Coronal T1-weighted Images Research Articles

Deep learning-based segmentation of kidneys and renal cysts on T2-weighted MRI from patients with autosomal dominant polycystic kidney disease

BackgroundOur aim was to train and test a deep learning-based algorithm for automatically segmenting kidneys and renal cysts in patients with autosomal dominant polycystic kidney disease (ADPKD).MethodsWe retrospectively selected all ADPKD patients who underwent renal MRI with coronal T2-weighted imaging at our institution from 2008 to 2022. The 20 most recent examinations constituted the test dataset, to mimic pseudoprospective enrolment. The remaining ones constituted the training dataset to which eight normal renal MRIs were added. Kidneys and cysts ground truth segmentations were performed on coronal T2-weighted images by a junior radiologist supervised by an experienced radiologist. Kidneys and cysts of the 20 test MRIs were segmented by the algorithm and three independent human raters. Segmentations were compared using overlap metrics. The total kidney volume (TKV), total cystic volume (TCV), and cystic index (TCV divided by TKV) were compared using Bland–Altman analysis.ResultsWe included 164 ADPKD patients. Dice similarity coefficients ranged from 85.9% to 87.4% between the algorithms and the raters’ segmentations and from 84.2% to 86.2% across raters’ segmentations. For TCV assessment, the biases ± standard deviations (SD) were 3–19 ± 137–151 mL between the algorithm and the raters, and 22–45 ± 49–57 mL across raters. The algorithm underestimated TKV and TCV in two outliers with TCV > 2800 mL. For cystic index assessment, the biases ± SD were 2.5–6.9% ± 6.7–8.3% between the algorithm and the raters, and 2.1–9.4 ± 7.4–11.6% across raters.ConclusionThe algorithm’s performance fell within the range of inter-rater variability, but large TKV and TCV were underestimated.Relevance statementAccurate automated segmentation of the renal cysts will enable the large-scale evaluation of the prognostic value of TCV and cystic index in ADPKD patients. If these biomarkers are prognostic, then automated segmentation will facilitate their use in daily routine.Key PointsCystic volume is an emerging biomarker in ADPKD.The algorithm’s performance in segmenting kidneys and cysts fell within interrater variability.The segmentation of very large cysts, under-represented in the training dataset, needs improvement.Graphical

Morphometric analysis of pituitary gland using magnetic resonance imaging in Indian subjects

Background: Before the advent of magnetic resonance imaging (MRI), the radiological assessment of the hypothalamic-pituitary region relied on plain radiographs and computed tomography scans. However, contemporary medical practice now widely recognizes MRI as the preferred imaging modality for this purpose. The objective is to investigate variations in size and morphology of the pituitary gland associated with age and gender. Aims and Objectives: (a) The aims of this study were as follows: To establish normative measurements of pituitary gland in South Indian population. (b) To evaluate the relationship between gender, weight, height, and body mass index in different patients using conventional MRI sequences. Materials and Methods: This cross-sectional study, approved by the Institutional Review Board, included the enrolment of 140 normal individuals. The dimensions of the pituitary gland were assessed in 140 individuals exhibiting apparently normal pituitary gland function. The height and length of the pituitary gland were measured utilizing mid-sagittal T1-weighted images (T1-WI) obtained through MRI. The width of the pituitary gland was determined using coronal T1-WI, and the pituitary gland volume was subsequently calculated. Results: This study comprised 98 females and 102 males. The anteroposterior dimension, height, and volume of the pituitary gland showed statistically significant correlations with age (P<0.05), while the transverse dimension did not show a significant correlation with age (P>0.05). The shape of the pituitary gland was found to have statistically significant correlations with age and gender. Conversely, the transverse diameter, height, and volume of the pituitary gland showed no significant correlations. Conclusion: The findings underscore the importance of considering pituitary size, shape, and volume in the evaluation of neuroendocrine disorders.

Enhancing Preoperative Diagnosis of Subscapular Muscle Injuries with Shoulder MRI-based Multimodal Radiomics

PurposeRotator cuff injury is a common ailment in the musculoskeletal system, with the subscapularis muscle being the largest and most robust muscle of the rotator cuff. The occurrence of subscapularis muscle tears is more frequent than previously reported. The main objective of this research is to harness the power of artificial intelligence to enhance the precision in diagnosing subscapularis muscle injuries via magnetic resonance imaging of the shoulder joint, prior to surgical intervention. This study seeks to integrate advanced artificial intelligence algorithms to analyze magnetic resonance imaging data, aiming to provide more accurate preoperative assessments, which can potentially lead to better surgical outcomes and patient care and promote technological progress in the field of medical imaging analysis. MethodThis is a multicenter study that involves 324 patients from a major medical center serving as both the training and testing groups, with an additional 60 patients from two other medical centers comprising the verifying group. The imaging protocol for all these subjects included a series of shoulder magnetic resonance imaging scans: T1-weighted coronal sequences, T2-weighted coronal, axial, and sagittal images. These comprehensive imaging modalities were utilized to thoroughly examine the shoulder joint's anatomical details and to detect any signs of subscapularis muscle damage. To enhance the diagnostic accuracy before surgical procedures, radiomic analysis was employed. This technique involves the extraction of a multitude of quantitative features from the magnetic resonance imaging, which can provide a more nuanced and data-driven approach to identifying subscapularis muscle injuries. The integration of radiomics in this study aims to offer a more precise preoperative assessment, potentially leading to improved surgical planning and patient outcomes. ResultIn the course of this study, a comprehensive extraction of 1197 radiomic features was performed for each imaging modality of every patient. The coronal T1-weighted modality, when assessed within the internal verifying cohort, delivered a diagnostic accuracy of 0.766, coupled with an AUC of 0.803. In the case of the T2-weighted modality, the coronal planes exhibited a diagnostic accuracy of 0.781 and an AUC of 0.844. The axial T2-weighted images recorded an accuracy of 0.719 and an AUC of 0.761, while the sagittal T2-weighted images scored an accuracy of 0.766 and an AUC of 0.821. The amalgamation of these imaging techniques through a multimodal strategy markedly enhanced the accuracy to 0.828, with an AUC of 0.916 for the internal verifying group. The diagnostic performance of the coronal T1-weighted modality in the external verifying cohort yielded an accuracy of 0.833, with an area under the curve (AUC) of 0.819. For the T2-weighted modality, the coronal imaging demonstrated an accuracy of 0.767 and an AUC of 0.794. The axial T2-weighted images had an accuracy of 0.783 and an AUC of 0.797, while the sagittal T2-weighted images achieved an accuracy of 0.833 and an AUC of 0.800. When combining the modalities, the multimodal approach significantly improved the accuracy to 0.867, with an AUC of 0.803 in the external verifying group, indicating a robust diagnostic capability. ConclusionOur study demonstrates that the application of multimodal radiomic techniques to shoulder magnetic resonance imaging significantly enhances the precision of preoperative diagnosis for subscapularis muscle injuries. This approach leverages the comprehensive data provided by various magnetic resonance imaging modalities to offer a more detailed and accurate assessment, which is crucial for surgical planning and patient care.

Role of MR Cholangiopancreatography in the Diagnosis of Various Pediatric Hepatic and Pancreatobiliary Diseases

Abstract Objective The aim was to determine the role of MR cholangiopancreatography in the diagnosis of various hepatic and pancreatobiliary diseases in children and to correlate the MR imaging findings with the clinical data, laboratory results and the findings of the available other imaging modalities. Patients and Methods The present study retrospectively included 83 patients, with signs and symptoms denoting hepatic and pancreatobiliary abnormalities, who underwent paired radiological examination using US and MRI. MRCP examinations are coronal 3D fast spin-echo HR MRCP sequence, as well as axial and coronal T2-weighted fast spin-echo and fat-suppressed sequence images, axial DWI using a multi-slice single-shot echo-planar imaging sequence and ADC maps in gray scale. Agreement between US and MRI was done as regards identification of individual radiological abnormalities, diagnostic accuracy of US vs. MRI as gold-standard modality for detection of IHBRD and EHBRD was tested, and also diagnostic accuracy of US and MRI versus final (gold-standard) diagnosis was calculated. Result MRCP was found to have significantly higher diagnostic accuracy when compared to ultrasound (90.2%, 41.0%) respectively, especially in detection of pancreatic lesions (p-value <0.0001). Although there was good agreement between US and MR as regards detection of IHBRD and EHBRD, it was found that the US had a sensitivity of 69%, specificity of 90%, predictive value of positive test of 81%, predictive value of negative test of 82% for diagnosis of IHBRD and had sensitivity of 64%, specificity of 100%, predictive value of positive test of 100%, predictive value of negative test of 81%, when contrasted versus MRI as the gold-standard test. Conclusion MRCP have a higher diagnostic accuracy compared to US in detecting various hepatic and pancreatobiliary disease in children, especially in diagnosis of pancreatic diseases.

Predictive value of mono-exponential and multiple mathematical models in locally advanced rectal cancer response to neoadjuvant chemoradiotherapy.

This prospective study aimed to assess the predictive value of mono-exponential and multiple mathematical diffusion-weighted imaging (DWI) models in determining the response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). The study included 103 LARC patients scheduled for preoperative chemoradiotherapy between December 2021 and June 2023 Magnetic resonance imaging (MRI) scans were performed using a 3.0-T MR scanner, encompassing sagittal, axial, and oblique coronal T2-weighted images without fat saturation, along with DWI perpendicular to the rectum's long axis. Various DWI parameters, including apparent diffusion coefficient (ADC), stretched exponential model (SEM), continuous-time random-walk model (CTRW), and fractional-order calculus model (FROC), were measured. The pathologic complete response (pCR) rate and tumor downstaging (T-downstage) rate were determined. After nCRT, SEM-α, SEM-DDC, CTRW-α, CTRW-β, CTRW-D, FROC-β, and ADC values were significantly higher in the pCR group compared to the non-pCR group (all P < 0.05). SEM-DDC, CTRW-α, CTRW-D, FROC-β, FROC-µ, and ADC values were significantly higher in the T-downstage group (ypT0-1) than in the non-T-downstage group (ypT2-4) (P < 0.05). The combination of CTRW (α + β + D) exhibited the best diagnostic performance for assessing pCR after nCRT (AUC = 0.840, P < 0.001). Pre-nCRT CTRW (α + β) demonstrated a predictive AUC of 0.652 (95%CI: 0.552-0.743), 90.3% sensitivity, and 43.1% specificity for pCR. Regarding T-downstage assessment after nCRT, the combination of CTRW (α + D) yielded the best diagnostic performance (AUC = 0.877, P = 0.048). In LARC patients, imaging markers derived from CTRW show promise in predicting tumor response before nCRT and assessing pCR after nCRT.

The role of humerus cortical thickness in predicting osteoporosis in MR imaging.

This study aims to evaluate the efficacy of humerus cortical thickness on coronal T1-weighted images of the humerus in distinguishing patients with normal vs. abnormal bone mineral density (BMD). Patients (n:138) with shoulder magnetic resonance imaging (MRI) and dual-energy x-ray absorptiometry (DXA) scans were evaluated. Patients were grouped into normal and low BMD (osteopenia and osteoporosis) according to DXA. An average cortical bone thickness (CBTavg) and gauge cortical bone thickness (CBTg) were calculated from the proximal humerus on coronal T1W MRI. Sensitivity and specificity for the prediction of osteoporosis were determined for several cortical bone thickness thresholds. Proximal humerus average cortical bone thickness measurements strongly correlated with DXA femur and lumbar scores (p < 0.01). Gauge cortical thickness measurements also correlated with DXA femur and lumbar scores (p < 0.01). Average cortical bone thickness measurement of 4.52 mm was determined to be a potential marker for predicting osteoporosis, with a sensitivity of 92.3% and a specificity of 84.9%. Average cortical bone thickness measurements obtained from shoulder MRI are correlated with DXA. It appears to be effective in differentiating patients with normal and abnormal BMD and may help to opportunistically predict patients with osteoporosis in a rapid, simple and practical way, potentially guiding further diagnostic assessments.

Impact of Emerging Deep Learning-Based MR Image Reconstruction Algorithms on Abdominal MRI Radiomic Features.

This study aims to evaluate, on one MRI vendor's platform, the impact of deep learning (DL)-based reconstruction techniques on MRI radiomic features compared to conventional image reconstruction techniques. Under IRB approval and informed consent, we prospectively collected undersampled coronal T2-weighted MR images of the abdomen (1.5 T; Philips Healthcare) from 17 pediatric and adult subjects and reconstructed them using a conventional image reconstruction technique (compressed sensitivity encoding [C-SENSE]) and two DL-based reconstruction techniques (SmartSpeed [Philips Healthcare, US FDA cleared] and SmartSpeed with Super Resolution [SmartSpeed-SuperRes, not US FDA cleared to date]). Eight regions of interest (ROIs) across organs/tissues (liver, spleen, kidney, pancreas, fat, and muscle) were manually placed. Eighty-six MRI radiomic features were then extracted. Pearson's correlation coefficients (PCCs) and intraclass correlation coefficients (ICCs) were calculated between (A) C-SENSE versus SmartSpeed, and (B) C-SENSE versus SmartSpeed-SuperRes. To quantify the impact from the perspective of the whole MR image, cross-ROI mean PCCs and ICCs were calculated for individual radiomic features. The impact of image reconstruction on individual radiomic features in different organs/tissues was evaluated using ANOVA analyses. According to cross-ROI mean PCCs, 50 out of 86 radiomic features were highly correlated (PCC, ≥0.8) between SmartSpeed and C-SENSE, whereas only 15 radiomic features were highly correlated between SmartSpeed-SuperRes and C-SENSE reconstructions. According to cross-ROI mean ICCs, 58 out of 86 radiomic features had high agreements (ICC ≥0.75) between SmartSpeed and C-SENSE, whereas only 9 radiomic features had high agreements between SmartSpeed-SuperRes and C-SENSE reconstructions. For SmartSpeed reconstruction, the psoas muscle ROI appeared to be impacted most with the lowest median (IQR) correlation of 0.57 (0.25). The circular liver ROI was impacted most by SmartSpeed-SuperRes (PCC, 0.60 [0.22]). ANOVA analyses suggest that the impact of DL reconstruction algorithms on radiomic features varies significantly among different organs/tissues (P < 0.001). MRI radiomic features are significantly altered by DL-based reconstruction compared to a conventional reconstruction technique. The impact of DL reconstruction algorithms on radiomic features varies significantly between different organs/tissues.

Normative Measurements of the Infraorbital Nerve by Magnetic Resonance Imaging in an Australia Cohort.

To report the normative dimensions of the infraorbital nerve on fat-suppressed gadolinium-enhanced MRI and correlate with patient demographics in an Australian cohort. A retrospective review of patients who underwent coronal fat-suppressed gadolinium T1-weighted MRI from September 2021 to December 2023. One hundred sixty-eight orbits were included. The maximum diameter of the infraorbital nerve and the optic nerve sheath was measured. Orbits were excluded if there was unilateral or bilateral pathology of the infraorbital nerve or optic nerve sheath, incomplete MRI sequences, poor image quality, or indiscernible infraorbital nerve on radiological examination. The mean age of participants was 58 ± 16 years, and 50% were females (n = 42). The mean normative measurements (mean ± standard deviation) on coronal T1-weighted imaging: optic nerve sheath, 5.08 ± 0.67 mm. On coronal fat-suppressed gadolinium T1-weighted imaging: infraorbital nerve, 0.89 ± 0.22mm. No significant differences were found between male or female participants in both the infraorbital nerve (p = 0.757) or optic nerve sheath (p = 0.646). There was no significant correlation between age and mean diameter of the infraorbital nerve (r = 0.077, p = 0.320) or optic nerve sheath (r = 0.075, p = 0.336). Additionally, no significant difference was identified between the mean diameter of the infraorbital nerve (p = 0.079) and optic nerve sheath (p = 0.120) across age groups. The mean infraorbital nerve to optic nerve sheath ratio was 0.18 ± 0.00. Normative dimensions of the infraorbital nerve may be used to identify enlargement in conditions such as IgG4-related ophthalmic disease and reactive lymphoid hyperplasia.

Normal ovarian sizes on MRI in pediatric patients: a preliminary study.

Ovarian enlargement is one of several findings of pathology, including ovarian torsion. With increasing use of MRI for acute abdominal pain in children, data for normal ovary size and appearance are needed. To provide preliminary data on normal sizes of ovaries on MRI in pediatric patients. This retrospective IRB-approved study included girls (5 to 17years of age) with MRI examinations performed for indications not related to the ovaries from 2018 to 2022. For each MRI, coronal T2-weighted single shot fast spin echo and axial T2-weighted fat-saturated images were independently reviewed by three pediatric radiologists who recorded ovary visualization and ovarian linear measurements (3 planes). Ovarian volumes were calculated from linear measurements. Agreement among observers was calculated using kappa statistics and intraclass correlation coefficients. A total of 181 MRIs were reviewed. The left ovary was visualized in 166-176 (92-97%) cases (R1-R3) and the right ovary was visualized in 165-174 (91-96%) cases with excellent agreement among reviewers (left: K = 0.89 [0.84-0.94], right: K = 0.85 [0.79-0.91]). Interrater class coefficient (ICC) for largest single dimension of the ovary was left: 0.83 (CI 0.79-0.87) and right: 0.85 (CI 0.81-0.89). There were significant moderate to strong correlations between ovarian volume and age (left: 0.67 [0.58-0.75], right: 0.66 [0.57-0.74]). The ovaries can be adequately visualized and measured on MRI with excellent inter-reader agreement. This study serves as the foundation for developing normative values for ovarian volumes by age on MRI.

Automatically Detecting Pancreatic Cysts in Autosomal Dominant Polycystic Kidney Disease on MRI Using Deep Learning.

Pancreatic cysts in autosomal dominant polycystic kidney disease (ADPKD) correlate with PKD2 mutations, which have a different phenotype than PKD1 mutations. However, pancreatic cysts are commonly overlooked by radiologists. Here, we automate the detection of pancreatic cysts on abdominal MRI in ADPKD. Eight nnU-Net-based segmentation models with 2D or 3D configuration and various loss functions were trained on positive-only or positive-and-negative datasets, comprising axial and coronal T2-weighted MR images from 254 scans on 146 ADPKD patients with pancreatic cysts labeled independently by two radiologists. Model performance was evaluated on test subjects unseen in training, comprising 40 internal, 40 external, and 23 test-retest reproducibility ADPKD patients. Two radiologists agreed on 52% of cysts labeled on training data, and 33%/25% on internal/external test datasets. The 2D model with a loss of combined dice similarity coefficient and cross-entropy trained with the dataset with both positive and negative cases produced an optimal dice score of 0.7 ± 0.5/0.8 ± 0.4 at the voxel level on internal/external validation and was thus used as the best-performing model. In the test-retest, the optimal model showed superior reproducibility (83% agreement between scan A and B) in segmenting pancreatic cysts compared to six expert observers (77% agreement). In the internal/external validation, the optimal model showed high specificity of 94%/100% but limited sensitivity of 20%/24%. Labeling pancreatic cysts on T2 images of the abdomen in patients with ADPKD is challenging, deep learning can help the automated detection of pancreatic cysts, and further image quality improvement is warranted.

Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function

Purpose: Motion-induced magnetic resonance imaging (MRI) artifacts can deteriorate image quality and reduce diagnostic accuracy, but motion by human subjects is inevitable and can even be caused by involuntary physiological movements. Deep-learning-based motion correction methods might provide a solution. However, most studies have been based on directly applying existing models, and the trained models are rarely accessible. Therefore, we aim to develop and evaluate a deep-learning-based method (Motion Correction-Net, or MC-Net) for suppressing motion artifacts in brain MRI scans. Methods: A total of 57 subjects, providing 20,889 slices in four datasets, were used. Furthermore, 3T 3D sagittal magnetization-prepared rapid gradient-echo (MP-RAGE) and 2D axial fluid-attenuated inversion-recovery (FLAIR) sequences were acquired. The MC-Net was derived from a UNet combined with a two-stage multi-loss function. T1-weighted axial brain images contaminated with synthetic motions were used to train the network to remove motion artifacts. Evaluation used simulated T1- and T2-weighted axial, coronal, and sagittal images unseen during training, as well as T1-weighted images with motion artifacts from real scans. The performance indices included the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and visual reading scores from three blinded clinical readers. A one-sided Wilcoxon signed-rank test was used to compare reader scores, with p &lt; 0.05 considered significant. Intraclass correlation coefficients (ICCs) were calculated for inter-rater evaluations. Results: The MC-Net outperformed other methods in terms of PSNR and SSIM for the T1 axial test set. The MC-Net significantly improved the quality of all T1-weighted images for all directions (i.e., the mean SSIM of axial, sagittal, and coronal slices improved from 0.77, 0.64, and 0.71 to 0.92, 0.75, and 0.84; the mean PSNR improved from 26.35, 24.03, and 24.55 to 29.72, 24.40, and 25.37, respectively) and for simulated as well as real motion artifacts, both using quantitative measures and visual scores. However, MC-Net performed poorly for images with untrained T2-weighted contrast because the T2 contrast was unseen during training and is different from T1 contrast. Conclusion: The proposed two-stage multi-loss MC-Net can effectively suppress motion artifacts in brain MRI without compromising image quality. Given the efficiency of MC-Net (with a single-image processing time of ~40 ms), it can potentially be used in clinical settings.

Pineal gland volume loss in females with multiple sclerosis.

Multiple sclerosis has a complex pathophysiology, and numerous risk factors can contribute to its development, like exposure to sunlight that is associated with serum levels of melatonin. The aim of this study was to determine whether the volume of the pineal gland, assessed by magnetic resonance imaging (MRI), correlated with the presence of multiple sclerosis. This retrospective study included a total of 394 patients. Subjects were divided into two groups: the first group consisted of 188 patients with a definite diagnosis of multiple sclerosis (based on revised McDonald criteria) and the second group consisted of 206 healthy controls. To examine the influence of age on pineal gland volume, we stratified the whole sample into three age groups: first involved patients under 20 years, second patients between 20 and 40 years, and third group included patients over 40 years. The maximum length (L) and height (H) of the pineal gland were measured on the T1-weighted sagittal images, and the width (W) was measured on the T2-weighted coronal or axial images. The volume of the gland was calculated as an approximation to an ellipse, according to the formula V = (L × H × W)/2. Pineal gland volume of female multiple sclerosis (MS) patients (N = 129) was significantly lower than in healthy females (N = 123) (p = 0.013; p < 0.05), unlike in males where there is not such difference. Also, pineal gland volume is not age-dependent, and the observed smaller pineal gland in MS patients can reliably be attributed to the disease itself. Additionally, large pineal gland size, especially over 62.83 mm3 when compared to pineal gland volume below 31.85 mm3 is associated with more than double reduced risk of multiple sclerosis (OR 0.42; p = 0.003). Our results suggest that women with multiple sclerosis have smaller pineal glands that can theoretically be explained by a lack of input stimuli and the resultant decrease in gland volume. Additionally, the risk of multiple sclerosis is reduced in larger pineal gland volumes.

Machine Learning-Based Perivascular Space Volumetry in Alzheimer Disease.

Impaired perivascular clearance has been suggested as a contributing factor to the pathogenesis of Alzheimer disease (AD). However, it remains unresolved when the anatomy of the perivascular space (PVS) is altered during AD progression. Therefore, this study investigates the association between PVS volume and AD progression in cognitively unimpaired (CU) individuals, both with and without subjective cognitive decline (SCD), and in those clinically diagnosed with mild cognitive impairment (MCI) or mild AD. A convolutional neural network was trained using manually corrected, filter-based segmentations (n = 1000) to automatically segment the PVS in the centrum semiovale from interpolated, coronal T2-weighted magnetic resonance imaging scans (n = 894). These scans were sourced from the national German Center for Neurodegenerative Diseases Longitudinal Cognitive Impairment and Dementia Study. Convolutional neural network-based segmentations and those performed by a human rater were compared in terms of segmentation volume, identified PVS clusters, as well as Dice score. The comparison revealed good segmentation quality (Pearson correlation coefficient r = 0.70 with P < 0.0001 for PVS volume, detection rate in cluster analysis = 84.3%, and Dice score = 59.0%). Subsequent multivariate linear regression analysis, adjusted for participants' age, was performed to correlate PVS volume with clinical diagnoses, disease progression, cerebrospinal fluid biomarkers, lifestyle factors, and cognitive function. Cognitive function was assessed using the Mini-Mental State Examination, the Comprehensive Neuropsychological Test Battery, and the Cognitive Subscale of the 13-Item Alzheimer's Disease Assessment Scale. Multivariate analysis, adjusted for age, revealed that participants with AD and MCI, but not those with SCD, had significantly higher PVS volumes compared with CU participants without SCD ( P = 0.001 for each group). Furthermore, CU participants who developed incident MCI within 4.5 years after the baseline assessment showed significantly higher PVS volumes at baseline compared with those who did not progress to MCI ( P = 0.03). Cognitive function was negatively correlated with PVS volume across all participant groups ( P ≤ 0.005 for each). No significant correlation was found between PVS volume and any of the following parameters: cerebrospinal fluid biomarkers, sleep quality, body mass index, nicotine consumption, or alcohol abuse. The very early changes of PVS volume may suggest that alterations in PVS function are involved in the pathophysiology of AD. Overall, the volumetric assessment of centrum semiovale PVS represents a very early imaging biomarker for AD.

Prognostic value of radiological T category using conventional MRI in patients with oral tongue cancer: comparison with pathological T category

PurposeThis study aimed to compare the radiological tumor (T)-category using multiparametric MRI with the pathological T category in patients with oral tongue squamous cell carcinoma (OTSCC) and to examine which is a better predictor of prognosis.MethodsThis retrospective study included 110 consecutive patients with surgically resected primary OTSCC who underwent preoperative contrast-enhanced MRI. T categories determined by maximum diameter and depth of invasion were retrospectively assessed based on the pathological specimen and multiparametric MRI. The MRI assessment included the axial and coronal T1-weighted image (T1WI), axial T2-weighted image (T2WI), coronal fat-suppressed T2WI, and axial and coronal fat-suppressed contrast-enhanced T1WI (CET1WI). Axial and coronal CET1WI measurements were divided into two groups: measurements excluding peritumoral enhancement (MEP) and measurements including peritumoral enhancement. The prognostic values for recurrence and disease-specific survival after radiological and pathological T categorization of cases into T1/T2 and T3/T4 groups were compared.ResultsThe T category of MEP on coronal CET1WI was the most relevant prognostic factor for recurrence [hazard ratio (HR) = 3.30, p = 0.001] and the HR was higher than the HR for pathological assessment (HR = 2.26, p = 0.026). The T category determined by MEP on coronal CET1WI was also the most relevant prognostic factor for disease-specific survival (HR = 3.12, p = 0.03), and the HR was higher than the HR for pathological assessment (HR = 2.02, p = 0.20).ConclusionThe T category determined by MEP on the coronal CET1WI was the best prognostic factor among all radiological and pathological T category measurements.

The diagnostic value of abbreviated MRI protocol in the surveillance of Branch-Duct intraductal papillary mucinous neoplasm

PurposeTo assess the diagnostic value of abbreviated protocol (AP) MRI to detect the degeneration signs in branch-duct intraductal papillary mucinous neoplasms (BD-IPMNs) in patients undergoing a routine MRI follow-up. MethodsThis dual-center retrospective study include patients with BD-IPMN diagnosed on initial comprehensive protocol (CP) MRI who underwent routine MRI follow-up. CP included axial and coronal T2-weighted images (T2WI), axial T1-weighted images (T1WI) before and after contrast administration, 3D MR cholangiopancreatography (MRCP) and diffusion-weighted images (DWI). Two APs, eliminating dynamic sequences ± DWI, were extracted from CP. Two radiologists evaluated the APs separately for IPMN degeneration signs according to Fukuoka criteria and compared the results to the follow-up CP. In patients who underwent EUS, imaging findings were correlated with pathological results. Per-patient and per-lesion sensitivity, specificity, PPV, NPV, and accuracy of APs were calculated. Additionally, the acquisition time for different protocols was calculated. ResultsOne hundred-fourteen patients (56.1 % women, median age: 71 years) with 256 lesions were included. Degeneration signs were observed in 24.6 % and 12.1 % per-patient and per-lesion, respectively. Regarding APs, the per patient sensitivity, specificity, PPV, NPV, and accuracy in the detection of the degeneration signs were 100 %, 93.5 %, 83.3 %, 100 %, and 95.1 %, respectively. No additional role for DWI was detected. AP without DWI economized nearly half of CP acquisition time (388 versus 663 s, respectively). ConclusionAP can confidently replace CP for BD-IPMN follow-up with high sensitivity and PPV while offering benefits such as patient comfort, improved MRI accessibility, and reduced dedicated time for image analysis. DWI necessitates special consideration. Clinical Relevance StatementOur data suggest that APs safely detect all degeneration signs of IPMN. While there is an overestimation of mural nodules due to the lack of contrast injection, this occurs in a negligible number of patients.

Deep learning-based reconstruction enhances image quality and improves diagnosis in magnetic resonance imaging of the shoulder joint.

Accelerated magnetic resonance imaging sequences reconstructed using the vendor-provided Recon deep learning algorithm (DL-MRI) were found to be more likely than conventional magnetic resonance imaging (MRI) sequences to detect subacromial (SbA) bursal thickening. However, the extent of this thickening was not extensively explored. This study aimed to compare the image quality of DL-MRI with conventional MRI sequences reconstructed via conventional pipelines (Conventional-MRI) for shoulder examinations and evaluate the effectiveness of DL-MRI in accurately demonstrating the degree of SbA bursal and subcoracoid (SC) bursal thickening. This prospective cross-sectional study enrolled 41 patients with chronic shoulder pain who underwent 3-T MRI (including both Conventional-MRI and accelerated MRI sequences) of the shoulder between December 2022 and April 2023. Each protocol consisted of oblique axial, coronal, and sagittal images, including proton density-weighted imaging (PDWI) with fat suppression (FS) and oblique coronal T1-weighted imaging (T1WI) with FS. The image quality and degree of artifacts were assessed using a 5-point Likert scale for both Conventional-MRI and DL-MRI. Additionally, the degree of SbA and SC bursal thickening, as well as the relative signal-to-noise ratio (rSNR) and relative contrast-to-noise ratio (rCNR) were analyzed using the paired Wilcoxon test. Statistical significance was defined as P<0.05. The utilization of accelerated sequences resulted in a remarkable 54.7% reduction in total scan time. Overall, DL-MRI exhibited superior image quality scores and fewer artifacts compared to Conventional-MRI. Specifically, DL-MRI demonstrated significantly higher measurements of SC bursal thickenings in the oblique sagittal PDWI sequence compared to Conventional-MRI [3.92 (2.83, 5.82) vs. 3.74 (2.92, 5.96) mm, P=0.028]. Moreover, DL-MRI exhibited higher detection of SbA bursal thickenings in the oblique coronal PDWI sequence [2.61 (1.85, 3.46) vs. 2.48 (1.84, 3.25) mm], with a notable trend towards significant differences (P=0.071). Furthermore, the rSNRs of the muscle in DL-MRI images were significantly higher than those in Conventional-MRI images across most sequences (P<0.001). However, the rSNRs of bone on Conventional-MRI of oblique axial and oblique coronal PDWI sequences showed adverse results [oblique axial: 1.000 (1.000, 1.000) vs. 0.444 (0.367, 0.523); and oblique coronal: 1.000 (1.000, 1.000) vs. 0.460 (0.387, 0.631); all P<0.001]. Additionally, all DL-MRI images exhibited significantly greater rSNRs and rCNRs compared to accelerated MRI sequences reconstructed using traditional pipelines (P<0.001). In conclusion, the utilization of DL-MRI enhances image quality and improves diagnostic capabilities, making it a promising alternative to Conventional-MRI for shoulder imaging.

The rat as a novel model for chronic rotator cuff injuries

Chronic rotator cuff injuries (CRCIs) still present a great challenge for orthopaedics surgeons. Many new therapeutic strategies are developed to facilitate repair and improve the healing process. However, there is no reliable animal model for chronic rotator cuff injury research. To present a new valuable rat model for future chronic rotator cuff injuries (CRCIs) repair studies, and describe the changes of CRCIs on the perspectives of histology, behavior and MRI. Sixty male Wistar rats were enrolled and underwent surgery of the left shoulder joint for persistent subacromial impingement. They were randomly divided into experimental group (n = 30, a 3D printed PEEK implant shuttled into the lower surface of the acromion) and sham operation group (n = 30, insert the same implant, but remove it immediately). Analyses of histology, behavior, MRI and inflammatory pain-related genes expression profiles were performed to evaluate the changes of CRCIs. After 2-weeks running, the rats in the experimental group exhibited compensatory gait patterns to protect the injured forelimb from loading after 2-weeks running. After 8-weeks running, the rats in the experimental group showed obvious CRCIs pathological changes: (1) acromion bone hyperplasia and thickening of the cortical bone; (2) supraspinatus muscle tendon of the humeral head: the bursal-side tendon was torn and layered with disordered structure, forming obvious gaps; the humeral-side tendon is partially broken, and has a neatly arranged collagen. Partial fat infiltration is found. The coronal T2-weighted images showed that abnormal tendon-to-bone junctions of the supraspinatus tendon. The signal intensity and continuity were destroyed with contracted tendon. At the nighttime, compared with the sham operation group, the expression level of IL-1β and COX-2 increased significantly (P = 0063, 0.0005) in the experimental group. The expression of COX-2 in experimental group is up-regulated about 1.5 times than that of daytime (P = 0.0011), but the expression of IL-1β, TNF-a, and NGF are all down-regulated (P = 0.0146, 0.0232, 0.0161). This novel rat model of chronic rotator cuff injuries has the similar characteristics with that of human shoulders. And it supplies a cost-effective, reliable animal model for advanced tissue engineered strategies and future therapeutic strategies.

Faster acquisition of magnetic resonance imaging sequences of the knee via deep learning reconstruction: a volunteer study

To evaluate whether deep learning reconstruction (DLR) can accelerate the acquisition of magnetic resonance imaging (MRI) sequences of the knee for clinical use. Using a 1.5-T MRI scanner, sagittal fat-suppressed T2-weighted imaging (fs-T2WI), coronal proton density-weighted imaging (PDWI), and coronal T1-weighted imaging (T1WI) were performed. DLR was applied to images with a number of signal averages (NSA) of 1 to obtain 1DLR images. Then 1NSA, 1DLR, and 4NSA images were compared subjectively, and by noise (standard deviation of intra-articular water or medial meniscus) and contrast-to-noise ratio between two anatomical structures or between an anatomical structure and intra-articular water. Twenty-seven healthy volunteers (age: 40.6±11.9 years) were enrolled. Three 1DLR image sequences were obtained within 200 s (approximately 12 minutes for 4NSA image). According to objective evaluations, PDWI 1DLR images showed the smallest noise and significantly higher contrast than 1NSA and 4NSA images. For fs-T2WI, smaller noise and higher contrast were observed in the order of 4NSA, 1DLR, and 1NSA images. According to the subjective analysis, structure visibility, image noise, and overall image quality were significantly better for PDWI 1DLR than 1NSA images; moreover, the visibility of the meniscus and bone, image noise, and overall image quality were significantly better for 1DLR than 4NSA images. Fs-T2WI and T1WI 1DLR images showed no difference between 1DLR and 4NSA images. Compared to PDWI 4NSA images, PDWI 1DLR images were of higher quality, while the quality of fs-T2WI and T1WI 1DLR images was similar to that of 4NSA images.

Towards validation in clinical routine: a comparative analysis of visual MTA ratings versus the automated ratio between inferior lateral ventricle and hippocampal volumes in Alzheimer’s disease diagnosis

PurposeTo assess the performance of the inferior lateral ventricle (ILV) to hippocampal (Hip) volume ratio on brain MRI, for Alzheimer’s disease (AD) diagnostics, comparing it to individual automated ILV and hippocampal volumes, and visual medial temporal lobe atrophy (MTA) consensus ratings.MethodsOne-hundred-twelve subjects (mean age ± SD, 66.85 ± 13.64 years) with varying degrees of cognitive decline underwent MRI using a Philips Ingenia 3T. The MTA scale by Scheltens, rated on coronal 3D T1-weighted images, was determined by three experienced radiologists, blinded to diagnosis and sex. Automated volumetry was computed by icobrain dm (v. 5.10) for total, left, right hippocampal, and ILV volumes. The ILV/Hip ratio, defined as the percentage ratio between ILV and hippocampal volumes, was calculated and compared against a normative reference population (n = 1903). Inter-rater agreement, association, classification accuracy, and clinical interpretability on patient level were reported.ResultsVisual MTA scores showed excellent inter-rater agreement. Ordinal logistic regression and correlation analyses demonstrated robust associations between automated brain segmentations and visual MTA ratings, with the ILV/Hip ratio consistently outperforming individual hippocampal and ILV volumes. Pairwise classification accuracy showed good performance without statistically significant differences between the ILV/Hip ratio and visual MTA across disease stages, indicating potential interchangeability. Comparison to the normative population and clinical interpretability assessments showed commensurability in classifying MTA “severity” between visual MTA and ILV/Hip ratio measurements.ConclusionThe ILV/Hip ratio shows the highest correlation to visual MTA, in comparison to automated individual ILV and hippocampal volumes, offering standardized measures for diagnostic support in different stages of cognitive decline.

Neuroimaging of brain tumors: back to the standardized MRI-protocol

Despite the widespread application of MRI in brain tumor MR-imaging, the standardized MRI protocol has not yet been approved. This fact creates obstacles for interpreting of the obtained data and even limits the diagnostic power of MRI. Standardization of MRI studies facilitates successful planning of surgery, chemotherapy, or radiation therapy and provides more reliable follow-up. In this regard, unification of image acquisition parameters and protocols remains relevant. We propose a standard MRI protocol that includes axial and coronal T2-weighted images, axial or 3D T2-FLAIR, 3D T2* (gradient echo), diffusion-weighted imaging, axial T1-weighted images, contrast-enhanced spin-echo T1-weighted images (either axial or three planes) and obligatory subsequent isotropic 3D T1 images. The following combination of sequences facilitates the correct diagnosis and follow-up. The least magnetic field strength should be 1.5 T.