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| import gradio as gr | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| def calculate_and_plot(t1, a1, t2, a2): | |
| try: | |
| # الحسابات الأساسية | |
| k = np.log(a1 / a2) / (t2 - t1) | |
| a0 = a1 / np.exp(-k * t1) | |
| tia = a0 / k | |
| result = f""" | |
| ### ✅ Results | |
| - **A₀ (initial activity):** {a0:.2f} MBq | |
| - **k (washout rate):** {k:.5f} / hour | |
| - **TIA:** {tia:.2f} MBq·h | |
| """ | |
| # إنشاء نقاط الرسم | |
| t_points = np.linspace(0, max(t2 + 40, 200), 100) | |
| a_points = a0 * np.exp(-k * t_points) | |
| # رسم المنحنى باستخدام matplotlib | |
| fig, ax = plt.subplots(figsize=(6, 4)) | |
| ax.plot(t_points, a_points, label="Fitted Curve", color='blue') | |
| ax.scatter([t1, t2], [a1, a2], color='red', label="Input Points") | |
| ax.set_xlabel("Time (hours)") | |
| ax.set_ylabel("Activity (MBq)") | |
| ax.set_title("Mono-Exponential Washout Curve") | |
| ax.grid(True) | |
| ax.legend() | |
| plt.tight_layout() | |
| return result, fig | |
| except Exception: | |
| return "⚠️ Error: Please check your inputs.", None | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# ⚛️ Lu-177 Dosimetry Tool\nEstimate Time-Integrated Activity (TIA) from Two SPECT Time Points") | |
| with gr.Row(): | |
| t1 = gr.Number(label="Time Point 1 (hours)", value=20) | |
| a1 = gr.Number(label="Activity at Time 1 (MBq)", value=12) | |
| with gr.Row(): | |
| t2 = gr.Number(label="Time Point 2 (hours)", value=60) | |
| a2 = gr.Number(label="Activity at Time 2 (MBq)", value=4) | |
| output_text = gr.Markdown() | |
| output_plot = gr.Plot() | |
| button = gr.Button("Calculate & Plot TIA") | |
| button.click(fn=calculate_and_plot, inputs=[t1, a1, t2, a2], outputs=[output_text, output_plot]) | |
| gr.Markdown(""" | |
| --- | |
| ### ℹ️ What Is This Tool? | |
| This open-source calculator estimates **Time-Integrated Activity (TIA)** for patients receiving **Lutetium-177 PSMA therapy** for metastatic prostate cancer. | |
| It uses **two post-treatment SPECT/CT time points** to fit a simplified **mono-exponential washout model**, helping clinicians perform dose calculations with minimal imaging. | |
| --- | |
| ### ⚙️ How It Works | |
| You enter two scan times (hours) and their corresponding measured activity (MBq). | |
| The tool automatically: | |
| - Fits an exponential time–activity curve | |
| - Calculates the washout rate (_k_) | |
| - Estimates initial activity (_A₀_) | |
| - Computes **TIA = A₀ / k**, which can be used in absorbed dose estimation | |
| --- | |
| ### 🧠 Scientific Basis | |
| Model used: | |
| > A(t) = A₀ · exp(–k · t) | |
| > TIA = A₀ / k | |
| This approach is ideal for organs with regular clearance kinetics (e.g., kidneys) or tumors with predictable washout, and is based on principles used in MIRD dosimetry and EANM guidelines. | |
| --- | |
| """) | |
| demo.launch() | |