T1-weighted MRI Images Research Articles

The Impact of Paraspinal Muscle Degeneration on Oswestry Disability Index Subsections Two Years After Spinal Surgery for Degenerative Lumbar Spondylolisthesis.

Secondary analysis of prospective study. To investigate the impact of fatty infiltration (FI) in the paraspinal muscles (PM) on postoperative Oswestry Disability Index (ODI) subsection-scores in patients undergoing elective lumbar surgery for degenerative lumbar spondylolisthesis (DLS). DLS can increase FI in the PM like the multifidus (MF), erector spinae (ES), and psoas (PS), leading to greater spinal disability and higher ODI-scores. It is unclear if increased PM FI affects all ODI subsections equally. This study reviewed data from a single-center cohort of DLS-patients who underwent elective lumbar surgery. Overall ODI and the subsection scores were prospectively assessed before and 2 years post-surgery. FI of the PS, MF, and ES was measured using T2-weighted MRI images. The relationship between PM FI and postoperative ODI subsections were analyzed, using multivariable linear regression analysis. Overall, 229 patients (59% female, mean age of 67±8y) were included with an overall baseline ODI of 50[40-62] and a postoperative ODI of 16.[2-34] The highest preoperative subsection-scores were seen in pain intensity (4[2-4]), changing degree of pain (4[3-4]), lifting (4[1-5]), and standing (4[3-4]). PM-measurements showed a mean FI of 41±10% for ES, 58±15% for MF, and 6±5% for PS. Patients with increased ES FI were more likely to show higher postoperative scores in all ODI subsections and in the overall ODI (Est=0.45, 95%CI 0.20-0.71, P=0.004). Increased MF FI was significantly associated with higher postoperative ODI subsection scores in standing (Est=0.02, 95%CI 0.01-0.03, P=0.033) and walking (Est=0.02, 95%CI 0.01-0.03, P=0.017). Increased erector spinae fatty infiltration is significantly associated with higher ODI scores across all subsections 2 years after lumbar surgery, while higher multifidus fatty infiltration is linked to greater disability in standing and walking. These findings underscore the need to maintain paraspinal muscle health to improve surgical planning, improve rehabilitation outcomes, and reduce postoperative disability.

Intra-individual structural covariance network in schizophrenia patients with persistent auditory hallucinations

Neuroimaging studies have revealed that the mechanisms of auditory hallucinations are related to morphological changes in multiple cortical regions, but studies on brain network properties are lacking. This study aims to construct intra-individual structural covariance networks and reveal network changes related to auditory hallucinations. T1-weighted MRI images were acquired from 90 schizophrenia patients with persistent auditory hallucinations (pAH group), 55 schizophrenia patients without auditory hallucinations (non-pAH group), and 83 healthy controls (HC group). Networks were constructed using the voxel-based gray matter volume and the intra-individual structural covariance was based on the similarity between the morphological variations of any two regions. One-way ANCOVA was employed to compare global and local network metrics among the three groups, and edge analysis was conducted via network-based statistics. In the pAH group, Pearson correlation analysis between network metrics and clinical symptoms was conducted. Compared with the HC group, both the pAH group (p = 0.01) and the non-pAH group (p = 3.56 × 10−4) had lower nodal efficiency of the left medial superior frontal gyrus. Compared to the non-pAH group and HC group, the pAH group presented lower nodal efficiency of the temporal pole of the left superior temporal gyrus (p = 1.09 × 10−3; p = 7.67 × 10−4) and right insula (p = 0.02; p = 8.99 × 10−6), and lower degree centrality of the right insula (p = 0.04; p = 1.65 × 10−5). The pAH group had a subnetwork with reduced structural covariance centered by the left temporal pole of the superior temporal gyrus. In the pAH group, the normalized clustering coefficient (r = −0.36, p = 8.45 × 10−3) and small-worldness (r = −0.35, p = 9.89 × 10−3) were negatively correlated with the PANSS positive scale score. This study revealed network changes in schizophrenia patients with persistent auditory hallucinations, and provided new insights into the structural architecture related to auditory hallucinations at the network level.

A case report: mucinous carcinoma of extrahepatic bile ducts with periportal calcification suggestive of lymphatic calcification.

Mucinous carcinoma is a rare subtype of extrahepatic bile duct (EHBD) cancer in which abundant mucin surrounds cancer cell nests. Calcification is often observed in mucus-producing tumors, and we herein report a case of mucinous carcinoma of EHBD with diffuse periportal calcifications. A 92-year-old woman was seen for jaundice. Non-contrast CT showed diffuse semi-linear branching calcifications extending from the hepatoduodenal ligament along the long axis of the intra- and extrahepatic portal veins. A bile duct mass and multiple lymph nodes were hyperintense on T2-weighted MRI images, and periportal abnormal intensity was visible along the intrahepatic portal tract. On contrast-enhanced CT, the mass and enlarged lymph nodes showed a weak enhancing effect. In addition, a lesion with a similar enhancing effect was observed in parallel with periportal calcification, raising suspicion of tumor extension. An endoscopic bile duct biopsy was performed, which resulted in the tumor being diagnosed as a mucinous carcinoma. Considering the imaging findings, it was determined to be a mucinous carcinoma of EHBD origin, with lymphatic vessel infiltration and multiple lymph node metastases. The lymphatic tracts presented lymphangitic carcinomatosis extending from the hepatoduodenal ligament to intrahepatic portal tracts, and the diffuse periportal calcification was presumed to be calcification associated with the mucin component of tumor infiltration.

T2 MRI visible Perivascular Spaces in Parkinson`s Disease: clinical significance and association with polysomnography measured sleep.

Poor sleep quality might contribute to the risk and progression of neurodegenerative disorders via deficient cerebral waste clearance functions during sleep. In this retrospective cross-sectional study, we explore the link between enlarged perivascular spaces (PVS), a putative marker of sleep-dependent glymphatic clearance, with sleep quality and motor symptoms in Parkinson`s disease (PD) patients. T2-weighted MRI images of 20 patients and 17 healthy control subjects were estimated visually for PVS in the basal ganglia (BG) and centrum semiovale (CSO). The patient group additionally underwent a single-night polysomnography. Readouts included polsyomnographic sleep features and slow-wave activity (SWA), a quantitative EEG marker of sleep depth. Associations between PVS counts, PD symptoms (MDS-UPDRS scores) and sleep parameters were evaluated using correlation and regression analyses. Intra- and inter-rater reproducibility was assessed with weighted Cohen`s kappa coefficient. BG and CSO PVS counts in both patients and controls did not differ significantly between groups. In patients, PVS in both brain regions were negatively associated with SWA (1-2Hz) (BG: r(15)=-0.58, padj=0.015 and CSO: r(15)=-0.6, padj=0.015). Basal ganglia PVS counts were positively associated with motor symptoms of daily living (IRR=1.05, CI [1.01, 1.09], p=0.007, padj=0.026) and antidepressant use (IRR=1.37, CI [1.05, 1.80], p=0.021, padj=0.043) after controlling for age. Centrum Semiovale PVS counts in patients were positively associated with a diagnosis of REM sleep behaviour disorder (IRR=1.39, CI [1.06 , 1.84]), p=0.018, padj=0.11). These results add evidence that sleep deterioration may play a role in impairing glymphatic clearance via altered perivascular function, potentially contributing to disease severity in PD patients.

Multimodal MRI-based radiomics models for the preoperative prediction of lymphovascular space invasion of endometrial carcinoma

PurposeTo evaluate the predictive capabilities of MRI-based radiomics for detecting lymphovascular space invasion (LVSI) in patients diagnosed with endometrial carcinoma (EC).Materials and methodsA retrospective analysis was conducted on 160 female patients diagnosed with EC. The radiomics model including T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) images was established. Additionally, a conventional MRI model, which incorporated MRI-reported FIGO stage, deep myometrial infiltration (DMI), adnexal involvement, and vaginal/parametrial involvement, was established. Finally, a combined model was created by integrating the radiomics signature and conventional MRI characteristics. The predictive performance was validated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. A stratified analysis was conducted to compare the differences between the three models by Delong test.ResultsIn predicting LVSI, the radiomics model outperformed the clinical model in the training cohort (AUC: 0.899 vs. 0.8862) but not in the test cohort (AUC: 0.812 vs. 0.8758). The combined model demonstrated superior performance in both the training and test cohorts (training cohort: AUC = 0.934, 95% CI: 0.8807–0.9873; testing cohort: AUC = 0.905, 95% CI: 0.7679-1).ConclusionsThe combined model exhibited utility in preoperatively predicting LVSI in patients with EC, offering potential benefits for clinical decision-making.

Estimating lumbar bone mineral density from conventional MRI and radiographs with deep learning in spine patients.

This study aimed to develop machine learning methods to estimate bone mineral density and detect osteopenia/osteoporosis from conventional lumbar MRI (T1-weighted and T2-weighted images) and planar radiography in combination with clinical data and imaging parameters of the acquisition protocol. A database of 429 patients subjected to lumbar MRI, radiographs and dual-energy x-ray absorptiometry within 6 months was created from an institutional database. Several machine learning models were trained and tested (373 patients for training, 86 for testing) with the following objectives: (1) direct estimation of the vertebral bone mineral density; (2) classification of T-score lower than - 1 or (3) lower than - 2.5. The models took as inputs either the images or radiomics features derived from them, alone or in combination with metadata (age, sex, body size, vertebral level, parameters of the imaging protocol). The best-performing models achieved mean absolute errors of 0.15-0.16g/cm2 for the direct estimation of bone mineral density, and areas under the receiver operating characteristic curve of 0.82 (MRIs) - 0.80 (radiographs) for the classification of T-scores lower than - 1, and 0.80 (MRIs) - 0.65 (radiographs) for T-scores lower than - 2.5. The models showed good discriminative performances in detecting cases of low bone mineral density, and more limited capabilities for the direct estimation of its value. Being based on routine imaging and readily available data, such models are promising tools to retrospectively analyse existing datasets as well as for the opportunistic investigation of bone disorders.

MRI T2w Radiomics-Based Machine Learning Models in Imaging Simulated Biopsy Add Diagnostic Value to PI-RADS in Predicting Prostate Cancer: A Retrospective Diagnostic Study.

Currently, prostate cancer (PCa) prebiopsy medical image diagnosis mainly relies on mpMRI and PI-RADS scores. However, PI-RADS has its limitations, such as inter- and intra-radiologist variability and the potential for imperceptible features. The primary objective of this study is to evaluate the effectiveness of a machine learning model based on radiomics analysis of MRI T2-weighted (T2w) images for predicting PCa in prebiopsy cases. A retrospective analysis was conducted using 820 lesions (363 cases, 457 controls) from The Cancer Imaging Archive (TCIA) Database for model development and validation. An additional 83 lesions (30 cases, 53 controls) from Hong Kong Queen Mary Hospital were used for independent external validation. The MRI T2w images were preprocessed, and radiomic features were extracted. Feature selection was performed using Cross Validation Least Angle Regression (CV-LARS). Using three different machine learning algorithms, a total of 18 prediction models and 3 shape control models were developed. The performance of the models, including the area under the curve (AUC) and diagnostic values such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared to the PI-RADS scoring system for both internal and external validation. All the models showed significant differences compared to the shape control model (all p < 0.001, except SVM model PI-RADS+2 Features p = 0.004, SVM model PI-RADS+3 Features p = 0.002). In internal validation, the best model, based on the LR algorithm, incorporated 3 radiomic features (AUC = 0.838, sensitivity = 76.85%, specificity = 77.36%). In external validation, the LR (3 features) model outperformed PI-RADS in predictive value with AUC 0.870 vs. 0.658, sensitivity 56.67% vs. 46.67%, specificity 92.45% vs. 84.91%, PPV 80.95% vs. 63.64%, and NPV 79.03% vs. 73.77%. The machine learning model based on radiomics analysis of MRI T2w images, along with simulated biopsy, provides additional diagnostic value to the PI-RADS scoring system in predicting PCa.

The Association between prior arthroplasty and Paraspinal Muscle Degeneration in patients undergoing elective lumbar surgery.

Spinal and lower extremity degeneration often causes pain and disability. Lower extremity osteoarthritis, eventually leading to total knee- (TKA) and -hip arthroplasty (THA), can alter posture through compensatory mechanisms, potentially causing spinal misalignment and paraspinal muscle (PM) atrophy. This study aims to evaluate the association between prior THA or TKA and PM-degeneration in patients undergoing elective lumbar surgery for degenerative conditions. A retrospective analysis of patients undergoing lumbar surgery for degenerative conditions was conducted. Patients were categorized based on prior THA, TKA, or both. Quantitative analysis of functional cross-sectional area (fCSA) and fat infiltration (FI) of psoas, multifidus (MF), and erector spinae (ES) muscles at L4-level was performed using T2-weighted MRI images. The association between the FI and fCSA of the PM and prior arthroplasty was investigated. Differences were assessed using ANOVA and multivariable linear regression. Overall, 584 patients (60% female, 64 ± 12 years) were included. 66 patients (11%) had prior TKA, 36 patients (6%) THA, and 15 patients (3%) both TKA and THA. Patients with arthroplasty were mostly female (57%) and notably older (p < 0.001). The FI of the MF and the ES was significantly higher in the arthroplasty-group (both p < 0.001). Patients with prior TKA showed significantly higher FI (Est = 4.3%, p = 0.013) and lower fCSA (Est=-0.9 cm2, p = 0.012) in the MF compared to the non-arthroplasty-group. This study demonstrates a significant lower fCSA and higher FI in the MF among individuals with prior TKA. This highlights the complex knee-spine relationship and how these structures interact with each other.

Accelerating breast MRI acquisition with generative AI models.

To investigate the use of the score-based diffusion model to accelerate breast MRI reconstruction. We trained a score-based model on 9549 MRI examinations of the female breast and employed it to reconstruct undersampled MRI images with undersampling factors of 2, 5, and 20. Images were evaluated by two experienced radiologists who rated the images based on their overall quality and diagnostic value on an independent test set of 100 additional MRI examinations. The score-based model produces MRI images of high quality and diagnostic value. Both T1- and T2-weighted MRI images could be reconstructed to a high degree of accuracy. Two radiologists rated the images as almost indistinguishable from the original images (rating 4 or 5 on a scale of 5) in 100% (radiologist 1) and 99% (radiologist 2) of cases when the acceleration factor was 2. This fraction dropped to 88% and 70% for an acceleration factor of 5 and to 5% and 21% with an extreme acceleration factor of 20. Score-based models can reconstruct MRI images at high fidelity, even at comparatively high acceleration factors, but further work on a larger scale of images is needed to ensure that diagnostic quality holds. The number of MRI examinations of the breast is expected to rise with MRI screening recommended for women with dense breasts. Accelerated image acquisition methods can help in making this examination more accessible. Accelerating breast MRI reconstruction remains a significant challenge in clinical settings. Score-based diffusion models can achieve near-perfect reconstruction for moderate undersampling factors. Faster breast MRI scans with maintained image quality could revolutionize clinic workflows and patient experience.

A comparison of 2D and 3D magnetic resonance imaging-based intratumoral and peritumoral radiomics models for the prognostic prediction of endometrial cancer: a pilot study

BackgroundAccurate prognostic assessment is vital for the personalized treatment of endometrial cancer (EC). Although radiomics models have demonstrated prognostic potential in EC, the impact of region of interest (ROI) delineation strategies and the clinical significance of peritumoral features remain uncertain. Our study thereby aimed to explore the predictive performance of varying radiomics models for the prediction of LVSI, DMI, and disease stage in EC.MethodsPatients with 174 histopathology-confirmed EC were retrospectively reviewed. ROIs were manually delineated using the 2D and 3D approach on T2-weighted MRI images. Six radiomics models involving intratumoral (2Dintra and 3Dintra), peritumoral (2Dperi and 3Dperi), and combined models (2Dintra + peri and 3Dintra + peri) were developed. Models were constructed using the logistic regression method with five-fold cross-validation. Area under the receiver operating characteristic curve (AUC) was assessed, and was compared using the Delong’s test.ResultsNo significant differences in AUC were observed between the 2Dintra and 3Dintra models, or the 2Dperi and 3Dperi models in all prediction tasks (P > 0.05). Significant difference was observed between the 3Dintra and 3Dperi models for LVSI (0.738 vs. 0.805) and DMI prediction (0.719 vs. 0.804). The 3Dintra + peri models demonstrated significantly better predictive performance in all 3 prediction tasks compared to the 3Dintra model in both the training and validation cohorts (P < 0.05).ConclusionsComparable predictive performance was observed between the 2D and 3D models. Combined models significantly improved predictive performance, especially with 3D delineation, suggesting that intra- and peritumoral features can provide complementary information for comprehensive prognostication of EC.

Exploring deep learning strategies for intervertebral disc herniation detection on veterinary MRI

Intervertebral Disc Herniation (IVDH) is a common spinal disease in dogs, significantly impacting their health, mobility, and overall well-being. This study initiates an effort to automate the detection and localization of IVDH lesions in veterinary MRI scans, utilizing advanced artificial intelligence (AI) methods. A comprehensive canine IVDH dataset, comprising T2-weighted sagittal MRI images from 213 pet dogs of various breeds, ages, and sizes, was compiled and utilized to train and test the IVDH detection models. The experimental results showed that traditional two-stage detection models reliably outperformed one-stage models, including the recent You Only Look Once X (YOLOX) detector. In terms of methodology, this study introduced a novel spinal localization module, successfully integrated into different object detection models to enhance IVDH detection, achieving an average precision (AP) of up to 75.32%. Additionally, transfer learning was explored to adapt the IVDH detection model for a smaller feline dataset. Overall, this study provides insights into advancing AI for veterinary care, identifying challenges and exploring potential strategies for future development in veterinary radiology.

Robust deep learning estimation of cortical bone porosity from MR T1-weighted images for individualized transcranial focused ultrasound planning.

Transcranial focused ultrasound (tFUS) is an emerging neuromodulation approach that has been demonstrated in animals but is difficult to translate to humans because of acoustic attenuation and scattering in the skull. Optimal dose delivery requires subject-specific skull porosity estimates which has traditionally been done using CT. We propose a deep learning (DL) estimation of skull porosity from T1-weighted MRI images which removes the need for radiation-inducing CT scans. We evaluate the impact of different DL approaches, including network architecture, input size and dimensionality, multichannel inputs, data augmentation, and loss functions. We also propose back-propagation in the mask (BIM), a method whereby only voxels inside the skull mask contribute to training. We evaluate the robustness of the best model to input image noise and MRI acquisition parameters and propagate porosity estimation errors in thousands of beam propagation scenarios. Our best performing model is a cGAN with a ResNet-9 generator with 3D 64×64×64 inputs trained with L1 and L2 losses. The model achieved a mean absolute error of 6.9% in the test set, compared to 9.5% with the pseudo-CT of Izquierdo et al. (38% improvement) and 9.4% with the generic pixel-to-pixel image translation cGAN pix2pix (36% improvement). Acoustic dose distributions in the thalamus were more accurate with our approach than with the pseudo-CT approach of both Burgos et al. and Izquierdo et al, resulting in near-optimal treatment planning and dose estimation at all frequencies compared to CT (reference). Our DL approach porosity estimates with ~7% error, is robust to input image noise and MRI acquisition parameters (sequence, coils, field strength) and yields near-optimal treatment planning and dose estimates for both central (thalamus) and lateral brain targets (amygdala) in the 200-1000 kHz frequency range.

Impact of Preoperative Intervertebral Disc Degeneration on Patient-Reported Outcome Measures After Lumbar Fusion

The Pfirrmann scoring system classifies lumbosacral disc degeneration based on MRI signal intensity. The relationship between pre-existing disc degeneration and PROMs after one-level lumbar fusion is not well documented. The purpose of this study was to investigate the relationship between the severity of preoperative intervertebral disc degeneration and pre- and postoperative patient-reported outcome measures (PROMs) in patients undergoing one-level lumbar fusion. All adult patients underwent posterior lumbar decompression and fusion (PLDF) or transforaminal lumbar interbody fusion (TLIF) between 2014-2022 were included. Patient demographics, and comorbidities were extracted from medical records. Lumbar intervertebral discs on sagittal MRI T2-weighted images were assessed by two independent graders utilizing Pfirrmann criteria. Grades I-III were categorized as low-grade disc degeneration, while IV-V were considered high-grade. Multivariable linear regression assessed the impact of disc degeneration on PROMS. A total of 150 patients were included, of which, 69 (46%) had low grade disc degeneration, while 81 (54%) had high grade degeneration. Patients with high-grade degeneration had increased preoperative VAS-Leg scores (6.10 vs. 4.54, p=0.005) and displayed greater one-year postoperative improvements in VAS-Back scores (-2.11 vs -0.66, p=0.002). Multivariable regression demonstrated Pfirrmann scores as independent predictors for both preoperative VAS-Leg scores (p=0.004) and postoperative VAS-Back improvement (p=0.005). In patients undergoing one-level lumbar fusion, higher Pfirmann scores were associated with increased preoperative leg pain and greater one-year postoperative improvement in back pain. Further studies into the relationship of pre-operative disc degeneration and their impact on postoperative outcomes may help guide clinical decision making and patient expectations.

Quantitative Assessment of Intervertebral Disc Degeneration Using the Disc Signal Intensity Index in Patients With Spine-Related Pain.

Objective This is a retrospective observational study that aims to investigate the association between disc signal intensity index (DSI2) scores and patient-reported outcome measures in patients with lumbar spine disorders. Methods We introduced DSI2 to quantitatively assess disc degeneration. MRI records of patients with lumbar spine-related pain between 2019 and 2022 were analyzed retrospectively. Patient demographics and outcomes were collected, including the Numerical Rating Scale of Pain and EuroQol Group 5 Dimension 5-Level Quality of Life (EQ-5D-5L) scores. The DSI2 was calculated by dividing the mean signal intensity of the L1-S1 discs by that of the CSF on midsagittal T2-weighted MRI images. Results Each DSI2 level corresponded to a Pfirrmann grade score at the respective lumbar level. Multivariable linear regression analysis using the EQ-5D-5L as the objective variable identified BMI (p = 0.007) and average DSI2 (p = 0.018) as independent risk factors for EQ-5D-5L deterioration. However, the mean Pfirrmann grade score was not an independent risk factor (p = 0.58). Conclusion Our study using DSI2 showed the relationship between disc degeneration and EQ-5D-5L deterioration. Distinct from the Pfirrmann grading system, the DSI2 method is a promising alternative for future disc research that excellently detects the subtle progression of degeneration.

A Case Series and Literature Review of Alveolar Echinococcosis in Kashmir, India: An Emerging Endemic Zone for Echinococcus multilocularis.

A prospective study on 110 patients with echinococcosis at Dr. Khuroo's Medical Clinic, Srinagar, Kashmir, India, from March 2019 to April 2024 identified 12 cases (4 males, 8 females; mean age of 46.58 ± 11.97 years) of Alveolar echinococcosis (AE). Two patients were detected through ultrasound examinations carried out for unrelated causes; one presented with features of liver abscess, and nine had pain in the right upper quadrant for a mean period of 2.2 ± 1.79 years. All had the liver as the primary organ involved, with 15 tumor masses of a mean maximum diameter of 9.22 ± 3.21 cm and volume of 426 ± 374.61 cm3. Tumors placed centrally had invaded vessels and the biliary tract in eight patients, and those placed peripherally had invaded the liver capsule and adjacent organs in nine patients. Histologic examination of liver biopsies or resected organs revealed necrotic lesions, calcifications, and granulomatous inflammation with slender, thin-walled vesicles of bizarre configuration that stained strongly eosinophilic with periodic acid Schiff. Two patients had segmental liver resections; one was treated with liver aspiration, while the other nine with advanced disease received chemotherapy with albendazole along with praziquantel. Patients showed clinical improvement on a median follow-up of 12 months (range 1 to 60 months); however, MRI T2-weighted images and 18F-FDG-PET-CECT scans in two patients showed active disease on follow-up at one and five years, respectively. A systematic review detected 146 cases of AE in India from 1980 to April 2024. Twenty cases were from foreign countries, mostly from Central Asian republics, and 118 (93.65%) of the remaining 126 Indian patients were permanent residents of Kashmir Valley. The disease affected a population of 79,197 residing in 22 villages from 5 border districts of the valley. These villages were either high in or adjacent to the Himalayan mountain range. Disease prevalence in the affected population was 146.47/105 (males 131.53/105 and females 163.18/105) and the incidence was 12.41/105/year (males 11.16/105/year and females 13.81/105/year). Possible causes of the emergence of AE are discussed, and future directions for research to face this challenge arebeen identified.

Diagnostic Utility of Specific Frailty Questionnaire: The Kihon Checklist for Hippocampal Atrophy in COPD.

Background/Objectives: COPD patients who are frail have been reported to develop brain atrophy, but no non-invasive diagnostic tool has been developed to detect this condition. Our study aimed to explore the diagnostic utility of the Kihon Checklist (KCL), a frailty questionnaire, in assessing hippocampal volume loss in patients with COPD. Methods: We recruited 40 COPD patients and 20 healthy individuals using the KCL to assess frailty across seven structural domains. Hippocampal volumes were obtained from T1-weighted MRI images, and ROC analysis was performed to detect hippocampal atrophy. Results: Our results showed that patients with COPD had significantly greater atrophic left hippocampal volumes than healthy subjects (p < 0.05). The univariate correlation coefficient between the left hippocampal volume and KCL (1-20), which pertains to instrumental and social activities of daily living, was the largest (ρ = -0.54, p < 0.0005) among the KCL subdomains. Additionally, both KCL (1-25) and KCL (1-20) demonstrated useful diagnostic potential (93% specificity and 90% sensitivity, respectively) for identifying individuals in the lowest 25% of the left hippocampal volume (AUC = 0.82). Conclusions: Our study suggests that frailty questionnaires focusing on daily vulnerability, such as the KCL, can effectively detect hippocampal atrophy in COPD patients.

Geometrical accuracy of magnetic resonance imaging for ocular proton therapy planning

Background & purposeMagnetic resonance imaging (MRI) is increasingly used in treatment preparation of ocular proton therapy, but its spatial accuracy might be limited by geometric distortions due to susceptibility artefacts. A correct geometry of the MR images is paramount since it defines where the dose will be delivered. In this study, we assessed the geometrical accuracy of ocular MRI. Materials & methodsA dedicated ocular 3 T MRI protocol, with localized shimming and increased gradients, was compared to computed tomography (CT) and X-ray images in a phantom and in 15 uveal melanoma patients. The MRI protocol contained three-dimensional T2-weighted and T1-weighted sequences with an isotropic reconstruction resolution of 0.3–0.4 mm. Tantalum clips were identified by three observers and clip-clip distances were compared between T2-weighted and T1-weighted MRI, CT and X-ray images for the phantom and between MRI and X-ray images for the patients. ResultsInterobserver variability was below 0.35 mm for the phantom and 0.30(T1)/0.61(T2) mm in patients. Mean absolute differences between MRI and reference were below 0.27 ± 0.16 mm and 0.32 ± 0.23 mm for the phantom and in patients, respectively. In patients, clip-clip distances were slightly larger on MRI than on X-ray images (mean difference T1: 0.11 ± 0.38 mm, T2: 0.10 ± 0.44 mm). Differences did not increase at larger distances and did not correlate to interobserver variability. ConclusionsA dedicated ocular MRI protocol can produce images of the eye with a geometrical accuracy below half the MRI acquisition voxel (<0.4 mm). Therefore, these images can be used for ocular proton therapy planning, both in the current model-based workflow and in proposed three-dimensional MR-based workflows.

Comparison of detailed brain volume measurements in schizophrenia with healthy individuals

Aims: This study aims to provide insights into the preliminary and definitive diagnosis, treatment modalities, and elucidation of the underlying pathophysiological mechanisms of schizophrenia, alongside ongoing research efforts. Methods: This retrospective study examined a cohort of 31 patients (17 male, 14 female) diagnosed with schizophrenia according to the criteria specified in the Diagnostic and Statistical Manual of Mental Disorders (DSM V) at the Psychiatry Clinic of the University Hospital. The healthy control group, comprising 31 individuals (17 male, 14 female), was selected from archived records at the same hospital, with sociodemographic characteristics matching those of the patient group. High-resolution T1-weighted MRI images of the individuals were analyzed using the VolBrain AssemblyNet program. Results: It has been observed that the volumes of various brain structures, including white matter (WM), gray matter (GM), subcortical GM, cortical GM, WM+GM, IC, total cerebrum, right cerebrum, left cerebrum, cerebrum WM, cerebrum GM, are significantly reduced (p&lt;0.05) in patients diagnosed with schizophrenia compared to healthy controls. Additionally, volumes of cerebellar WM, GM, vermis, brainstem, accumbens, hippocampus, thalamus, ventral diencephalon (DC), amygdala, and basal forebrain were found to be decreased in schizophrenia patients compared to healthy controls. Conversely, volumes of the pallidum, caudate, putamen, inferior lateral ventricle, lateral ventricle, third ventricle, fourth ventricle, and CSF were observed to be increased. Conclusion: These findings underscore the widespread nature of neuroanatomical alterations in schizophrenia and highlight the importance of understanding these changes for elucidating the disorder's pathophysiology and developing targeted therapeutic interventions. Further research is warranted to explore the interplay of genetic, developmental, and environmental factors in schizophrenia.

Conversion of T2-Weighted Magnetic Resonance Images of Cervical Spine Trauma to Short T1 Inversion Recovery (STIR) Images by Generative Adversarial Network.

The short T1 inversion recovery (STIR) sequence is advantageous for visualizing ligamentous injuries, but the STIR sequence may be missing in some cases. The purpose of this study was to generate synthetic STIR images from MRI T2-weighted images (T2WI) of patients with cervical spine trauma using a generative adversarial network (GAN). Methods: A total of 969 pairs of T2WI and STIR images were extracted from 79 patients with cervical spine trauma. The synthetic model was trained 100 times, and the performance of the model was evaluated with five-fold cross-validation. Results: As for quantitative validation, the structural similarity score was 0.519±0.1 and the peak signal-to-noise ratio score was 19.37±1.9 dB. As for qualitative validation, the incorporation of synthetic STIR images generated by a GAN alongside T2WI substantially enhances sensitivity in the detection of interspinous ligament injuries, outperforming assessments reliant solely on T2WI. The GAN model can generate synthetic STIRs from T2 images of cervical spine trauma using image-to-image conversion techniques. The use of a combination of synthetic STIR images generated by a GAN and T2WI improves sensitivity in detecting interspinous ligament injuries compared to assessments that use only T2WI.

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features

BackgroundSemiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients’ survival analysis.MethodsTraining (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic.ResultsAlthough the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis.ConclusionsThe Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival.Trial registrationClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/, date of registration: 30th June 2017.