--- a
+++ b/dosma/tissues/patellar_cartilage.py
@@ -0,0 +1,302 @@
+"""Analysis for patellar cartilage.
+
+Attributes:
+    BOUNDS (dict): Upper bounds for quantitative values.
+"""
+import itertools
+import os
+import warnings
+
+import numpy as np
+import pandas as pd
+import scipy.ndimage as sni
+
+from dosma.core.device import get_array_module
+from dosma.core.quant_vals import QuantitativeValueType
+from dosma.defaults import preferences
+from dosma.tissues.tissue import Tissue, largest_cc
+from dosma.utils import io_utils
+
+import matplotlib.pyplot as plt
+
+# milliseconds
+BOUNDS = {
+    QuantitativeValueType.T2: 60.0,
+    QuantitativeValueType.T1_RHO: 100.0,
+    QuantitativeValueType.T2_STAR: 50.0,
+}
+
+__all__ = ["PatellarCartilage"]
+
+
+class PatellarCartilage(Tissue):
+    """Handles analysis and visualization for patellar cartilage."""
+
+    ID = 3
+    STR_ID = "pc"
+    FULL_NAME = "patellar cartilage"
+
+    # Expected quantitative values
+    T1_EXPECTED = 1000  # milliseconds
+
+    # Region Keys
+    _ANTERIOR_KEY = 0
+    _POSTERIOR_KEY = 1
+    _CORONAL_KEYS = [_ANTERIOR_KEY, _POSTERIOR_KEY]
+
+    _MEDIAL_KEY = 0
+    _LATERAL_KEY = 1
+    _SAGITTAL_KEYS = [_MEDIAL_KEY, _LATERAL_KEY]
+
+    _REGION_DEEP_KEY = 0
+    _REGION_SUPERFICIAL_KEY = 1
+    _TOTAL_AXIAL_KEY = -1
+
+    def __init__(self, weights_dir: str = None, medial_to_lateral: bool = None):
+        super().__init__(weights_dir=weights_dir, medial_to_lateral=medial_to_lateral)
+
+        self.regions_mask = None
+
+    def unroll_coronal(self, quant_map: np.ndarray):
+        """Unroll patellar cartilage in the coronal direction.
+
+        Because patellar cartilage is flat, "unrolling" is projecting the patellar
+        cartilage onto the coronal axis.
+
+        Args:
+            quant_map (np.ndarray):
+        """
+        mask = self.__mask__.volume
+
+        assert (
+            self.regions_mask is not None
+        ), "region_mask not initialized. Should be initialized when mask is set"
+        region_mask_deep_superficial = self.regions_mask[..., 0]
+
+        superficial = (
+            (region_mask_deep_superficial == self._REGION_SUPERFICIAL_KEY) * mask * quant_map
+        )
+        superficial[superficial == 0] = np.nan
+        superficial = np.nanmean(superficial, axis=2)
+
+        deep = (region_mask_deep_superficial == self._REGION_DEEP_KEY) * mask * quant_map
+        deep[deep == 0] = np.nan
+        deep = np.nanmean(deep, axis=2)
+
+        total = mask * quant_map
+        total[total == 0] = np.nan
+        total = np.nanmean(total, axis=2)
+
+        return total, superficial, deep
+
+    def split_regions(self, base_map):
+        """Split patellar cartilage into deep/superficial regions.
+
+        For patellar cartilage, the superficial/deep transition occurs in
+        the anterior/posterior (A/P) direction. The boundary is determined
+        for each non-zero 1D column spanning independently by the local
+        center-of-mass (COM). The medial/lateral (M/L) plane is computed
+        using the global COM.
+
+        Args:
+            base_map (ndarray): Binary 3D mask with orientation (SI, AP, ML/LM).
+                If `self.medial_to_lateral`, last dimension should be ML.
+        """
+        if np.sum(base_map) == 0:
+            warnings.warn("No mask for `%s` was found." % self.FULL_NAME)
+
+        # Superficial/Deep (A/P)
+        locs = base_map.sum(axis=1).nonzero()
+        voxels = base_map[locs[0], :, locs[1]]
+        com_sup_inf = np.asarray(
+            [
+                int(np.ceil(sni.measurements.center_of_mass(voxels[i, :])[0]))
+                for i in range(voxels.shape[0])
+            ]
+        )
+        region_mask_sup_deep = np.full(base_map.shape, self._REGION_DEEP_KEY)
+        for i in range(len(com_sup_inf)):
+            region_mask_sup_deep[
+                locs[0][i], : com_sup_inf[i], locs[1][i]
+            ] = self._REGION_SUPERFICIAL_KEY
+
+        # M/L
+        cum_ml = np.nonzero(base_map.sum(axis=(0, 1)))[0]  # noqa: F841
+        # midpoint_ml = int(np.ceil((np.min(cum_ml) + np.max(cum_ml)) / 2))
+        midpoint_ml = int(np.ceil(sni.measurements.center_of_mass(base_map)[2]))
+        region_mask_med_lat = np.full(base_map.shape, self._LATERAL_KEY)
+        medial_span = slice(0, midpoint_ml) if self.medial_to_lateral else slice(midpoint_ml, None)
+        region_mask_med_lat[:, :, medial_span] = self._MEDIAL_KEY
+
+        self.regions_mask = np.stack([region_mask_sup_deep, region_mask_med_lat], axis=-1)
+
+    def __calc_quant_vals__(self, quant_map, map_type):
+        subject_pid = self.pid
+
+        super().__calc_quant_vals__(quant_map, map_type)
+
+        assert (
+            self.regions_mask is not None
+        ), "region_mask not initialized. Should be initialized when mask is set"
+
+        quant_map_volume = quant_map.volume
+        mask = self.__mask__.volume
+        quant_map_volume = mask * quant_map_volume
+
+        deep_superficial_map = self.regions_mask[..., 0]
+        med_lat_map = self.regions_mask[..., 1]
+
+        axial_names = ["superficial", "deep", "total"]
+        sagittal_names = ["medial", "lateral"]
+
+        pd_header = ["Subject", "Location", "Condyle", "Mean", "Std", "Median"]
+        pd_list = []
+
+        regions = itertools.product(
+            [self._REGION_SUPERFICIAL_KEY, self._REGION_DEEP_KEY, self._TOTAL_AXIAL_KEY],
+            [self._MEDIAL_KEY, self._LATERAL_KEY],
+        )
+        for axial, sagittal in regions:
+            if axial == self._TOTAL_AXIAL_KEY:
+                axial_map = np.asarray(
+                    deep_superficial_map == self._REGION_SUPERFICIAL_KEY, dtype=np.float32
+                ) + np.asarray(deep_superficial_map == self._REGION_DEEP_KEY, dtype=np.float32)
+                axial_map = np.asarray(axial_map, dtype=np.bool)
+            else:
+                axial_map = deep_superficial_map == axial
+
+            sagittal_map = med_lat_map == sagittal
+
+            curr_region_mask = quant_map_volume * axial_map * sagittal_map
+            curr_region_mask = curr_region_mask[curr_region_mask != 0]
+
+            # discard all values that are 0
+            c_mean = np.nanmean(curr_region_mask)
+            c_std = np.nanstd(curr_region_mask)
+            c_median = np.nanmedian(curr_region_mask)
+
+            row_info = [
+                subject_pid,
+                axial_names[axial],
+                sagittal_names[sagittal],
+                c_mean,
+                c_std,
+                c_median,
+            ]
+
+            pd_list.append(row_info)
+
+        # Generate 2D unrolled matrix
+        total, superficial, deep = self.unroll_coronal(quant_map.volume)
+
+        df = pd.DataFrame(pd_list, columns=pd_header)
+        qv_name = map_type.name
+        maps = [
+            {
+                "title": "%s superficial" % qv_name,
+                "data": superficial,
+                "xlabel": "Slice",
+                "ylabel": "Angle (binned)",
+                "filename": "%s_superficial" % qv_name,
+                "raw_data_filename": "%s_superficial.data" % qv_name,
+            },
+            {
+                "title": "%s deep" % qv_name,
+                "data": deep,
+                "xlabel": "Slice",
+                "ylabel": "Angle (binned)",
+                "filename": "%s_deep" % qv_name,
+                "raw_data_filename": "%s_deep.data" % qv_name,
+            },
+            {
+                "title": "%s total" % qv_name,
+                "data": total,
+                "xlabel": "Slice",
+                "ylabel": "Angle (binned)",
+                "filename": "%s_total" % qv_name,
+                "raw_data_filename": "%s_total.data" % qv_name,
+            },
+        ]
+
+        self.__store_quant_vals__(maps, df, map_type)
+
+    def set_mask(self, mask, use_largest_cc: bool = True):
+        xp = get_array_module(mask.A)
+        if use_largest_cc:
+            msk = xp.asarray(largest_cc(mask.A), dtype=xp.uint8)
+        else:
+            msk = xp.asarray(mask.A, dtype=xp.uint8)
+        mask_copy = mask._partial_clone(volume=msk)
+        super().set_mask(mask_copy)
+
+        self.split_regions(self.__mask__.volume)
+
+    def __save_quant_data__(self, dirpath):
+        """Save quantitative data and 2D visualizations of patellar cartilage
+
+        Check which quantitative values (T2, T1rho, etc) are defined for
+        patellar cartilage and analyze these:
+
+        1. Save 2D total, superficial, and deep visualization maps
+        2. Save {'medial', 'lateral'}, {'anterior', 'posterior'},
+            {'superior', 'inferior', 'total'} data to excel file
+
+        :param dirpath: base filepath to save data
+        """
+        q_names = []
+        dfs = []
+
+        for quant_val in QuantitativeValueType:
+            if quant_val.name not in self.quant_vals.keys():
+                continue
+
+            q_names.append(quant_val.name)
+            q_val = self.quant_vals[quant_val.name]
+            dfs.append(q_val[1])
+
+            q_name_dirpath = io_utils.mkdirs(os.path.join(dirpath, quant_val.name.lower()))
+            for q_map_data in q_val[0]:
+                filepath = os.path.join(q_name_dirpath, q_map_data["filename"])
+                xlabel = ""
+                ylabel = ""
+                title = q_map_data["title"]
+                data_map = q_map_data["data"]
+
+                axs_bounds = self.__get_axis_bounds__(data_map, leave_buffer=True)
+
+                plt.clf()
+
+                upper_bound = BOUNDS[quant_val]
+                if preferences.visualization_use_vmax:
+                    # Hard bounds - clipping
+                    plt.imshow(data_map, cmap="jet", vmin=0.0, vmax=BOUNDS[quant_val])
+                else:
+                    # Try to use a soft bounds
+                    if np.sum(data_map <= upper_bound) == 0:
+                        plt.imshow(data_map, cmap="jet", vmin=0.0, vmax=BOUNDS[quant_val])
+                    else:
+                        warnings.warn(
+                            "%s: Pixel value exceeded upper bound (%0.1f). Using normalized scale."
+                            % (quant_val.name, upper_bound)
+                        )
+                        plt.imshow(data_map, cmap="jet")
+
+                plt.xlabel(xlabel)
+                plt.ylabel(ylabel)
+                plt.title(title)
+                plt.ylim(axs_bounds[0])
+                plt.gca().invert_yaxis()
+                plt.xlim(axs_bounds[1])
+                # plt.axis('tight')
+                clb = plt.colorbar()
+                clb.ax.set_ylabel("(ms)")
+                plt.savefig(filepath)
+
+                # Save data
+                raw_data_filepath = os.path.join(
+                    q_name_dirpath, "raw_data", q_map_data["raw_data_filename"]
+                )
+                io_utils.save_pik(raw_data_filepath, data_map)
+
+        if len(dfs) > 0:
+            io_utils.save_tables(os.path.join(dirpath, "data.xlsx"), dfs, q_names)