A pulse sequence shaper for the pursuance of the research using a wide spectrum of radiospectroscopy and relaxation methods in NQR is proposed. The distinctive feature of this product is its implementation with the application of a multi-functional programmable frequency synthesizer suitable for high-speed amplitude and phase manipulations.
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Signal suppression of the gastrointestinal tract fluids in magnetic resonance cholangiopancreatography (MRCP) has been performed using various natural and chemical substances with the different MRCP pulse sequences. This study aimed to investigate the signal suppressions of the grape syrup and the grape syrup/lemon aqueous solutions in MRCP using the heavily T2-weighted sequences. For this purpose, the potassium, iron and manganese contents of grape syrup were measured by atomic absorption spectroscopy. The grape syrup and the grape syrup/lemon solutions with the various grape syrup percentages were imaged using the heavily T2-weighted sequences of MRCP such as T2 HASTE thick slab and T2 SPACE. MRCP in the volunteers was performed before and 10 minutes after oral administration of the grape syrup solution. The concentrations of potassium, iron, and manganese in grape syrup were 34.8, 2.4 and 1 mg/l, respectively. The in vitro study showed significantly lower signal to noise ratio using the grape syrup samples comparing to the grape syrup/lemon. The effective signal suppression for the in vitro study and considerable enhancement of negative contrast in the post-contrast MRCP was obtained using a T2 HASTE thick slab sequence. As a conclusion, the grape syrup solution can be used for signal suppression of the gastrointestinal tract fluid in MRCP as an oral negative contrast material. A T2 HASTE thick slab sequence produces suitable contrast in MRCP images using the grape syrup solution.
This review article explores the historical background and recent advances in the application of artificial intelligence (AI) in the development of radiofrequency pulses and pulse sequences in nuclear magnetic resonance spectroscopy (NMR) and imaging (MRI). The introduction of AI into this field, which traces back to the late 1970s, has recently witnessed remarkable progress, leading to the design of specialized frameworks and software solutions such as DeepRF, MRzero, and GENETICS-AI. Through an analysis of literature and case studies, this review tracks the transformation of AI-driven pulse design from initial proof-of-concept studies to comprehensive scientific programs, shedding light on the potential implications for the broader NMR and MRI communities. The fusion of artificial intelligence and magnetic resonance pulse design stands as a promising frontier in spectroscopy and imaging, offering innovative enhancements in data acquisition, analysis, and interpretation across diverse scientific domains.
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