Make quantitative charts data-auditable

metrics.py

@@ -86,10 +86,20 @@ def slice_metric_curve(
     moving: np.ndarray,
     warped: np.ndarray,
     axis: int = 2,
+    metric: str = "mse",
 ) -> Dict[str, Iterable[float]]:
-    """逐切片计算 MSE，适合生成“配准前后误差曲线”。"""
+    """逐切片计算配准前后指标曲线。"""
 
     fixed, moving, warped = crop_to_common_shape(fixed, moving, warped)
+    metric_funcs = {
+        "mse": mse,
+        "mae": mae,
+        "ncc": global_ncc,
+    }
+    if metric not in metric_funcs:
+        raise ValueError(f"不支持的逐切片指标: {metric}")
+    metric_fn = metric_funcs[metric]
+
     before = []
     after = []
 
@@ -97,13 +107,13 @@ def slice_metric_curve(
         selector = [slice(None)] * 3
         selector[axis] = index
         selector = tuple(selector)
-        before.append(mse(fixed[selector], moving[selector]))
-        after.append(mse(fixed[selector], warped[selector]))
+        before.append(metric_fn(fixed[selector], moving[selector]))
+        after.append(metric_fn(fixed[selector], warped[selector]))
 
     return {
         "slice_index": list(range(fixed.shape[axis])),
-        "before_mse": before,
-        "after_mse": after,
+        f"before_{metric}": before,
+        f"after_{metric}": after,
     }
 
 
@@ -120,4 +130,3 @@ def ddf_summary(ddf_xyz: np.ndarray) -> Dict[str, float]:
         "ddf_p95": float(np.percentile(mag, 95)),
         "ddf_max": float(np.max(mag)),
     }
-