clinical-imaging

Clinical Imaging: Clinical & Physiological Imaging Analysis

Overview

Clinical Imaging provides computational tools for analyzing clinical and physiological imaging data. This skill covers diffusion MRI apparent diffusion coefficient (ADC) map computation, micro-CT bone morphometry (BV/TV, trabecular thickness), hemodynamic parameter analysis from blood pressure waveforms, circadian rhythm cosinor analysis, ciliary beat frequency measurement via FFT, tissue deformation analysis using optical flow, and amyloid plaque quantification from fluorescence microscopy.

When to Use This Skill

• Computing ADC maps from multi-b-value diffusion MRI data
• Analyzing bone microarchitecture from micro-CT volumes
• Processing blood pressure or hemodynamic waveform data
• Fitting circadian rhythm data with cosinor models
• Measuring ciliary beat frequency from high-speed video
• Quantifying tissue deformation from image sequences
• Counting and measuring amyloid plaques in fluorescence images

Related Skills: For DICOM file handling use pydicom. For biosignal processing (ECG, EMG, EDA) use neurokit2. For general image analysis use bioimage-analysis.

Installation

uv pip install nibabel SimpleITK scipy opencv-python scikit-image numpy pandas matplotlib

Quick Start

import numpy as np
from scipy.optimize import curve_fit

# Circadian rhythm cosinor analysis
def cosinor(t, mesor, amplitude, acrophase, period=24):
    return mesor + amplitude * np.cos(2 * np.pi * t / period + acrophase)

# Example: body temperature over 48 hours
time_hours = np.arange(0, 48, 1)
temperature = 36.8 + 0.3 * np.cos(2 * np.pi * time_hours / 24 - 1.0) + \
              np.random.normal(0, 0.1, len(time_hours))

popt, pcov = curve_fit(cosinor, time_hours, temperature,
                       p0=[36.8, 0.3, -1.0], maxfev=10000)
print(f"MESOR: {popt[0]:.2f} C")
print(f"Amplitude: {popt[1]:.3f} C")
print(f"Acrophase: {np.degrees(popt[2]):.1f} degrees ({popt[2]/(2*np.pi)*24:.1f} h)")