Radiofrequency antenna concepts for human cardiac MR at 14.0 T

OBJECTIVE: To examine the feasibility of human cardiac MR (CMR) at 14.0 T using high-density radiofrequency (RF) dipole transceiver arrays in conjunction with static and dynamic parallel transmission (pTx). MATERIALS AND METHODS: RF arrays comprised of self-grounded bow-tie (SGBT) antennas, bow-tie (BT) antennas, or fractionated dipole (FD) antennas were used in this simulation study. Static and dynamic pTx were applied to enhance transmission field (B(1)(+)) uniformity and efficiency in the heart of the human voxel model. B(1)(+) distribution and maximum specific absorption rate averaged over 10 g tissue (SAR(10g)) were examined at 7.0 T and 14.0 T. RESULTS: At 14.0 T static pTx revealed a minimum B(1)(+)(ROI) efficiency of 0.91 μT/√kW (SGBT), 0.73 μT/√kW (BT), and 0.56 μT/√kW (FD) and maximum SAR(10g) of 4.24 W/kg, 1.45 W/kg, and 2.04 W/kg. Dynamic pTx with 8 kT points indicate a balance between B(1)(+)(ROI) homogeneity (coefficient of variation  1.11 µT/√kW) at 14.0 T with a maximum SAR(10g) < 5.25 W/kg. DISCUSSION: MRI of the human heart at 14.0 T is feasible from an electrodynamic and theoretical standpoint, provided that multi-channel high-density antennas are arranged accordingly. These findings provide a technical foundation for further explorations into CMR at 14.0 T

Motion-compensated fat-water imaging for 3D cardiac MRI at ultra-high fields

PURPOSE: Respiratory motion-compensated (MC) 3D cardiac fat-water imaging at 7T. METHODS: Free-breathing bipolar 3D triple-echo gradient-recalled-echo (GRE) data with radial phase-encoding (RPE) trajectory were acquired in 11 healthy volunteers (7M\4F, 21-35 years, mean: 30 years) with a wide range of body mass index (BMI; 19.9-34.0 kg/m2 ) and volunteer tailored B(1)(+) shimming. The bipolar-corrected triple-echo GRE-RPE data were binned into different respiratory phases (self-navigation) and were used for the estimation of non-rigid motion vector fields (MF) and respiratory resolved (RR) maps of the main magnetic field deviations (ΔB0 ). RR ΔB0 maps and MC ΔB0 maps were compared to a reference respiratory phase to assess respiration-induced changes. Subsequently, cardiac binned fat-water images were obtained using a model-based, respiratory motion-corrected image reconstruction. RESULTS: The 3D cardiac fat-water imaging at 7T was successfully demonstrated. Local respiration-induced frequency shifts in MC ΔB(0) maps are small compared to the chemical shifts used in the multi-peak model. Compared to the reference exhale ΔB0 map these changes are in the order of 10 Hz on average. Cardiac binned MC fat-water reconstruction reduced respiration induced blurring in the fat-water images, and flow artifacts are reduced in the end-diastolic fat-water separated images. CONCLUSION: This work demonstrates the feasibility of 3D fat-water imaging at UHF for the entire human heart despite spatial and temporal B(1)(+) and B0 variations, as well as respiratory and cardiac motion

3D model-based super-resolution motion-corrected cardiac T1 mapping

OBJECTIVE: To provide 3D high-resolution cardiac T1 maps using model-based super-resolution reconstruction (SRR). APPROACH: Due to signal-to-noise ratio (SNR) limitations and the motion of the heart during imaging, often 2D T1 maps with only low through-plane resolution (i.e. slice thickness of 6 to 8 mm) can be obtained. Here, a model-based SRR approach is presented, which combines multiple stacks of 2D acquisitions with 6 to 8 mm slice thickness and generates 3D high-resolution T1 maps with a slice thickness of 1.5 to 2 mm. Every stack was acquired in a different breath hold (BH) and any misalignment between BH was corrected retrospectively. The novelty of the proposed approach is the BH correction and the application of model-based SRR on cardiac T1 Mapping. The proposed approach was evaluated in numerical simulations and phantom experiments and demonstrated in four healthy subjects. MAIN RESULTS: Alignment of BH states was essential for SRR even in healthy volunteers. In simulations, respiratory motion could be estimated with an RMS error of 0.18 ± 0.28 mm. SRR improved the visualization of small structures. High accuracy and precision (average standard deviation of 69.62 ms) of the T1 values was ensured by SRR while the detectability of small structures increased by 40%. SIGNIFICANCE: The proposed SRR approach provided T1 maps with high in-plane and high through-plane resolution (1.3×1.3×1.5 to 2 mm(3)). The approach led to improvements in the visualization of small structures and precise T1 values

Rapid estimation of 2D relative B(1)(+)-maps from localizers in the human heart at 7T using deep learning

PURPOSE: Subject-tailored parallel transmission pulses for ultra-high fields body applications are typically calculated based on subject-specific B(1)(+)-maps of all transmit channels, which require lengthy adjustment times. This study investigates the feasibility of using deep learning to estimate complex, channel-wise, relative 2D B(1)(+)-maps from a single gradient echo localizer to overcome long calibration times. METHODS: 126 channel-wise, complex, relative 2D B(1)(+)-maps of the human heart from 44 subjects were acquired at 7T using a Cartesian, cardiac gradient-echo sequence obtained under breath-hold to create a library for network training and cross-validation. The deep learning predicted maps were qualitatively compared to the ground truth. Phase-only B(1)(+)-shimming was subsequently performed on the estimated B(1)(+)-maps for a region of interest covering the heart. The proposed network was applied at 7T to 3 unseen test subjects. RESULTS: The deep learning-based B(1)(+)-maps, derived in approximately 0.2 seconds, match the ground truth for the magnitude and phase. The static, phase-only pulse design performs best when maximizing the mean transmission efficiency. In-vivo application of the proposed network to unseen subjects demonstrates the feasibility of this approach: the network yields predicted B(1)(+)-maps comparable to the acquired ground truth and anatomical scans reflect the resulting B(1)(+)-pattern using the deep learning-based maps. CONCLUSION: The feasibility of estimating 2D relative B(1)(+)-maps from initial localizer scans of the human heart at 7T using deep learning is successfully demonstrated. Because the technique requires only sub-seconds to derive channel-wise B(1)(+)-maps, it offers high potential for advancing clinical body imaging at ultra-high fields

Characteristics of Early-Onset vs Late-Onset Colorectal Cancer: A Review.

The incidence of early-onset colorectal cancer (younger than 50 years) is rising globally, the reasons for which are unclear. It appears to represent a unique disease process with different clinical, pathological, and molecular characteristics compared with late-onset colorectal cancer. Data on oncological outcomes are limited, and sensitivity to conventional neoadjuvant and adjuvant therapy regimens appear to be unknown. The purpose of this review is to summarize the available literature on early-onset colorectal cancer. Within the next decade, it is estimated that 1 in 10 colon cancers and 1 in 4 rectal cancers will be diagnosed in adults younger than 50 years. Potential risk factors include a Westernized diet, obesity, antibiotic usage, and alterations in the gut microbiome. Although genetic predisposition plays a role, most cases are sporadic. The full spectrum of germline and somatic sequence variations implicated remains unknown. Younger patients typically present with descending colonic or rectal cancer, advanced disease stage, and unfavorable histopathological features. Despite being more likely to receive neoadjuvant and adjuvant therapy, patients with early-onset disease demonstrate comparable oncological outcomes with their older counterparts. The clinicopathological features, underlying molecular profiles, and drivers of early-onset colorectal cancer differ from those of late-onset disease. Standardized, age-specific preventive, screening, diagnostic, and therapeutic strategies are required to optimize outcomes

Calibration-free parallel transmission of the cervical, thoracic, and lumbar spinal cord at 7T

PURPOSE: To address the limitations of spinal cord imaging at ultra-high field (UHF) due to time-consuming parallel transmit (pTx) adjustments. This study introduces calibration-free offline computed universal shim modes that can be applied seamlessly for different pTx RF coils and spinal cord target regions, substantially enhancing spinal cord imaging efficiency at UHF. METHODS: A library of channel-wise relative B(+)(1) maps for the cervical spinal cord (six datasets) and thoracic and lumbar spinal cord (nine datasets) was constructed to optimize transmit homogeneity and efficiency for these regions. A tailored B0 shim was optimized for the cervical spine to enhance spatial magnetic field homogeneity further. The performance of the universal shims was validated using absolute saturation based B(+)(1) mapping and high-resolution 2D and 3D multi-echo gradient-recalled echo (GRE) data to assess the image quality. RESULTS: The proposed universal shims demonstrated a 50% improvement in B(+)(1) efficiency compared to the default (zero phase) shim mode. B(+)(1) homogeneity was also improved by 20%. The optimized universal shims achieved performance comparable to subject-specific pTx adjustments, while eliminating the need for lengthy pTx calibration times, saving about 10 min per experiment. CONCLUSION: The development of universal shims represents a significant advance by eliminating time-consuming subject-specific pTx adjustments. This approach is expected to make UHF spinal cord imaging more accessible and user-friendly, particularly for non-pTx experts