Conventional MR Research Articles

Added-value of dynamic contrast-enhanced MRI to conventional MRI for the differentiation between inflammatory myofibroblastic tumor and squamous cell carcinoma in the sinonasal region.

The purpose of this study was to evaluate the additional value of dynamic contrast-enhanced (DCE) MRI and diffusion weighted MRI (DWI) in differentiation between inflammatory myofibroblastic tumor (IMT) and squamous cell carcinoma (SCC) in the sinonasal cavity. Patients with pathologically proven IMT and SCC in the sinonasal region were enrolled in this retrospective study. All participants underwent conventional MRI and dynamic contrast-enhanced MRI, while a subset of them performed DWI. All the MRI parameters were independently analyzed by two investigators. This retrospective study included 21 patients with IMT and 55 patients with SCC. Significant differences were found in the conventional MR imaging features including mass margin, T2 signal intensity and track sign of maxillary (p < 0.05). For DCE-MRI features, significant differences were found in progressive centripetal continual enhancement and CImax (p < 0.001 and p = 0.026, respectively). A marginal significant difference was found in ADC values between IMT (0.86 ± 0.59) and SCC (1.14 ± 0.25) (p = 0.061). The conventional MRI analysis revealed that the combination of mass margin and track sign of maxillary yielded an accuracy of 81.6%. Using a combination of progressive centripetal continual enhancement on DCE-MRI and track sign of maxillary in multivariate logistic regression analysis, the accuracy was elevated to 92.1%. The incorporation of DCE-MRI features into conventional MRI showed improved diagnostic performance in differentiating IMT from SCC in the sinonasal region. The novel progressive centripetal continual enhancement on DCE-MRI is the most effective feature of IMT.

NIMG-85. RADIATION DOSE IS ASSOCIATED WITH CHANGE IN MAGNETIC RESONANCE FINGERPRINTING PROPERTIES OF ANATOMICALLY DISTINCT REGIONS OF INTEREST IN GLIOBLASTOMA MULTIFORME PATIENTS

Abstract Magnetic Resonance Fingerprinting (MRF) is an investigational imaging technique that provides spatially-resolved quantification of tissue properties, in contrast to qualitatively weighted conventional MR imaging. Intentional sequential variation of acquisition parameters throughout the MRF acquisition produces a unique quantitative signal timecourse for each tissue, which generates a spatial “fingerprint” for individual voxels. This study applies MRF to quantitatively correlate radiation dose with MR signal intensity changes, specifically T1, T2, and M0 maps across regions of interest (ROIs) that are involved in cognitive speed and processing. Three patients who underwent radiation therapy for resected glioblastoma multiforme participated in this study. MRF scans were conducted at pre-radiotherapy (pre-RT), mid-radiotherapy (mid-RT), and post-radiotherapy (post-RT) timepoints. ROIs (bilateral hippocampi, amygdalae, dorsolateral prefrontal cortices, and corpus callosum) were contoured and radiation dose to the ROI contours at mid-RT and post-RT timepoints were obtained using MIM treatment planning software (MIM Software, Beachwood, OH). Spearman’s rank correlation test was conducted in MATLAB (MATLAB R2024a, Natick, MA) for mean radiation dose to ROIs against MRF T1 relaxation time, T2 relaxation time, and M0 proton density. Mean radiation dose exhibited statistically significant correlation with absolute changes from pre-RT to post-RT T1 relaxation time, T2 relaxation time, and M0 proton density values across the ROIs. Mid-RT scans showed intermediate changes, suggesting a progressive alteration in tissue characteristics with accumulating radiation dose. The hippocampus and amygdala received lower radiation doses than other ROIs and exhibited less pronounced changes in T1, T2, and M0 values. These results demonstrate a novel application of MRF in quantitative characterization of imaging changes in anatomically distinct ROIs. The correlation between radiation dose and absolute changes in MRF signal values from pre-RT to post-RT MRF scans reflect progressive changes as a result of radiation exposure.

Enhancing preoperative diagnosis of pancreatic ductal adenocarcinoma and mass-forming chronic pancreatitis: a study on normalized conventional MR imaging parameters.

To assess the utility of signal intensity ratio (SIR) in distinguishing between mass-forming chronic pancreatitis (MFCP) and pancreatic ductal adenocarcinoma (PDAC), thereby reducing unnecessary pancreatectomies or delayed diagnosis brought by misdiagnosis. This retrospective study included 170 participants (34 with MFCP and 136 with PDAC) who underwent radical pancreatic surgery and were diagnosed via specimen pathology. The study group was carefully selected with a 1:4 ratio matching for sex, age, and operation time between two entities. T1 SIR, T2 SIR, arterial phase (AP) SIR, portal venous phase (VP) SIR, delay phase (DP) SIR, DWI0-50 SIR, and DWI500-1000 SIR, were calculated by dividing the signal intensity of lesions by that of the paraspinal muscle, serving as a reference organ. Intraclass Correlation Coefficient (ICC) was estimated to evaluate the intraobserver and interobserver reliability. Wilcoxon tests were employed for univariate analysis, and receiver operating characteristic (ROC) curves were generated to determine optimal cutoff points and AUC values for selected predictors. A tenfold cross-validation method was applied to validate the robustness of the results. The ICC demonstrated excellent correlation for both intraobserver and interobserver(ICCs > 0.8). T1 SIR, AP SIR, VP SIR, and DP SIR were significantly lower in the PDAC group compared to the MFCP group, and exhibited good independent predictive properties with the sensitivities of 61.8, 61.8, 70.6, and 73.5%, specificities of 66.2, 68.4, 59.6, and 55.9%, and AUCs of 0.620, 0.659, 0.670, and 0.668, respectively, hovering around 0.7. The tenfold cross-validation confirmed the reliability and robustness of our findings, with consistent AUC, sensitivity, specificity, and 95% confidence intervals over 1000 iterations. T1 SIR, AP SIR, VP SIR, and DP SIR show promise as potential imaging biomarkers for distinguishing between MFCP and PDAC.

An eXplainable Deep Learning Model for Multi-Modal MRI Grading of IDH-Mutant Astrocytomas

IDH-Mutant-astrocytomas are malignant brain glioma tumors. They are graded as lower (grades II and III) or higher (grade IV) according to their rate of growth and molecular features. Conventional IDH grading requires pathology examination, which is invasive, costly, and time-consuming. Non-invasive MRI methods are widely used but require a high level of interpretation for tumor grading. Recent advances in deep learning have shown potential to aid and improve the accuracy and efficiency of tumor classification. This study proposes a novel non-invasive approach for IDH-Mutant-astrocytomas grading using Light-weight Attention Network (LAN) that integrates multi-modal MRI data, including both conventional and advanced MR modalities. The study addresses inter-modality heterogeneity using Principal Component Analysis (PCA) while minimizing computational complexity. LAN uses a 3D Convolutional Neural Network (CNN) and a volumetric attention mechanism to extract and classify local and global tumor patterns. This is followed by an eXplainable AI approach that use SHapley Additive exPlanations (SHAP) values to understand the model decisions. LAN model outperforms other pre-trained state-of-the-art models, with an overall accuracy of 0.84. The advanced modalities Arterial Spin Labeling (ASL) and the Diffusion-weighted imaging(DWI) SHAP score contributes significantly in improving tumor heterogeneity visualization for aggressive grade II along with conventional MRIs. This work demonstrates the feasibility of integrating explainable deep learning with multi-modal MRI data for precise and comprehensible early glioma grading, potentially leading to better clinical decision-making.

Comprehensive assessment of imaging quality of artificial intelligence-assisted compressed sensing-based MR images in routine clinical settings

BackgroundConventional MR acceleration techniques, such as compressed sensing, parallel imaging, and half Fourier often face limitations, including noise amplification, reduced signal-to-noise ratio (SNR) and increased susceptibility to artifacts, which can compromise image quality, especially in high-speed acquisitions. Artificial intelligence (AI)-assisted compressed sensing (ACS) has emerged as a novel approach that combines the conventional techniques with advanced AI algorithms. The objective of this study was to examine the imaging quality of the ACS approach by qualitative and quantitative analysis for brain, spine, kidney, liver, and knee MR imaging, as well as compare the performance of this method with conventional (non-ACS) MR imaging.MethodsThis study included 50 subjects. Three radiologists independently assessed the quality of MR images based on artefacts, image sharpness, overall image quality and diagnostic efficacy. SNR, contrast-to-noise ratio (CNR), edge content (EC), enhancement measure (EME), scanning time were used for quantitative evaluation. The Cohen’s kappa correlation coefficient (k) was employed to measure radiologists’ inter-observer agreement, and the Mann Whitney U-test used for comparison between non-ACS and ACS.ResultsThe qualitative analysis of three radiologists demonstrated that ACS images showed superior clinical information than non-ACS images with a mean k of ~ 0.70. The images acquired with ACS approach showed statistically higher values (p < 0.05) for SNR, CNR, EC, and EME compared to the non-ACS images. Furthermore, the study’s findings indicated that ACS-enabled images reduced scan time by more than 50% while maintaining high imaging quality.ConclusionIntegrating ACS technology into routine clinical settings has the potential to speed up image acquisition, improve image quality, and enhance diagnostic procedures and patient throughput.

Observation of dynamic position and morphological changes of temporomandibular joint discs under Angle's classification

Objective: To observe the dynamic changes of the temporomandibular joint (TMJ) disc in joint movement under different Angle's classification, providing reference for understanding joint functional movement and providing a basis for more accurate clinical imaging diagnosis. Methods: A total of 30 patients (13 males and 17 females) with temporomandibular disorders who were admitted to Beijing Friendship Hospital, Capital Medical University and General Hospital of the People's Liberation Army from January 2022 to April 2024 were enrolled. Thirty adults (13 males and 17 females) with different Angle's classification, with an average age of (34.4±8.5) years, were subjected to dynamic imaging of their TMJ from the closed position to the maximum opening position, and then to the closed position using MRI. The position and morphological changes of the articular discs were observed. Results: The results showed that volunteers with no displacement of the articular disc in class Ⅰ, Ⅱ, and Ⅲ relationships had different shapes of the articular disc during open and closed mouth movements. However, in the maximum opening position, the articular disc were all located directly below the maxillary nodules, and their shape is double concave. In terms of irreversible anterior displacement of the articular disc, in class Ⅰ Angle, the posterior zone of the disc contacts the anterior inclined plane of condyle from the maximum opening position back to the front of the closing position. In class Ⅱ, the posterior zone of the disc contacts the anterior inclined plane of condyle from the beginning of opening position to maximum opening position. In class Ⅲ, the posterior zone of the disc is always in contact with the anterior inclined plane of condyle throughout the entire movement process. And among them, the articular disc presents a forward displacement state at the closing position, its morphology undergoes folding phenomenon. When the openness is 2.5 cm, the articular disc moves up to a certain extent, and is closer to the anterior inclined plane of condyle, and its shape is also partially changed. When the openness is 4.3 cm, the shape of the articular disc, located between the anterior inclined plane of the joint node and the posterior inclined plane of the condyle, is typical double concave, which is sufficient to show that the articular disc is reversible when maximum opening position is reached. In terms of reversible anterior disc displacement, in class Ⅰ Angle, the posterior zone of the disc contact with the anterior inclined plane of condyle at the beginning of the opening position and the end of the closing position. In classⅡ Angle, the posterior zone of the articular disc is not in contact with the anterior inclined plane of condyle. In class Ⅲ Angle, the posterior zone of the articular disc contact with the anterior inclined plane of condyle at the end of the closing position. Conclusions: Multi level dynamic MR imaging data of the temporomandibular joint can dynamically observe the movement of the temporomandibular joint, intuitively and accurately display the position and shape of the articular disc during movement, and can serve as a useful supplement to static conventional MR imaging of the TMJ. The patient's TMJ needs to reach the maximum opening position in order to determine whether the joint disc displacement can be reversible or not.

Giant cell tumor of bone of temporal bone and skull base: report of 6 cases.

Five cases of giant cell tumor of bone (GCTB) in the head and neck region were reported, with a main focus on the radiological findings to identify common characteristics for the diagnosis of GCTB in these sites. Five consecutive patients diagnosed with GCTB were retrospectively selected. Radiological features on conventional and advanced MR sequences and CT were analyzed. HE staining and immunohistochemical examination were performed using antibodies against p63 and CD68. The common clinical features were local mass (3/5), tinnitus (3/5) and headache (2/5). Radiologically, all the cases were well-circumscribed osteolytic lesion, majority of cases demonstrated an expansile growth pattern and "soap bubble" appearance on CT (4/5). On MRI, the tumors showed predominantly hypointensity both on T1WI and T2WI, and no evidence of restricted diffusion on DWI. Intratumoral hemorrhage (2/5), cystic alternation (2/5) and very low signal on T2WI in the periphery region of the tumor (4/5) was found. Fluid-fluid level was noted in one case, which was eventually verified to be GCTB with secondary aneurysmal bone cyst (ABC). With contrast agent, all the cases showed striking (3/5) or mild to intermediate (2/5) enhancement. Although the above described radiological findings are not specific for GCTB in head and neck region, a well-defined osteolytic lesion in the bones of head and neck region with "soap bubble" appearance on CT and hypointensity on T2WI with very low signal in the peripheral region of the tumor on MRI highly suggest GCTB for patient ages 20 to 40.

Usefulness of synthetic MRI for differentiation of IDH-mutant diffuse gliomas and its comparison with the T2-FLAIR mismatch sign.

The T2-FLAIR mismatch sign is a characteristic imaging biomarker for astrocytoma, isocitrate dehydrogenase (IDH)-mutant. However, investigators have provided varying interpretations of the positivity/negativity of this sign given for individual cases the nature of qualitative visual assessment. Moreover, MR sequence parameters also influence the appearance of the T2-FLAIR mismatch sign. To resolve these issues, we used synthetic MR technique to quantitatively evaluate and differentiate astrocytoma from oligodendroglioma. This study included 20 patients with newly diagnosed non-enhanced IDH-mutant diffuse glioma who underwent preoperative synthetic MRI using the Quantification of Relaxation Times and Proton Density by Multiecho acquisition of a saturation-recovery using Turbo spin-Echo Readout (QRAPMASTER) sequence at our institution. Two independent reviewers evaluated preoperative conventional MR images to determine the presence or absence of the T2-FLAIR mismatch sign. Synthetic MRI was used to measure T1, T2 and proton density (PD) values in the tumor lesion. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance. The pathological diagnoses included astrocytoma, IDH-mutant (n = 12) and oligodendroglioma, IDH-mutant and 1p/19q-codeleted (n = 8). The sensitivity and specificity of T2-FLAIR mismatch sign for astrocytoma were 66.7% and 100% [area under the ROC curve (AUC) = 0.833], respectively. Astrocytoma had significantly higher T1, T2, and PD values than did oligodendroglioma (p < 0.0001, < 0.0001, and 0.0154, respectively). A cutoff lesion T1 value of 1580 ms completely differentiated astrocytoma from oligodendroglioma (AUC = 1.00). Quantitative evaluation of non-enhanced IDH-mutant diffuse glioma using synthetic MRI allowed for better differentiation between astrocytoma and oligodendroglioma than did conventional T2-FLAIR mismatch sign. Measurement of T1 and T2 value by synthetic MRI could improve the differentiation of IDH-mutant diffuse gliomas.

Arterial Spin-Labeling MR Imaging in the Detection of Intracranial Arteriovenous Malformations in Patients with Hereditary Hemorrhagic Telangiectasia.

Hereditary hemorrhagic telangiectasia (HHT) is an autosomal dominant disease that causes vascular malformations in a variety of organs and tissues, including brain AVMs. Because brain AVMs have the potential to cause disabling or fatal intracranial hemorrhage, detection of these lesions before rupture is the goal of screening MR imaging/MRA examinations in patients with HHT. Prior studies have demonstrated superior sensitivity for HHT-related brain AVMs by using postcontrast MR imaging sequences as compared with MRA alone. We now present data regarding the incremental benefit of including arterial spin-labeling (ASL) perfusion sequences as part of MR imaging/MRA screening in patients with this condition. We retrospectively analyzed 831 patients at the UCSF Hereditary Hemorrhagic Telangiectasia Center of Excellence. Of these, 42 patients had complete MR imaging/MRA, ASL perfusion scans, and criterion-standard DSA data. Two neuroradiologists reviewed imaging studies and a third provided adjudication when needed. Eight patients had no brain AVMs detected on DSA. The remaining 34 patients had 57 brain AVMs on DSA. Of the 57 identified AVMs, 51 (89.5%) were detected on ASL and 43 (75.4%) were detected on conventional MR imaging/MRA sequences (P = .049), with 8 lesions detected on ASL perfusion but not on conventional MR imaging. ASL provides increased sensitivity for brain AVMs in patients with HHT. Inclusion of ASL should be considered as part of comprehensive MR imaging/MRA screening protocols for institutions taking care of patients with HHT.

Distinguishing Pituitary Metastasis and Pituitary Neuroendocrine Tumors through Conventional MR Imaging and Clinical Features.

Given their overlapping features, pituitary metastases frequently imitate pituitary neuroendocrine tumors in neuroimaging studies. This study aimed to distinguish pituitary metastases from pituitary neuroendocrine tumors on the basis of conventional MR imaging and clinical features as a practical approach. In this 2-center retrospective study, backward from January 2024, preoperative pituitary MR imaging examinations of 22 pituitary metastases and 74 pituitary neuroendocrine tumors were analyzed. Exclusion criteria were as follows: absence of a definitive histopathologic diagnosis, history of pituitary surgery or radiation therapy before MR imaging, and pituitary neuroendocrine tumors treated with medical therapy. Two radiologists systematically evaluated 13 conventional MR imaging features that have been reported more commonly as indicative of pituitary metastases and pituitary neuroendocrine tumors in the literature. Age, sex, history of cancer, and maximum tumor size constituted the clinical/epidemiologic features. The primary cancer origin for this study was also noted. Univariable and multivariable logistic regression was used for the selection of variables, determining independent predictors, and modeling. Interobserver agreement was evaluated for all imaging parameters using the Cohen κ statistic or intraclass correlation coefficient. A total of 22 patients with pituitary metastases (8 women; mean age, 49.5 [SD, 13] years) and 74 patients with pituitary neuroendocrine tumors (36 women; mean age, 50.1 [SD, 11] years) were enrolled. There was no statistically significant distributional difference in age, sex, or maximum tumor size between the 2 groups. Lung cancer (9/22; 41%) was the most commonly reported primary tumor, followed by breast (3/22; 13.6%) and unknown cancer (3/22; 13.6%). Logistic regression revealed 3 independent predictors: rapid growth on control MR imaging, masslike or nodular expansion of the pituitary stalk, and a history of cancer. The model based on these 3 features achieved an area under the curve, accuracy, sensitivity, specificity, and Brier score of 0.987 (95% CI, 0.964-1), 97.9% (95% CI, 92.7%-99.8%), 95.5% (95% CI, 77.2%-99.9%), 98.6% (95% CI, 92.7%-100%), and 0.025, respectively. Two conventional features based on pituitary MR imaging with the clinical variable of history of cancer had satisfying predictive performance, making them potential discriminators between pituitary metastases and pituitary neuroendocrine tumors. In cases in which differentiation between pituitary metastases and pituitary neuroendocrine tumors poses a challenge, the results of this study may help with the diagnosis.

Occult Amyloid-β-Related Angiitis: Neuroimaging Findings at 1.5T, 3T, and 7T MRI.

Cerebral amyloid angiopathy (CAA) is a progressive neurodegenerative small vessel disease that is associated with intracranial hemorrhage and cognitive impairment in the elderly. The clinical and radiographic presentations have many overlapping features with vascular cognitive impairment, hemorrhagic stroke, and Alzheimer disease (AD). Amyloid-β-related angiitis (ABRA) is a form of primary CNS vasculitis linked to CAA, with the development of spontaneous autoimmune inflammation against amyloid in the vessel wall with resultant vasculitis. The diagnosis of ABRA and CAA is important. ABRA is often fatal if untreated and requires prompt immunosuppression. Important medical therapies such as anticoagulation and antiamyloid agents for AD are contraindicated in CAA. Here, we present a biopsy-proved case of ABRA with underlying occult CAA. Initial 1.5T and 3T MR imaging did not suggest CAA per the Boston Criteria 2.0. ABRA was not included in the differential diagnosis due to the lack of any CAA-related findings on conventional MR imaging. However, a follow-up 7T MR imaging revealed extensive cortical/subcortical cerebral microbleeds, cortical superficial siderosis, and intragyral hemorrhage in extensive detail throughout the supratentorial brain regions, which radiologically supported the diagnosis of ABRA in the setting of CAA. This case suggests an increased utility of high-field MR imaging to detect occult hemorrhagic neuroimaging findings with the potential to both diagnose more patients with CAA and diagnose them earlier.

Usefulness of Olfactory Bulb Measurement in 3D-FIESTA in Differentiating Parkinson Disease from Atypical Parkinsonism.

Parkinson disease is a prevalent disease, with olfactory dysfunction recognized as an early nonmotor manifestation. It is sometimes difficult to differentiate Parkinson disease from atypical parkinsonism using conventional MR imaging and motor symptoms. It is also known that olfactory loss occurs to a lesser extent or is absent in atypical parkinsonism. To the best of our knowledge, no study has examined olfactory bulb changes to differentiate Parkinson disease from atypical parkinsonism, even in an early diagnosis, and its association with conventional MR imaging findings. Hence, we aimed to assess the utility of olfactory bulb measurements in differentiating Parkinson disease from atypical parkinsonism even in the early stage. In this retrospective study, we enrolled 108 patients with Parkinson disease, 13 with corticobasal syndrome, 15 with multiple system atrophy, and 17 with progressive supranuclear palsy who developed parkinsonism. Thirty-nine age-matched healthy subjects served as controls. All subjects underwent conventional MR imaging and 3D FIESTA for olfactory bulb measurements using manual ROI quantification of the cross-sectional olfactory bulb area using the coronal plane. Bilateral olfactory bulb measurements were averaged. For group comparisons, we used the Welch t test, and we assessed diagnostic accuracy using receiver operating characteristic analysis. Patients with Parkinson disease had a mean olfactory bulb area of 4.2 (SD, 1.0 mm2), significantly smaller than in age-matched healthy subjects (6.6 [SD, 1.7 mm2], P < .001), and those with corticobasal syndrome (5.4 [SD, 1.2 mm2], P < .001), multiple system atrophy (6.5 [SD, 1.2 mm2], P < .001), and progressive supranuclear palsy (5.4 [SD, 1.2 mm2], P < .001). The receiver operating characteristic analysis for the olfactory bulb area measurements showed good diagnostic performance in differentiating Parkinson disease from atypical parkinsonism, with an area under the curve of 0.87, an optimal cutoff value of 5.1 mm2, and a false-positive rate of 18%. When we compared within 2 years of symptom onset, the olfactory bulb in Parkinson disease (4.2 [SD, 1.1 mm2]) remained significantly smaller than in atypical parkinsonism (versus corticobasal syndrome (6.1 [SD, 0.7 mm2]), P < .001; multiple system atrophy (6.3 [SD, 1.4 mm2]), P < .001; and progressive supranuclear palsy (5.2 [1.3 mm2], P = .003, respectively). 3D FIESTA-based olfactory bulb measurement holds promise for distinguishing Parkinson disease from atypical parkinsonism, especially in the early stage.

Toward Ultra-High-Quality-Factor Wireless Masing Magnetic Resonance Sensing.

It has recently been shown that a bolus of hyperpolarized nuclear spins can yield stimulated emission signals similar in nature to maser signals, potentially enabling new ways of sensing hyperpolarized contrast media, including most notably [1-13C]pyruvate that is under evaluation in over 50 clinical trials for metabolic imaging of cancer. The stimulated NMR signal emissions lasting for minutes do not require radio-frequency excitation, offering unprecedented advantages compared to conventional MR sensing. However, creating nuclear spin maser emission is challenging in practice due to stringent fundamental requirements, making practical in vivo applications hardly possible using conventional passive MR detectors. Here, we demonstrate the utility of a wireless NMR maser detector, the quality factor of which was enhanced 22-fold (to 1,670) via parametric pumping. This active-feedback technique breaks the intrinsic fundamental limit of NMR detector circuit quality factor. We show the use of parametric pumping to reduce the threshold requirement for inducing nuclear spin masing at 300 MHz resonance frequency in a preclinical MRI scanner. Indeed, stimulated emission from hyperpolarized protons was obtained under highly unfavorable conditions of low magnetic field homogeneity (T2* of 3 ms). Greater gains of the quality factor of the MR detector (up to 1 million) were also demonstrated.

Toward Ultra‐High‐Quality‐Factor Wireless Masing Magnetic Resonance Sensing

AbstractIt has recently been shown that a bolus of hyperpolarized nuclear spins can yield stimulated emission signals similar in nature to maser signals, potentially enabling new ways of sensing hyperpolarized contrast media, including most notably [1‐13C]pyruvate that is under evaluation in over 50 clinical trials for metabolic imaging of cancer. The stimulated NMR signal emissions lasting for minutes do not require radio‐frequency excitation, offering unprecedented advantages compared to conventional MR sensing. However, creating nuclear spin maser emission is challenging in practice due to stringent fundamental requirements, making practical in vivo applications hardly possible using conventional passive MR detectors. Here, we demonstrate the utility of a wireless NMR maser detector, the quality factor of which was enhanced 22‐fold (to 1,670) via parametric pumping. This active‐feedback technique breaks the intrinsic fundamental limit of NMR detector circuit quality factor. We show the use of parametric pumping to reduce the threshold requirement for inducing nuclear spin masing at 300 MHz resonance frequency in a preclinical MRI scanner. Indeed, stimulated emission from hyperpolarized protons was obtained under highly unfavorable conditions of low magnetic field homogeneity (T2* of 3 ms). Greater gains of the quality factor of the MR detector (up to 1 million) were also demonstrated.

Quantitative Magnetic Resonance Imaging (qMRI) of the Small Bowel in Crohn's Disease: State-of-the-Art and Future Directions.

Crohn's disease (CD) is a chronic inflammatory disease of the gastrointestinal tract in which repeated episodes of acute inflammation may lead to long-term bowel damage. Cross-sectional imaging is used in conjunction with endoscopy to diagnose and monitor disease and detect complications. Magnetic resonance imaging (MRI) has demonstrable utility in evaluating inflammatory activity. However, subjective interpretation of conventional MR sequences is limited in its ability to fully phenotype the underlying histopathological processes in chronic disease. In particular, conventional MRI can be confounded by the presence of mural fibrosis and muscle hypertrophy, which can mask or sometimes mimic inflammation. Quantitative MRI (qMRI) methods provide a means to better differentiate mural inflammation from fibrosis and improve quantification of these processes. qMRI may also provide more objective measures of disease activity and enable better tailoring of treatment. Here, we review quantitative MRI methods for imaging the small bowel in CD and consider the path to their clinical translation. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Rate of abnormalities in quantitative MR neuroimaging of persons with chronic traumatic brain injury

BackgroundMild traumatic brain injury (mTBI) can result in lasting brain damage that is often too subtle to detect by qualitative visual inspection on conventional MR imaging. Although a number of FDA-cleared MR neuroimaging tools have demonstrated changes associated with mTBI, they are still under-utilized in clinical practice.MethodsWe investigated a group of 65 individuals with predominantly mTBI (60 mTBI, 48 due to motor-vehicle collision, mean age 47 ± 13 years, 27 men and 38 women) with MR neuroimaging performed in a median of 37 months post-injury. We evaluated abnormalities in brain volumetry including analysis of left-right asymmetry by quantitative volumetric analysis, cerebral perfusion by pseudo-continuous arterial spin labeling (PCASL), white matter microstructure by diffusion tensor imaging (DTI), and neurometabolites via magnetic resonance spectroscopy (MRS).ResultsAll participants demonstrated atrophy in at least one lobar structure or increased lateral ventricular volume. The globus pallidi and cerebellar grey matter were most likely to demonstrate atrophy and asymmetry. Perfusion imaging revealed significant reductions of cerebral blood flow in both occipital and right frontoparietal regions. Diffusion abnormalities were relatively less common though a subset analysis of participants with higher resolution DTI demonstrated additional abnormalities. All participants showed abnormal levels on at least one brain metabolite, most commonly in choline and N-acetylaspartate.ConclusionWe demonstrate the presence of coup-contrecoup perfusion injury patterns, widespread atrophy, regional brain volume asymmetry, and metabolic aberrations as sensitive markers of chronic mTBI sequelae. Our findings expand the historic focus on quantitative imaging of mTBI with DTI by highlighting the complementary importance of volumetry, arterial spin labeling perfusion and magnetic resonance spectroscopy neurometabolite analyses in the evaluation of chronic mTBI.

Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas

Background1p/19q co-deletion in low-grade gliomas (LGG, World Health Organization grade II and III) is of great significance in clinical decision making. We aim to use radiomics analysis to predict 1p/19q co-deletion in LGG based on amide proton transfer weighted (APTw), diffusion weighted imaging (DWI), and conventional MRI.MethodsThis retrospective study included 90 patients histopathologically diagnosed with LGG. We performed a radiomics analysis by extracting 8454 MRI-based features form APTw, DWI and conventional MR images and applied a least absolute shrinkage and selection operator (LASSO) algorithm to select radiomics signature. A radiomics score (Rad-score) was generated using a linear combination of the values of the selected features weighted for each of the patients. Three neuroradiologists, including one experienced neuroradiologist and two resident physicians, independently evaluated the MR features of LGG and provided predictions on whether the tumor had 1p/19q co-deletion or 1p/19q intact status. A clinical model was then constructed based on the significant variables identified in this analysis. A combined model incorporating both the Rad-score and clinical factors was also constructed. The predictive performance was validated by receiver operating characteristic curve analysis, DeLong analysis and decision curve analysis. P < 0.05 was statistically significant.ResultsThe radiomics model and the combined model both exhibited excellent performance on both the training and test sets, achieving areas under the curve (AUCs) of 0.948 and 0.966, as well as 0.909 and 0.896, respectively. These results surpassed the performance of the clinical model, which achieved AUCs of 0.760 and 0.766 on the training and test sets, respectively. After performing Delong analysis, the clinical model did not significantly differ in predictive performance from three neuroradiologists. In the training set, both the radiomic and combined models performed better than all neuroradiologists. In the test set, the models exhibited higher AUCs than the neuroradiologists, with the radiomics model significantly outperforming resident physicians B and C, but not differing significantly from experienced neuroradiologist.ConclusionsOur results suggest that our algorithm can noninvasively predict the 1p/19q co-deletion status of LGG. The predictive performance of radiomics model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians, thereby offering the potential to facilitate non-invasive 1p/19q co-deletion prediction of LGG.

Assessment for Carotid Atherosclerotic Plaque Using Vessel Wall Magnetic Resonance Imaging: A Multireader ROC Study to Determine Optimal Sequence for Detecting Vessel Wall Calcification

Introduction: We aimed to compare conventional vessel wall MR imaging techniques and quantitative susceptibility mapping (QSM) to determine the optimal sequence for detecting carotid artery calcification. Methods: Twenty-two patients who underwent carotid vessel wall MR imaging and neck CT were enrolled. Four slices of 6-mm sections from the bilateral internal carotid bifurcation were subdivided into 4 segments according to clock position (0–3, 3–6, 6–9, and 9–12) and assessed for calcification. Two blinded radiologists independently reviewed a total of 704 segments and scored the likelihood of calcification using a 5-point scale on spin-echo imaging, FLASH, and QSM. The observer performance for detecting calcification was evaluated by a multireader, multiple-case receiver operating characteristic study. Weighted κ statistics were calculated to assess interobserver agreement. Results: QSM had a mean area under the receiver operating characteristic curve of 0.85, which was significantly higher than that of any other sequence (p < 0.01) and showed substantial interreader agreement (κ = 0.68). A segment with a score of 3–5 was defined as positive, and a segment with a score of 1–2 was defined as negative; the sensitivity and specificity of QSM were 0.75 and 0.87, respectively. Conclusion: QSM was the most reliable MR sequence for the detection of plaque calcification.

Evaluating the Efficacy of Perfusion MRI and Conventional MRI in Distinguishing Recurrent Cerebral Metastasis from Brain Radiation Necrosis.

Differentiating recurrent cerebral metastasis (CM) from brain radiation necrosis (BRN) is pivotal for guiding appropriate treatment and prognostication. Despite advances in imaging techniques, however, accurately distinguishing these conditions non-invasively is still challenging. This single-center retrospective study reviewed 32 cases (28 patients) with confirmed cerebral metastases who underwent surgical excision of lesions initially diagnosed by MRI and/or MR perfusion scans from 1 January 2015 to 30 September 2020. Diagnostic accuracy was assessed by comparing imaging findings with postoperative histopathology. Conventional MRI accurately identified recurrent CM in 75% of cases. MR perfusion scans showed significantly higher mean maximum relative cerebral blood volume (max. rCBV) in metastasis cases, indicating its potential as a discriminative biomarker. No single imaging modality could definitively distinguish CM from BRN. Survival analysis revealed gender as the only significant factor affecting overall survival, with no significant survival difference observed between patients with CM and BRN after controlling for confounding factors. This study underscores the limitations of both conventional MRI and MR perfusion scans in differentiating recurrent CM from BRN. Histopathological examination remains essential for accurate diagnosis. Further research is needed to improve the reliability of non-invasive imaging and to guide the management of patients with these post-radiation events.

Arterial Spin-Labeling and DSC Perfusion Metrics Improve Agreement in Neuroradiologists' Clinical Interpretations of Posttreatment High-Grade Glioma Surveillance MR Imaging-An Institutional Experience.

MR perfusion has shown value in the evaluation of posttreatment high-grade gliomas, but few studies have shown its impact on the consistency and confidence of neuroradiologists' interpretation in routine clinical practice. We evaluated the impact of adding MR perfusion metrics to conventional contrast-enhanced MR imaging in posttreatment high-grade glioma surveillance imaging. This retrospective study included 45 adults with high-grade gliomas who had posttreatment perfusion MR imaging. Four neuroradiologists assigned Brain Tumor Reporting and Data System scores for each examination on the basis of the interpretation of contrast-enhanced MR imaging and then after the addition of arterial spin-labeling-CBF, DSC-relative CBV, and DSC-fractional tumor burden. Interrater agreement and rater agreement with a multidisciplinary consensus group were assessed with κ statistics. Raters used a 5-point Likert scale to report confidence scores. The frequency of clinically meaningful score changes resulting from the addition of each perfusion metric was determined. Interrater agreement was moderate for contrast-enhanced MR imaging alone (κ = 0.63) and higher with perfusion metrics (arterial spin-labeling-CBF, κ = 0.67; DSC-relative CBV, κ = 0.66; DSC-fractional tumor burden, κ = 0.70). Agreement between raters and consensus was highest with DSC-fractional tumor burden (κ = 0.66-0.80). Confidence scores were highest with DSC-fractional tumor burden. Across all raters, the addition of perfusion resulted in clinically meaningful interpretation changes in 2%-20% of patients compared with contrast-enhanced MR imaging alone. Adding perfusion to contrast-enhanced MR imaging improved interrater agreement, rater agreement with consensus, and rater confidence in the interpretation of posttreatment high-grade glioma MR imaging, with the highest agreement and confidence scores seen with DSC-fractional tumor burden. Perfusion MR imaging also resulted in interpretation changes that could change therapeutic management in up to 20% of patients.