Magnetic Resonance Imaging Sequences Research Articles

Synthetic [18F]FET-PET Generation and Hotspot Prediction Via Preoperative MRI Fusion of Glioma Lacking Radiographic High-Grade Characteristics

Abstract Background Limited amino acid availability for Positron Emission Tomography (PET) imaging hinders therapeutic decision-making for gliomas without typical high-grade imaging features. To address this gap, we evaluated a generative artificial intelligence (AI) approach for creating synthetic [18F]FET-PET and predicting high [18F]FET-uptake from magnetic resonance imaging (MRI). Methods We trained a deep learning (DL)-based model to segment tumors in MRI and extracted radiomic features using the pyradiomics package and classification with a Random Forest classifier. To generate [18F]FET-PET images, we employed a Generative Adversarial Network (GAN) framework and utilized a split-input fusion module for processing different MRI sequences through feature extraction, concatenation, and self-attention. Results We included MR and PET images from 215 studies for the hotspot classification and 211 studies for the synthetic PET generation task. The top-performing radiomic features achieved 80% accuracy for hotspot prediction. From the synthetic [18F]FET-PET, 85% were classified as clinically useful by senior physicians. Peak Signal-to-Noise Ratio analysis indicated high signal fidelity with a peak at 40 dB, while Structural Similarity Index values showed structural congruence. Root Mean Square Error analysis demonstrated lower values below 5.6. Most Visual Information Fidelity scores ranged between 0.6 and 0.7. This indicates that synthetic PET images retain the essential information required for clinical assessment and diagnosis. Conclusion For the first time, we demonstrate that predicting high [18F]FET-uptake and generating synthetic PET images from preoperative MRI in LGG and HGG is feasible. Advanced MRI modalities and other generative AI models will be used to improve the algorithm further in future studies.

Audiovisual Breathing Guidance for Improved Image Quality and Scan Efficiency of T2- and Diffusion-Weighted Liver MRI

Objectives Impaired image quality and long scan times frequently occur in respiratory-triggered sequences in liver magnetic resonance imaging (MRI). We evaluated the impact of an in-bore active breathing guidance (BG) application on image quality and scan time of respiratory-triggered T2-weighted (T2) and diffusion-weighted imaging (DWI) by comparing sequences with standard triggering (T2S and DWIS) and with BG (T2BG and DWIBG). Materials and Methods In this prospective study, random patients with clinical indications for liver MRI underwent 3 T MRI with standard and BG acquisitions. The audiovisual BG application received the respiratory signal from the scanner, and animated breathing instructions were displayed using a mirror and screen behind the MRI bore. Prior to the DWIBG and T2BG acquisition, patients received a short video instruction about MRI with BG. Suitable parameters for desired breathing pattern for T2BG and DWIBG were set individually for each patient based on the patient's physical respiratory ability (ie, 4 seconds breathing followed by 4.5 seconds breath holding). Artifacts, sharpness, lesion conspicuity, and overall image quality were assessed using a Likert scale from 1 (nondiagnostic) to 5 (excellent). Scan time, apparent contrast-to-noise ratio, and apparent signal-to-noise ratio (aSNR) for all sequences were analyzed. Paired t test and Wilcoxon test were used for statistical analysis. Results Thirty-two patients (mean age: 55 ± 13 years, 13 female) were included. T2BG showed less artifacts (4.5 ± 0.7 vs 4.1 ± 0.8, P < 0.001) and better sharpness, lesion conspicuity, and overall image quality (eg, overall image quality 4.6 ± 0.7 vs 4.4 ± 0.7, P = 0.004) compared with T2S. DWIBG demonstrated improved image quality in all categories compared with DWIS (eg, overall image quality 4.5 ± 0.5 vs 4.3 ± 0.5, P = 0.005) and less artifacts (4.1 ± 0.5 vs 3.8 ± 0.7, P = 0.007). Scan times of T2BG (286 ± 23 vs 345 ± 68 seconds, P < 0.001) and DWIBG (160 ± 4 vs 252 ± 70 seconds, P < 0.001) were reduced by 17% and 37%, respectively. aSNR and apparent contrast-to-noise ratio (eg, aSNR: 23.45 ± 11.31 [T2BG] vs 25.84 ± 10.76 [T2S]; P = 0.079) were similar for both sequences for both approaches. Conclusions Active BG for respiratory-triggered liver T2w and DWI sequences led to significant reduction of breathing artifacts, improved image quality, and shorter scan time compared with standard acquisitions.

Comparison of diagnostic performance for pulmonary nodule detection between free-breathing spiral ultrashort echo time and free-breathing radial volumetric interpolated breath-hold examination

ObjectiveThis study aims to evaluate the efficacy of two free-breathing magnetic resonance imaging (MRI) sequences—spiral ultrashort echo time (spiral UTE) and radial volumetric interpolated breath-hold examination (radial VIBE).MethodsPatients were prospectively enrolled between February 2021 and September 2022. All participants underwent both 3T MRI scanning, utilizing the radial VIBE sequence and spiral UTE sequence, as well as standard chest CT imaging. The CT and MRI examinations were conducted within a 7-day interval. Two radiologists assessed the image quality using a visual 5-point ordinal Likert scale, and pulmonary nodules identified on MRI were evaluated through comparison with CT as the reference standard.ResultsA total of 52 patients participated in this study, during which 82 pulmonary nodules were detected via CT imaging. The image quality scores for depicting pulmonary vasculature and airways using the spiral UTE sequence (4.61 ± 0.63; 4.76 ± 0.48) were significantly higher than those for the radial VIBE sequence (4.27 ± 0.87; 4.14 ± 0.82) (P < 0.05). However, for nodules smaller than 6 mm, the detection rate for the spiral UTE sequence (82.61%) was notably higher than that of the radial VIBE sequence (39.13%) (P < 0.05). Additionally, the detection rate for ground-glass nodules was higher with the spiral UTE sequence (75.00%) compared to the radial VIBE sequence (17.86%) (P < 0.05). The Pearson correlation coefficient (r) between radial VIBE and CT was 0.99 (P < 0.001), and the Pearson correlation coefficient (r) between spiral UTE and CT was also 0.99 (P < 0.001).ConclusionThe spiral UTE sequence demonstrates superior capability in visualizing ground glass nodules, blood vessels, and airways. In cases where patients present with ground glass nodules, the spiral UTE sequence is the preferred choice. Conversely, when the nodules are solid or partially solid, it is advisable to opt for radial VIBE sequences that are time-efficient and exhibit fewer artifacts.

The Effect of Pacemakers and Defibrillators on Distortion in 2 Magnetic Resonance Imaging (MRI) Sequences Commonly Used in Radiation Oncology Practice—3D True Fast Imaging with Steady State Precession (TrueFISP) at 0.35T MR-Linear Accelerator (LINAC) and 3D T1 at 3T MR Simulator

Background: We aimed to measure the pacemaker- and defibrillator-induced distortion at 0.35T and 3.0T magnetic fields. Methods: The pacemaker/defibrillator was placed at the top center of a water-filled/MagPhan phantom, followed by a T1 scan at 3T and a TrueFISP scan at 0.35T. The extent of distortion (i.e., the distance from the device to the furthest signal loss/void/rings) in the water-filled phantom was measured in MIM. For geometrical distortion (i.e., dislocation of geometrical structures), the spheres in the MagPhan phantom were contoured and their distortion was calculated based on their manufacturing coordinate positions. Results: The maximum extent of distortion caused by the defibrillator was 18.8 cm at 0.35T and 5.8 cm at 3.0T. Similarly, the maximum extent of distortion caused by the pacemaker was 9.28 cm at 0.35T and 2.8 cm at 3.0T. Geometrical distortion measurements using the MagPhan phantom showed that the maximum distortion caused by the defibrillator was 12.8 mm at 0.35T and 13.2 mm at 3.0T. Likewise, the maximum distortion caused by the pacemaker was 8.7 mm at 0.35T and 6.0 mm at 3.0T. Conclusions: Defibrillators cause larger distortions/signal voids than pacemakers, and require careful consideration when performing MRI-based treatment planning. To minimize distortion, sequences with lower sensitivity to magnetic field inhomogeneity should be used.

Faster Acquisition and Improved Image Quality of T2-Weighted Dixon Breast MRI at 3T Using Deep Learning: A Prospective Study.

The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2DL) and a conventional T2-w FSE Dixon sequence (T2STD) for breast magnetic resonance imaging (MRI). This prospective study was conducted between November 2022 and April 2023 using a 3T scanner. Both T2DL and T2STD sequences were acquired for each patient. Quantitative analysis was based on region-of-interest (ROI) measurements of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Qualitative analysis was performed independently by two radiologists using Likert scales to evaluate various image quality features, morphology, and diagnostic confidence for cysts and breast cancers. Reader preference between T2DL and T2STD was assessed via side-by-side comparison, and inter-reader reliability was also analyzed. Total of 151 women were enrolled, with 140 women (mean age: 52 ± 14 years; 85 cysts and 31 breast cancers) included in the final analysis. The acquisition time was 110 s ± 0 for T2DL compared to 266 s ± 0 for T2STD. SNR and CNR were significantly higher in T2DL (P < 0.001). T2DL was associated with higher image quality scores, reduced noise, and fewer artifacts (P < 0.001). All evaluated anatomical regions (breast and axilla), breast implants, and bone margins were rated higher in T2DL (P ≤ 0.008), except for bone marrow, which scored higher in T2STD (P < 0.001). Scores for conspicuity, sharpness/margins, and microstructure of cysts and breast cancers were higher in T2DL (P ≤ 0.002). Diagnostic confidence for cysts was improved with T2DL (P < 0.001). Readers significantly preferred T2DL over T2STD in side-by-side comparisons (P < 0.001). T2DL effectively corrected for SNR loss caused by accelerated image acquisition and provided a 58% reduction in acquisition time compared to T2STD. This led to fewer artifacts and improved overall image quality. Thus, T2DL is feasible and has the potential to replace conventional T2-w sequences for breast MRI examinations.

Ultrafast MRI for Pediatric Brain Assessment in Routine Clinical Practice.

To assess the feasibility of ultrafast brain magnetic resonance imaging (MRI) in pediatric patients. We retrospectively reviewed 194 pediatric patients aged 0 to 19 years (median 10.2 years) who underwent both ultrafast and conventional brain MRI between May 2019 and August 2020. Ultrafast MRI sequences included T1 and T2-weighted images (T1WI and T2WI), fluid-attenuated inversion recovery (FLAIR), T2*-weighted image (T2*WI), and diffusion-weighted image (DWI). Qualitative image quality and lesion evaluations were conducted on 5-point Likert scales by two blinded radiologists, with quantitative assessment of lesion count and size on T1WI, T2WI, and FLAIR sequences for each protocol. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) analyses were used for comparison. The total scan times for equivalent image contrasts were 1 minute 44 seconds for ultrafast MRI and 15 minutes 30 seconds for conventional MRI. Overall, image quality was lower in ultrafast MRI than in conventional MRI, with mean quality scores ranging from 2.0 to 4.8 for ultrafast MRI and 4.8 to 5.0 for conventional MRI across sequences (P < 0.001 for T1WI, T2WI, FLAIR, and T2*WI for both readers; P = 0.018 [reader 1] and 0.031 [reader 2] for DWI). Lesion detection rates on ultrafast MRI relative to conventional MRI were as follows: T1WI, 97.1%; T2WI, 99.6%; FLAIR, 92.9%; T2*WI, 74.1%; and DWI, 100%. The ICC (95% confidence interval) for lesion size measurements between ultrafast and conventional MRI was as follows: T1WI, 0.998 (0.996-0.999); T2WI, 0.998 (0.997-0.999); and FLAIR, 0.99 (0.985-0.994). Ultrafast MRI significantly reduces scan time and provides acceptable results, albeit with slightly lower image quality than conventional MRI, for evaluating intracranial abnormalities in pediatric patients.

Key MRI Features in Acute Stroke: Insights from an MRI-First Approach at a Tertiary Care Centre

Magnetic Resonance Imaging (MRI) has emerged as a crucial tool in acute stroke management, offering superior sensitivity and specificity in detecting early ischemic changes. This review explores key MRI features in acute stroke based on insights from an MRI-first approach implemented at Universiti Putra Malaysia's tertiary care center. The Putra Acute Stroke Protocol, an optimized 8-minute MRI protocol, has demonstrated the feasibility of prioritizing MRI in hyperacute stroke settings. The manuscript discusses essential MRI sequences, including Diffusion-Weighted Imaging (DWI), Fluid-Attenuated Inversion Recovery (FLAIR), and Susceptibility-Weighted Imaging (SWI). It highlights the diagnostic and prognostic significance of specific imaging markers such as the FLAIR Vascular Hyperintensity Sign (FVHS), Susceptibility Vessel Sign (SVS), and Prominent Vein Sign (PVS). These features provide crucial information about infarct core, penumbra, collateral circulation, and thrombus composition, guiding treatment decisions and improving patient outcomes. The review also addresses the challenges and benefits of implementing an MRI-first policy, including concerns about delays and costs, while emphasizing its value in personalizing stroke management. By leveraging MRI's capabilities, clinicians can make more informed decisions about reperfusion therapies, potentially extending treatment windows for suitable candidates and improving overall stroke care.

SHIVA-CMB: a deep-learning-based robust cerebral microbleed segmentation tool trained on multi-source T2*GRE- and susceptibility-weighted MRI

Cerebral microbleeds (CMB) represent a feature of cerebral small vessel disease (cSVD), a prominent vascular contributor to age-related cognitive decline, dementia, and stroke. They are visible as spherical hypointense signals on T2*- or susceptibility-weighted magnetic resonance imaging (MRI) sequences. An increasing number of automated CMB detection methods being proposed are based on supervised deep learning (DL). Yet, the lack of open sharing of pre-trained models hampers the practical application and evaluation of these methods beyond specific data sources used in each study. Here, we present the SHIVA-CMB detector, a 3D Unet-based tool trained on 450 scans taken from seven acquisitions in six different cohort studies that included both T2*- and susceptibility-weighted MRI. In a held-out test set of 96 scans, it achieved the sensitivity, precision, and F1 (or Dice similarity coefficient) score of 0.67, 0.82, and 0.74, with less than one false positive detection per image (FPavg = 0.6) and per CMB (FPcmb = 0.15). It achieved a similar level of performance in a separate, evaluation-only dataset with acquisitions never seen during the training (0.67, 0.91, 0.77, 0.5, 0.07 for the sensitivity, precision, F1 score, FPavg, and FPcmb). Further demonstrating its generalizability, it showed a high correlation (Pearson’s R = 0.89, p < 0.0001) with a visual count by expert raters in another independent set of 1992 T2*-weighted scans from a large, multi-center cohort study. Importantly, we publicly share both the pipeline (https://github.com/pboutinaud/SHiVAi/) and pre-trained models (https://github.com/pboutinaud/SHIVA-CMB/) to the research community to promote the active application and evaluation of our tool. We believe this effort will help accelerate research on the pathophysiology and functional consequences of CMB by enabling rapid characterization of CMB in large-scale studies.

MRI in the evaluation of plantar plate disease: diagnostic value of the “stress test”

Abstract Introduction The plantar plate, also called the plantar ligament, is a fibrocartilaginous structure found in the metatarsophalangeal (MTP) and interphalangeal (IP) joints. Our study aimed to evaluate the role of magnetic resonance imaging (MRI) performed with the patient in the standard position or with joint hyperextension (the “stress test”, ST) in the study of plantar plate (PP) disease that involves metatarsophalangeal joints. Materials and methods All patients underwent forefoot MRI (Atroscan C, Esaote, Genoa, Italy), operating at 0.2 T. All patients first underwent a standard MRI examination (coronal T1 and T2 weighted image (WI) with fat suppression and axial and sagittal T2 WI); the examination was completed by performing a stress test (hyperextension of toes). The ST is an easy task to perform and is not time-consuming (requiring only one additional sagittal fast spin echo (FSE) T2-weighted MRI sequence; repetition time/ echo time (TR/TE): 3200/90 ms) for patients and operators. A 45°-dorsiflexion ST was performed for approximately 2.30 min, the time required to complete the sequence. No further diagnostic investigations were necessary; no patients underwent arthrography or arthro-MRI. The examinations were performed in a double-blind mode by two operators with proven experience in musculoskeletal radiology; no cases of intra-operator discordance were found. Results Twenty-five patients were recruited into our study over a 2-year period; 15 were positive for metatarsal pain and 10 were controls. Before treatment (surgery), all patients displaying symptoms underwent evaluation. As a result, the imaging features accurately represented the natural and actual conditions of the lesions. Among the symptomatic patients, 11 out of the 15 exhibited a PP tear or dysfunction in both the standard position and the ST. Additionally, two out of the 15 individuals displayed a tear in the ST alone, with no indication of it in the standard position. In contrast, two out of 15 patients showed no evidence of a PP tear in either the standard position or the ST. However, these two patients demonstrated dorsal subluxation during the ST, likely due to micro-instability resulting from PP failure. In the asymptomatic patients, nine out of the 10 individuals were found to be negative for PP dysfunction. Only one out of the 10 patients exhibited dorsal subluxation solely in the ST, indicative of plantar plate dysfunction, but no evidence of a tear in the PP. In the asymptomatic patients, standard MRI provided a specificity of 100% and a high negative predictive value (NPV) (90%), while the latter increased with the ST (specificity and NPV equal to 100%). In symptomatic patients, standard MRI gave a sensitivity of 75% when assessing a PP tear, which increased to 100% with the ST; the sensitivity of standard MRI the evaluation of MF subluxation was 60%, but it reached 100% with the ST. Conclusions In our study, by introducing the ST, the sensitivity in both the diagnosis of a PP tear and the evaluation of MTP subluxation reached 100% (a surgical assessment was performed on all positive patients for confirmation). Ultrasound has the advantage of being a non-invasive method. However, comparing the results of our study with the data available in the literature, ultrasound has a lower sensitivity and a negative predictive value. Also, ultrasound does not allow for the assessment of possible bone marrow oedema or the degree of concomitant arthritis. If other studies in the literature confirm these results, it will be possible to consider incorporating the ST into diagnostic practice in the future.

Automated classification of pathological differentiation in head and neck squamous cell carcinoma using combined radiomics models from CET1WI and T2WI.

This study aims to develop an automated radiomics-based model to grade the pathological differentiation of head and neck squamous cell carcinoma (HNSCC) and to assess the influence of various magnetic resonance imaging (MRI) sequences on the model's performance. We retrospectively analyzed MRI data from 256 patients across two medical centers, including both contrast-enhanced T1-weighted images (CET1WI) and T2-weighted images (T2WI). Regions of interest were delineated for radiomics feature extraction, followed by dimensionality reduction. An XGBoost classifier was then employed to build the predictive model, with its classification efficiency assessed using receiver operating characteristic curves and the area under the curve (AUC). In validation cohort, the AUC (macro/micro) values for models utilizing CET1WI, T2WI, and the combination of CET1WI and T2WI were 0.801/0.814, 0.741/0.798, and 0.885/0.895, respectively. The AUC for the three differentiations, ranging from well-differentiated to poorly differentiated, were 0.867, 0.909, and 0.837, respectively. The macro/micro precision, recall, and F1 scores of 0.688/0.736, 0.744/0.828, and 0.685/0.779 for the CET1WI + T2WI model. This study demonstrates that constructing a radiomics model based on CET1WI and T2WI sequences can be used to predict the pathological differentiation grading of HNSCC patients. This study suggests that a radiomics model integrating CET1WI and T2WI MRI sequences can effectively predict the pathological differentiation of HNSCC, providing an alternative diagnostic approach through non-invasive preoperative methods.

Pediatric Neuroimaging of Multiple Sclerosis and Neuroinflammatory Diseases.

Using a pediatric-focused lens, this review article briefly summarizes the presentation of several demyelinating and neuroinflammatory diseases using conventional magnetic resonance imaging (MRI) sequences, such as T1-weighted with and without an exogenous gadolinium-based contrast agent, T2-weighted, and fluid-attenuated inversion recovery (FLAIR). These conventional sequences exploit the intrinsic properties of tissue to provide a distinct signal contrast that is useful for evaluating disease features and monitoring treatment responses in patients by characterizing lesion involvement in the central nervous system and tracking temporal features with blood-brain barrier disruption. Illustrative examples are presented for pediatric-onset multiple sclerosis and neuroinflammatory diseases. This work also highlights findings from advanced MRI techniques, often infrequently employed due to the challenges involved in acquisition, post-processing, and interpretation, and identifies the need for future studies to extract the unique information, such as alterations in neurochemistry, disruptions of structural organization, or atypical functional connectivity, that may be relevant for the diagnosis and management of disease.

Is the Swallow Tail Sign a Useful Imaging Biomarker in Clinical Neurology? A Systematic Review.

Loss of dorsolateral nigral hyperintensity (DNH) in iron-sensitive sequences of Magnetic Resonance Imaging (MRI), also described as "swallow tail sign" (STS) loss, has shown promising diagnostic value in Parkinson's Disease (PD) and Atypical Parkinsonian Syndromes (APS). To conduct a bibliometric analysis on substantia nigra MRI and a systematic review on the clinical utility of STS visual assessment on Susceptibility-Weighted Imaging in various clinical entities. VOSviewer's keyword co-occurrence network was employed using Web of Science (WOS). Complying with the PRISMA statement, we searched MEDLINE, WOS, SCOPUS, ProQuest and Google Scholar for peer-reviewed studies conducted in vivo, excluding quantitative imaging techniques. DNH is a relatively novel parameter in substantia nigra MRI literature. Our SWI-focused review included 42 studies (3281 patients). Diagnostic accuracy of STS loss for PD/APS differentiation from controls and for Lewy Body Dementia differentiation from other dementias was 47.8-98.5% and 76-90%, respectively, with poorer capacity, however, in delineating PD from APS. STS evaluation in idiopathic REM sleep behavior disorder, a sign of prodromal PD, was typically concordant with nuclear scans, identifying subjects with high conversion risk. Iron deposition can affect STS in Multiple Sclerosis and STS loss in Amyotrophic Lateral Sclerosis is linked with multisystem degeneration, with poorer prognosis. In healthy individuals iron-induced microvessel changes are suspected for false positive results. STS assessment exhibits potential in different settings, with a possibly intermediate role in the diagnostic work-up of various conditions. Its clinical utility should be explored further, through standardized MRI protocols on larger cohorts.

Deep Depthwise Residual Network for Knee Meniscus Segmentation From Magnetic Resonance Imaging

ABSTRACTThe menisci within the knee are essential for various anatomical functions, including load‐bearing, joint stability, cartilage protection, shock absorption, and lubrication. Magnetic resonance imaging (MRI) provides highly detailed images of internal organs and soft tissues, which are indispensable for physicians and radiologists assessing the meniscus. Given the multitude of images in each MRI sequence and diverse MRI data, the segmentation of the meniscus presents considerable challenges through image processing methods. The region‐specific characteristics of the meniscus can vary from one image to another within the sequence. Consequently, achieving automatic and accurate segmentation of meniscus in knee MRI images is a crucial step in meniscus analysis. This paper introduces the “UNet with depthwise residual network” (DR‐UNet), a depthwise convolutional neural network, designed specifically for meniscus segmentation in MRI images. The proposed architecture significantly improves the accuracy of meniscus segmentation compared to different segmentation networks. The training and testing phases utilized fat suppression turbo‐spin‐echo (FS TSE) MRI sequences collected from 100 distinct knee joints using a Siemens 3 Tesla MRI machine. Additionally, we employed data augmentation techniques to expand the dataset strategically, addressing the challenge of a substantial training dataset requirement. The DR‐UNet model demonstrated impressive meniscus segmentation performance, achieving a Dice similarity coefficient range of 0.743–0.9646 and a Jaccard index range of 0.653–0.869, thereby showcasing its advanced segmentation capabilities.

Comparing the Accuracy and Reliability of Detecting the Intensity of Spinal Inflammation on STIR Sequence with Apparent Diffusion Coefficient Values in Axial Spondyloarthritis

Objective: To compare the accuracy and reliability of detecting the intensity of spinal inflammation on short tau inversion recovery (STIR) with the apparent diffusion coefficient (ADC) values of the active magnetic resonance imaging (MRI) lesions in axial spondyloarthritis (axSpA). Materials and methods: Fifty active lesions in the STIR sequence of spinal MRI were identified. With reference to sites of active lesions in STIR, the corresponding region of interest (ROI) on the ADC map was drawn to determine the maximum ADC (ADC[Formula: see text]), mean ADC (ADC[Formula: see text]), normalized maximum (nADC[Formula: see text]), and mean (nADC[Formula: see text]). Four independent readers scored the identified active lesions as “intense” or “non-intense” according to the Spondyloarthritis Research Consortium of Canada (SPARCC) MRI index. They were compared to various ADC parameters for assessment of accuracy and reliability. Regression analyses were used to adjust potential factors that could affect ADC. Results: Significant differences were found in ADC[Formula: see text] between “intense” and “non-intense” lesions scored by three of the four readers (1,405.7 ± 271.4 vs. 1,165.8 ± 223.8, [Formula: see text] = 0.01; 1,420.7 ± 272.1 vs. 1,209.0 ± 248.5, [Formula: see text] = 0.01; 1,438.0 ± 307.2 vs. 1,213.6 ± 231.0, [Formula: see text] = 0.01). Only one reader could differentiate a difference in “intense” and “non-intense” lesions with respect to ADC[Formula: see text] (899.2 ± 248.3 vs. 711.0 ± 222.6, [Formula: see text] = 0.01) and nADC[Formula: see text] (4.4 ± 2.1 vs. 3.4 ± 1.4, [Formula: see text] = 0.05). Inter-reader agreements were slight to moderate (kappa = 0.07–0.45). Reliability substantially improved when only the lowest and highest 25th percentiles of ADC values were included (kappa = 0.17–0.75). Regression analyses showed that the “intense” lesions were associated with higher ADC values after adjustment for confounders. Conclusion: Reading of STIR MRI is limited by the lack of ability in differentiating subtle differences of spinal inflammation. ADC could be an alternative method.

Performance of respiratory gated 4D flow MRI with adaptive k-space reordering in healthy controls and aortic dissection: reproducibility and agreement with 2D phase contrast MRI.

A four-dimensional phase-contrast magnetic resonance imaging sequence with respiratory-controlled adaptive k-space reordering (ReCAR-4DPC) offers potential benefits of improved scan efficiency and motion robustness. The purpose of this study was to evaluate the reproducibility of flow measurement using this technique and to compare hemodynamic metrics obtained to two-dimensional phase contrast MRI (2DPC)-derived metrics of the thoracic aorta. ReCAR-4DPC was performed with identical scan parameters in 15 healthy volunteers (6M,9F, mean [range] 37 [23-47] years) and 11 patients with thoracic aortic dissection (6M,5F, 56 [31-81] years) and acquisition time was recorded. Peak systolic velocity (PSV), average flow (AF) and net forward volume (NFV) were quantified by two readers for ReCAR-4DPC at ascending, descending and diaphragmatic aorta levels. Reference standard 2DPC measurements at the same levels were performed by a separate experienced cardiovascular radiologist. ReCAR-4DPC intra-reader agreement, inter-reader agreement, inter-scan repeatability and concordance with 2DPC-derived metrics (all segments combined) were evaluated with Lin's concordance correlation coefficient (LCCC) and reduced major axis regression. The overall average ± SD MRI acquisition time of all subjects was 11:59 ± 3:57 min, with shorter average times (9:37 ± 1:57 min) in healthy volunteers compared to patients (15:13 ± 3:44 min). There was near-perfect intra-reader, inter-reader and inter-scan concordance (LCCC for all metrics > 0.97, > 0.98 and > 0.92 respectively) for ReCAR-4DPC. Concordance with 2DPC was also high (LCCC all > 0.89), with overall minimally lower PSV, AF and NFV values derived from ReCAR-4DPC compared to reference 2DPC derived metrics. ReCAR-4DPC is a reproducible and relatively fast approach for comprehensive measurement of thoracic aortic flow metrics, with robust correlation to conventional 2DPC.

An integrative nomogram based on MRI radiomics and clinical characteristics for prognosis prediction in cervical spinal cord Injury.

To construct a nomogram model based on magnetic resonance imaging (MRI) radiomics combined with clinical characteristics and evaluate its role and value in predicting the prognosis of patients with cervical spinal cord injury (cSCI). In this study, we assessed the prognosis of 168 cSCI patients using the American Spinal Injury Association (ASIA) scale and the Functional Independence Measure (FIM) scale. The study involved extracting radiomics features using both manually defined metrics and features derived through deep learning via transfer learning methods from MRI sequences, specifically T1-weighted and T2-weighted images (T1WI & T2WI). The feature selection was performed employing the least absolute shrinkage and selection operator (Lasso) regression across both radiomics and deep transfer learning datasets. Following this selection process, a deep learning radiomics signature was established. This signature, in conjunction with clinical data, was incorporated into a predictive model. The efficacy of the models was appraised using the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis (DCA) to assess their diagnostic performance. Comparing the effectiveness of the models by linking the AUC of each model, we chose the best-performance radiomics model with clinical model to create the final nomogram. Our analysis revealed that, in the testing cohort, the combined model achieved an AUC of 0.979 for the ASIA and 0.947 for the FIM. The training cohort showed more promising performance, with an AUC of 0.957 for ASIA and 1.000 for FIM. Furthermore, the calibration curve showed that the predicted probability of the nomogram was consistent with the actual incidence rate and the DCA curve validated its effectiveness as a prognostic tool in a clinical setting. We constructed a combined model that can be used to help predict the prognosis of cSCI patients with radiomics and clinical characteristics, and further provided guidance for clinical decision-making by generating a nomogram.

Versatile MRI acquisition and processing protocol for population-based neuroimaging.

Neuroimaging has an essential role in studies of brain health and of cerebrovascular and neurodegenerative diseases, requiring the availability of versatile magnetic resonance imaging (MRI) acquisition and processing protocols. We designed and developed a multipurpose high-resolution MRI protocol for large-scale and long-term population neuroimaging studies that includes structural, diffusion-weighted and functional MRI modalities. This modular protocol takes almost 1 h of scan time and is, apart from a concluding abdominal scan, entirely dedicated to the brain. The protocol links the acquisition of an extensive set of MRI contrasts directly to the corresponding fully automated data processing pipelines and to the required quality assurance of the MRI data and of the image-derived phenotypes. Since its successful implementation in the population-based Rhineland Study (ongoing, currently more than 11,000 participants, target participant number of 20,000), the proposed MRI protocol has proved suitable for epidemiological and clinical cross-sectional and longitudinal studies, including multisite studies. The approach requires expertise in magnetic resonance image acquisition, in computer science for the data management and the execution of processing pipelines, and in brain anatomy for the quality assessment of the MRI data. The protocol takes ~1 h of MRI acquisition and ~20 h of data processing to complete for a single dataset, but parallelization over multiple datasets using high-performance computing resources reduces the processing time. By making the protocol, MRI sequences and pipelines available, we aim to contribute to better comparability, interoperability and reusability of large-scale neuroimaging data.

Automated Characterization of Abdominal MRI Exams Using Deep Learning.

Advances in magnetic resonance imaging (MRI) have revolutionized disease detection and treatment planning. However, as the volume and complexity of MRI data grow with increasing heterogeneity between institutions in imaging protocol, scanner technology, and data labeling, there is a need for a standardized methodology to efficiently identify, characterize, and label MRI sequences. Such a methodology is crucial for advancing research efforts that incorporate MRI data from diverse populations to develop robust machine learning models. This research utilizes convolutional neural networks (CNNs) to automatically classify sequence, orientation, and contrast, specifically tailored for abdominal MRI. Three distinct CNN models with similar backbone architectures were trained to classify single image slices into one of 12 sequences, 4 orientations, and 2 contrast classes. Results derived from this method demonstrate high levels of performance for the three specialized CNN models, with model accuracies for sequence, orientation, and contrast of 96.9%, 97.4%, and 97.3%, respectively.

Prenatal cfDNA Sequencing and Incidental Detection of Maternal Cancer

BackgroundCell-free DNA (cfDNA) sequence analysis to screen for fetal aneuploidy can incidentally detect maternal cancer. Additional data are needed to identify DNA-sequencing patterns and other biomarkers that can identify pregnant persons who are most likely to have cancer and to determine the best approach for follow-up.MethodsIn this ongoing study we performed cancer screening in pregnant or postpartum persons who did not perceive signs or symptoms of cancer but received unusual clinical cfDNA-sequencing results or results that were nonreportable (i.e., the fetal aneuploidy status could not be assessed) from one of 12 different commercial laboratories in North America. We used a uniform cancer-screening protocol including rapid whole-body magnetic resonance imaging (MRI), laboratory tests, and standardized cfDNA sequencing for research purposes with the use of a genomewide platform. The primary outcome was the presence of cancer in participants after the initial cancer-screening evaluation. Secondary analyses included test performance.ResultsCancer was present in 52 of the 107 participants in the initial cohort (48.6%). The sensitivity and specificity of whole-body MRI in detecting occult cancer were 98.0% and 88.5%, respectively. Physical examination and laboratory tests were of limited use in identifying participants with cancer. Research sequencing showed that 49 participants had a combination of copy-number gains and losses across multiple (≥3) chromosomes; cancer was present in 47 of the participants (95.9%) with this sequencing pattern. Sequencing patterns of cfDNA in which there were only chromosomal gains (multiple trisomies) or only chromosomal losses (one or more monosomies) were found in participants with nonmalignant conditions, such as fibroids.ConclusionsIn this study, 48.6% of participants who received unusual or nonreportable clinical cfDNA-sequencing results had an occult cancer. Further study of DNA-sequencing patterns that are suggestive of occult cancer during prenatal screening is warranted. (Funded by the NIH Intramural Research Programs; ClinicalTrials.gov number, NCT04049604.)

Do Intact Collateral Ligaments Play a Role in the Overall Clinical and Radiological Results of Rotating Hinge Knee Arthroplasties?

Rotating hinge knee arthroplasty (RHKA) implants are often utilized as a salvage procedure in cases where bone stock is compromised. The aim of this retrospective study was to investigate whether residual collateral ligament integrity affects functional and radiographic outcomes in patients undergoing revision surgery with RHKA. Patients treated with RHKA between 2011 and 2021 were eligible. Inclusion criteria were patients who had undergone an RHKA as a revision or a primary procedure, had complete radiological and clinical data, and had a minimum follow-up of two years. Patients who died before the second-year follow-up visit, those who had missing clinical and/or radiological data, and those who did not want to participate were excluded. There were 79 knees that were included. Patients were evaluated with standing radiographs and two-dimensional multi-acquisition variable-resonance imaging combination selective (MAVRIC) magnetic resonance imaging (MRI) sequences to assess ligament integrity. Range of motion (ROM), Visual Analog Scale (VAS), Knee Society Scores (KSS), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores were recorded. The mean age was 66 years (range, 39 to 79), and the mean follow-up was 54.6 months (approximately 4.5 years). The integrity of the collateral was successfully determined by the MRI scans and patients were divided into three groups: those who had both collateral ligaments (n = 53), isolated MCL damage (n = 14), and those who had both collateral ligaments damaged (n = 12). No significant difference was observed between the groups with regard to both radiological data and clinical scores (P > 0.05). KSS, KSS-F, and WOMAC function scores at the last follow-up were significantly higher in patients treated for secondary osteoarthritis (complex primaries). No significant difference in functional outcomes was detected in patients treated with RHKA, independent of collateral ligament integrity, at an average of 54 months postoperatively. When an RHKA is indicated as the first treatment option, results are more satisfactory in comparison to revision cases.