diff --git a/monai/data/wsi_reader.py b/monai/data/wsi_reader.py
index b31d4d9c3a..f3f099160f 100644
--- a/monai/data/wsi_reader.py
+++ b/monai/data/wsi_reader.py
@@ -19,6 +19,7 @@
 
 import numpy as np
 import torch
+import cv2
 
 from monai.config import DtypeLike, NdarrayOrTensor, PathLike
 from monai.data.image_reader import ImageReader, _stack_images
@@ -940,6 +941,87 @@ def get_mpp(self, wsi, level: int) -> tuple[float, float]:
 
         raise ValueError("`mpp` cannot be obtained for this file. Please use `level` instead.")
 
+    def get_img_at_mpp(self, wsi, mpp: tuple, atol: float = 0.00, rtol: float = 0.05) -> np.array:
+        """
+        Returns the representation of the whole slide image at a given micro-per-pixel (mpp) resolution.
+        The optional tolerance parameters are considered at the level whose mpp value is closest to the one provided by the user.
+        If the user-provided mpp is larger than the mpp of the closest level—indicating that the closest level has a higher resolution than requested—the image is downscaled to a resolution that matches the user-provided mpp.
+        Otherwise, if the closest level's resolution is not sufficient to meet the user's requested resolution, the next lower level (which has a higher resolution) is chosen.
+        The image from this level is then down-scaled to achieve a resolution at the user-provided mpp value.
+
+        Args:
+            wsi: whole slide image object from WSIReader
+            mpp: the resolution in micron per pixel at which the representation of the whole slide image should be extracted.
+            atol: the acceptable absolute tolerance for resolution in micro per pixel.
+            rtol: the acceptable relative tolerance for resolution in micro per pixel.
+
+        """
+
+        user_mpp_x, user_mpp_y = mpp
+        mpp_list = [self.get_mpp(wsi, lvl) for lvl in range(wsi.level_count)]
+        closest_lvl = self._find_closest_level("mpp", mpp, mpp_list, 0, 5)  # Should not throw ValueError, instead just return the closest value; 
+        mpp_closest_lvl = mpp_list[closest_lvl]
+        closest_lvl_dim = wsi.level_dimensions[closest_lvl]
+
+        print(f'Closest Level: {closest_lvl} with MPP: {mpp_closest_lvl}')
+        mpp_closest_lvl_x, mpp_closest_lvl_y = mpp_closest_lvl
+
+        # Define tolerance intervals for x and y of closest level
+        lower_bound_x = mpp_closest_lvl_x * (1 - rtol) - atol
+        upper_bound_x = mpp_closest_lvl_x * (1 + rtol) + atol
+        lower_bound_y = mpp_closest_lvl_y * (1 - rtol) - atol
+        upper_bound_y = mpp_closest_lvl_y * (1 + rtol) + atol
+
+        # Check if user-provided mpp_x and mpp_y fall within the tolerance intervals for closest level
+        within_tolerance_x = (user_mpp_x >= lower_bound_x) & (user_mpp_x <= upper_bound_x)
+        within_tolerance_y = (user_mpp_y >= lower_bound_y) & (user_mpp_y <= upper_bound_y)
+        within_tolerance = within_tolerance_x & within_tolerance_y
+    
+        if within_tolerance:
+            # Take closest_level and continue with returning img at level
+            print(f'User-provided MPP lies within tolerance of level {closest_lvl}, returning wsi at this level.')
+            closest_lvl_wsi = np.array(wsi.read_region(location=(0, 0), level=closest_lvl, size=closest_lvl_dim))[:, :, :3]
+
+            return closest_lvl_wsi
+        else:
+            # If mpp_closest_level < mpp -> closest_level has higher res than img at mpp => downscale from closest_level to mpp
+            closest_level_is_bigger_x = mpp_closest_lvl_x < user_mpp_x
+            closest_level_is_bigger_y = mpp_closest_lvl_y < user_mpp_y
+            closest_level_is_bigger = closest_level_is_bigger_x & closest_level_is_bigger_y
+
+            if closest_level_is_bigger:
+                ds_factor_x = mpp_closest_lvl_x / user_mpp_x
+                ds_factor_y = mpp_closest_lvl_y / user_mpp_y
+
+                closest_lvl_wsi = np.array(wsi.read_region(location=(0, 0), level=closest_lvl, size=closest_lvl_dim))[:, :, :3]
+
+                target_res_x = int(np.round(closest_lvl_dim[0] * ds_factor_x))
+                target_res_y = int(np.round(closest_lvl_dim[1] * ds_factor_y))
+
+                closest_lvl_wsi = cv2.resize(closest_lvl_wsi, dsize=(target_res_x, target_res_y), interpolation=cv2.INTER_LINEAR)
+
+                print(f'Case 1: Downscaling using factor {(ds_factor_x, ds_factor_y)}')
+                return closest_lvl_wsi
+            else:
+                # Else: increase resolution (ie, decrement level) and then downsample
+                closest_lvl = closest_lvl - 1
+                mpp_closest_lvl = mpp_list[closest_lvl]  # Update MPP
+                mpp_closest_lvl_x, mpp_closest_lvl_y = mpp_closest_lvl
+
+                ds_factor_x = mpp_closest_lvl_x / user_mpp_x
+                ds_factor_y = mpp_closest_lvl_y / user_mpp_y
+
+                closest_lvl_dim = wsi.level_dimensions[closest_lvl]
+                closest_lvl_wsi = np.array(wsi.read_region(location=(0, 0), level=closest_lvl, size=closest_lvl_dim))[:, :, :3]
+
+                target_res_x = int(np.round(closest_lvl_dim[0] * ds_factor_x))
+                target_res_y = int(np.round(closest_lvl_dim[1] * ds_factor_y))
+
+                closest_lvl_wsi = cv2.resize(closest_lvl_wsi, dsize=(target_res_x, target_res_y), interpolation=cv2.INTER_LINEAR)
+
+                print(f'Case 2: Downscaling using factor {(ds_factor_x, ds_factor_y)}, now from level {closest_lvl}')
+                return closest_lvl_wsi
+
     def get_power(self, wsi, level: int) -> float:
         """
         Returns the objective power of the whole slide image at a given level.