLS), circumferential (GCS) and radial strain (GRS) together with their systolic strain rates were measured off-line (using advanced cardiac mechanics quantification analysis Software, aCMQ software). Conventional Simpson-derived left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV) and ejection fraction were also obtained. Between-group comparisons used unpaired t-test; associations were assessed with Pearson correlation.

Results: Compared with control group, patients with alcohol-related disorders showed higher (less negative) GLS (-13.5%±2.1% vs. -18.2%±2.5%, P<0.001), lower GCS (-17.6%±3.5% vs. -22.5%±4.0%, P<0.001), lower GRS (22.4%±5.2% vs. 30.8%±6.0%, P<0.001) and lower mean systolic strain rate (1.4±0.3 vs. 1.8±0.4 s^-1, P<0.001). LVEDV (90.5±15.2 vs. 78.0±12.5 mL, P<0.001), LVESV (58.6±12.0 vs. 35.5±10.0 mL, P<0.001) and LVEF (52.3%±5.0% vs. 60.5%±4.5%, P<0.001) all differed significantly. Within the patient cohort, LVEF correlated positively with GLS, GCS, GRS and strain rate (r=0.72, 0.68, 0.70, 0.73, all P<0.001), while LVEDV and LVESV correlated negatively with these indices (r range, -0.60 to -0.71; all P<0.001).

Conclusions: In males with alcohol-related disorders, 2D-STI consistently reveals multidirectional strain deficits that correlate tightly with conventional left ventricular volumes and ejection fraction, supporting its use for early detection of sub-clinical systolic dysfunction.

Background: Coronary computed tomography angiography (CCTA) has provided excellent anatomical detail for coronary artery disease (CAD), but does not provide hemodynamic assessment. The application of conventional computed tomography perfusion (CTP) combined with CCTA to address this issue has been found to increase the scan time, radiation dose, and contrast media (CM). This study aimed to evaluate the feasibility of the low-dose "one-stop" myocardial CTP as an innovative computed tomography (CT) examination that could comprehensively assess patients suspected of CAD in a single scan.

Methods: Consecutive patients (n=94) with suspected CAD who underwent the 70 kV "one-stop" CTP and gender- and age-matched patients (n=62) who underwent conventional CCTA were included. The best enhanced CTP phase for coronary arteries was selected as the CCTA phase. The CM and effective dose (ED) were recorded. The image quality of the two groups was assessed. Patients who underwent CTP were divided into three groups [normal (0%, n=14), non-significant (1-49%, n=31), and significant stenosis (50-100%, n=49)] on the basis of degree of coronary stenosis. The cardiac function, myocardial strain, and myocardial blood flow (MBF) of each subgroup were analyzed.

Results: Compared to the conventional CCTA protocol, the ED of "one-stop" CTP reduced by 44.5% (4.13±0.33 vs. 7.56±1.43 mSv, P<0.05). Image noise in the CTP-derived CCTA phase was slightly higher (23.78±1.01 vs. 18.5±1.04, P<0.05). There were no significant differences in the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) between two groups (all P>0.05). The left ventricular ejection fraction (LVEF) and the absolute value of left ventricular (LV) global radial strain (GRS), circumferential strain (GCS), and longitudinal strain (GLS) decreased as the coronary stenosis increased (LVEF: r=-0.56; GLS: r=0.61; GCS: r=0.54; GRS: r=-0.46; P<0.05 for all comparisons). MBF was significantly higher in patients without CAD compared with those with non-significant and significant arterial stenosis (139.96±5.3 vs. 133.95±3.7 vs. 125.53±4.55 mL/100 mL/min, P<0.05 for all). MBF also varied significantly among territories supplied by coronary arteries with different stenosis, exhibiting a significant difference (all P<0.05).

Conclusions: The advanced low-dose "one-stop" CTP protocol enables the simultaneous acquisition of coronary artery anatomy, ventricular function, myocardial strain, and hemodynamic information using low radiation dose and CM usage. This approach is beneficial for clinical decision-making and patient care in individuals with CAD.