High-quality MRI is essential for accurate diagnosis and reliable research in veterinary and preclinical settings. Precise imaging enables veterinarians to detect subtle abnormalities, guide treatment plans, and monitor disease progression. In research, high-quality images ensure precise biomarker analysis, improving the reliability of data for drug and device development.

However, veterinary and preclinical MRI presents unique challenges. The smaller anatomical structures of animals require higher resolution to capture fine details, but this can increase scan times, raising the risk of anesthesia complications. Additionally, patient movement—whether due to respiration, stress, or anesthesia limitations—can introduce artifacts that degrade image clarity.

These challenges necessitate specialized tools and workflows, such as AI-powered post-processing, optimized imaging protocols, and efficient positioning systems.

Optimizing parameters for precision

Resolution: Higher resolution is essential for smaller animals to capture minute details, though it increases scan time. Balancing resolution with speed is crucial to avoid prolonged anesthesia exposure.

Contrast and Signal-to-Noise Ratio (SNR): Contrast adjustments help differentiate soft tissues or lesions, while SNR ensures image clarity by reducing background noise.

Slice Thickness and Orientation: Thinner slices provide more anatomical detail but can lead to longer acquisition times and more artifacts. Engineers and radiologists collaborate to find the right balance, depending on the clinical or research focus.

Collaboration to refine protocols
Tailoring MRI protocols to specific research or diagnostic needs ensures that image quality remains high across different applications, such as neurological imaging or musculoskeletal assessments.

Engineers handle the technical aspects, including calibrating machines and configuring coils, while radiologists determine anatomical targets and clinical priorities.

Standardized tools like HawkProtocols offer a streamlined framework, helping facilities maintain consistency and efficiency.

Case Study: Addressing motion artifacts with optimized protocols
Motion artifacts are a common challenge in veterinary MRI, often caused by involuntary movements or respiration. Faster imaging techniques, such as Fast Spin Echo (FSE) or parallel imaging, help minimize scan times and reduce the need for extended anesthesia. 

In addition, AI-based tools like HawkAI can correct noise and artifacts during post-processing, enhancing image clarity even when faster acquisition compromises resolution.

This integrated approach ensures veterinarians and researchers capture the highest quality images while minimizing patient risk.

Since we have our own MRI, we have a clinical component and can participate in preclinical studies. These activities allow our application engineers to remain relevant and aligned with practices when optimizing protocols.

Clinical Impact

High-quality imaging allows veterinarians to make timely and accurate diagnoses, reducing unnecessary procedures and treatment delays. This helps lower healthcare costs and improves patient outcomes by enabling targeted interventions.

Research Benefits

In preclinical research, clear MRI images are essential for precise biomarker identification and reliable data collection. High-quality imaging improves the reproducibility of research results, which is critical for drug development and regulatory approval.

Faster, more accurate imaging also accelerates the pace of scientific innovation, helping researchers bring new treatments to market more efficiently.

Ethical Impact

Improving image quality directly supports animal welfare by reducing the need for repeat scans and minimizing anesthesia exposure. Shorter scan times also lessen the stress experienced by animals, aligning MRI practices with ethical standards and best practices for care.

High-quality imaging thus not only benefits research and diagnostics but also fosters the humane treatment of animal patients.