diff --git a/Dockerfile b/Dockerfile
index 07e7156..54cb00f 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -5,8 +5,8 @@ FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu${UBUNTU_VERSION}
 
 # Python
 RUN apt-get update && apt-get install --no-install-recommends -y \
-        python3-pip && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
+        python3-pip git && \
+        apt-get clean && rm -rf /var/lib/apt/lists/*
 
 # Torch
 ARG TORCH_VERSION=2.5
@@ -14,15 +14,14 @@ ARG TORCH_VARIANT=cpu
 RUN python3 -m pip --no-cache-dir install \
         --index-url https://download.pytorch.org/whl/${TORCH_VARIANT}\
         torch==${TORCH_VERSION}
-       
+
 # MRpro    
 ARG MRPRO_VERSION=0.250107
-RUN python3 -m pip --no-cache-dir install \
-        mrpro==${MRPRO_VERSION}
+RUN python3 -m pip --no-cache-dir install git+https://github.com/PTB-MR/mrpro@cMRF
 
 # MRpro Server
 WORKDIR /home
-COPY *.py /home/mrpro_server/
+COPY * /home/mrpro_server/
 CMD ["python3", "/home/mrpro_server/server.py"]
 
 # Metadata
diff --git a/mrpro_server/combine_mrd_header.py b/mrpro_server/combine_mrd_header.py
new file mode 100644
index 0000000..50a35f7
--- /dev/null
+++ b/mrpro_server/combine_mrd_header.py
@@ -0,0 +1,152 @@
+"""Functions to combine ismrmrd data files and meta files."""
+
+from pathlib import Path
+
+import ismrmrd
+
+
+def read_ismrmrd_dataset(fname: Path) -> tuple:
+    """Read ismrmrd dataset from file.
+
+    Parameters
+    ----------
+    fname
+        file path to the ismrmrd dataset.
+
+    Returns
+    -------
+        ismrmrd header and list of acquisitions.
+    """
+    with ismrmrd.File(str(fname), "r") as file:
+        ds = file[list(file.keys())[-1]]
+        header = ds.header
+        acqs = ds.acquisitions[:]
+
+    return header, acqs
+
+
+def insert_traj_from_meta(
+    data_acqs: list[ismrmrd.acquisition.Acquisition],
+    meta_acqs: list[ismrmrd.acquisition.Acquisition],
+) -> list[ismrmrd.acquisition.Acquisition]:
+    """
+    Insert trajectory information from the meta file into the data file.
+
+    Parameters
+    ----------
+    data_acqs : list
+        list of acquisitions from the data file.
+    meta_acqs : list
+        list of acquisitions from the meta file.
+
+    Returns
+    -------
+    list of acquisitions with the trajectory information from the meta file.
+    """
+    if not len(data_acqs) == len(meta_acqs):
+        raise ValueError("Number of acquisitions in data and meta file do not match.")
+
+    for i, (acq_d, acq_m) in enumerate(zip(data_acqs, meta_acqs, strict=False)):
+        if not acq_d.number_of_samples == acq_m.number_of_samples:
+            raise ValueError(f"Number of samples in acquisition {i} do not match.")
+
+        # insert trajectory information from meta file
+        acq_d.resize(
+            number_of_samples=acq_d.number_of_samples,
+            active_channels=acq_d.active_channels,
+            trajectory_dimensions=acq_m.trajectory_dimensions,
+        )
+        acq_d.traj[:] = acq_m.traj[:]
+        data_acqs[i] = acq_d
+
+    return data_acqs
+
+
+def update_header_from_meta(
+    data_header: ismrmrd.xsd.ismrmrdHeader,
+    meta_header: ismrmrd.xsd.ismrmrdHeader,
+    enc_idx: int = 0,
+) -> ismrmrd.xsd.ismrmrdHeader:
+    """Update the header of the data file with the information from the meta file.
+
+    Parameters
+    ----------
+    data_header : ismrmrd.xsd.ismrmrdHeader
+        Header of the ISMRMRD data file.
+    meta_header : ismrmrd.xsd.ismrmrdHeader
+        Header of the ISMRMRD meta file created with the seq-file.
+    enc_idx : int, optional
+        Encoding index, by default 0
+
+    Returns
+    -------
+    ismrmrd.xsd.ismrmrdHeader
+        Updated header.
+    """
+
+    # Helper function to copy attributes if they are not None
+    def copy_attributes(source, target, attributes):
+        for attr in attributes:
+            value = getattr(source, attr, None)
+            if value is not None:
+                setattr(target, attr, value)
+
+    # Define the attributes to update for encodedSpace and reconSpace
+    attributes_to_update = ["matrixSize", "fieldOfView_mm"]
+
+    # Update encodedSpace
+    copy_attributes(
+        meta_header.encoding[enc_idx].encodedSpace,
+        data_header.encoding[enc_idx].encodedSpace,
+        attributes_to_update,
+    )
+
+    # Update reconSpace
+    copy_attributes(
+        meta_header.encoding[enc_idx].reconSpace,
+        data_header.encoding[enc_idx].reconSpace,
+        attributes_to_update,
+    )
+
+    # Update trajectory type
+    if meta_header.encoding[enc_idx].trajectory is not None:
+        data_header.encoding[enc_idx].trajectory = meta_header.encoding[enc_idx].trajectory
+
+    return data_header
+
+
+def combine_ismrmrd_files(data_file: Path, meta_file: Path, filename_ext: str = "_with_traj.h5") -> ismrmrd.Dataset:
+    """Combine ismrmrd data file and meta file.
+
+    Parameters
+    ----------
+    data_file
+        path to the ismrmrd data file
+    meta_file
+        path to the ismrmrd meta file
+    filename_ext, optional
+        filename extension of the output file, by default '_with_traj.h5'
+
+    Returns
+    -------
+        combined ismrmrd file from data and meta file.
+    """
+    filename_out = data_file.parent / (data_file.stem + filename_ext)
+
+    data_header, data_acqs = read_ismrmrd_dataset(data_file)
+    meta_header, meta_acqs = read_ismrmrd_dataset(meta_file)
+
+    new_acqs = insert_traj_from_meta(data_acqs, meta_acqs)
+    new_header = update_header_from_meta(data_header, meta_header)
+
+    # Create new file
+    ds = ismrmrd.Dataset(filename_out)
+    ds.write_xml_header(new_header.toXML())
+
+    # add acquisitions with trajectory information
+    for acq in new_acqs:
+        ds.append_acquisition(acq)
+
+    ds.close()
+
+    return ds
diff --git a/mrpro_server/flip_angle_pattern.txt b/mrpro_server/flip_angle_pattern.txt
new file mode 100644
index 0000000..eb6e4f0
--- /dev/null
+++ b/mrpro_server/flip_angle_pattern.txt
@@ -0,0 +1,705 @@
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\ No newline at end of file
diff --git a/mrpro_server/header.h5 b/mrpro_server/header.h5
new file mode 100644
index 0000000..1878a20
Binary files /dev/null and b/mrpro_server/header.h5 differ
diff --git a/mrpro_server/process.py b/mrpro_server/process.py
index eb16000..b7c5d24 100644
--- a/mrpro_server/process.py
+++ b/mrpro_server/process.py
@@ -6,11 +6,16 @@
 import tempfile
 from pathlib import Path
 
+from einops import rearrange
+import mrpro.operators.models.EPG
+
 import constants
 import ismrmrd
 import mrpro
 import torch
 
+from combine_mrd_header import combine_ismrmrd_files
+
 
 def process(
     acquisitions: list[ismrmrd.Acquisition],
@@ -22,6 +27,11 @@ def process(
     """Process ISMRMRD Acquisitions to ISMRMRD Images."""
 
     reference = acquisitions[0]  # Only needed until we can build header from IData
+
+    # read flip angle pattern
+    with open(Path("flip_angle_pattern.txt"), "r") as file:
+        fa = torch.as_tensor([float(line) for line in file.readlines()]) / 180 * torch.pi
+
     with tempfile.TemporaryDirectory() as tmpdir:
         # TODO: create KData directly from ISMRMRD Acquisitions
         file = Path(tmpdir) / "data.mrd"
@@ -29,24 +39,106 @@ def process(
             dset.write_xml_header(metadata)
             while acquisitions:
                 dset.append_acquisition(acquisitions.pop())
+
+        # combine data and header to data_with_traj.h5
+        _ = combine_ismrmrd_files(file, Path("header.h5"))
+
         logging.info(f"KData written to {file!s}. Size:  {file.stat().st_size}")
         gc.collect()
 
         ##################################################
 
-        kdata = mrpro.data.KData.from_file(file, ktrajectory=mrpro.data.traj_calculators.KTrajectoryCartesian())
-        recon = mrpro.algorithms.reconstruction.DirectReconstruction(kdata, csm=None)
-        image = recon(kdata)
+        # Define the T1 and T2 values to be included in the dictionaries
+        t1 = torch.cat(
+            (torch.arange(50, 2000 + 10, 10), torch.arange(2020, 3000 + 20, 20), torch.arange(3050, 5000 + 50, 50))
+        )
+        t2 = torch.cat((torch.arange(6, 100 + 2, 2), torch.arange(105, 200 + 5, 5), torch.arange(220, 500 + 20, 20)))
+
+        n_lines_per_img = 20
+        n_lines_overlap = 10
+
+        kdata = mrpro.data.KData.from_file(
+            Path(tmpdir) / "data_with_traj.h5", mrpro.data.traj_calculators.KTrajectoryIsmrmrd()
+        )
+        logging.info("KData created")
+        avg_recon = mrpro.algorithms.reconstruction.DirectReconstruction(kdata)
+        logging.info("avg img created")
+
+        # Split data into dynamics and reconstruct
+        dyn_idx = mrpro.utils.split_idx(torch.arange(0, 47), n_lines_per_img, n_lines_overlap)
+        dyn_idx = torch.cat([dyn_idx + ind * 47 for ind in range(15)], dim=0)
+
+        kdata_dyn = kdata.split_k1_into_other(dyn_idx, other_label="repetition")
+
+        dyn_recon = mrpro.algorithms.reconstruction.DirectReconstruction(kdata_dyn, csm=avg_recon.csm)
+        logging.info("dyn img reco done")
+        dcf_data_dyn = rearrange(avg_recon.dcf.data, "k2 k1 other k0->other k2 k1 k0")
+        dcf_data_dyn = rearrange(
+            dcf_data_dyn[dyn_idx.flatten(), ...], "(other k1) 1 k2 k0->other k2 k1 k0", k1=dyn_idx.shape[-1]
+        )
+        dyn_recon.dcf = mrpro.data.DcfData(dcf_data_dyn)
+
+        img = dyn_recon(kdata_dyn).rss()[:, 0, :, :]
+        logging.info("dyn images created")
+
+        # Dictionary settings
+        t1, t2 = torch.broadcast_tensors(t1[None, :], t2[:, None])
+        t1_all = t1.flatten().to(dtype=torch.float32)
+        t2_all = t2.flatten().to(dtype=torch.float32)
+
+        t1 = t1_all[t1_all >= t2_all]
+        t2 = t2_all[t1_all >= t2_all]
+        m0 = torch.ones_like(t1)
+
+        # Dictionary calculation
+        n_rf_pulses_per_block = 47  # 47 RF pulses in each block
+        acq_t_ms = kdata.header.acq_info.acquisition_time_stamp[0, 0, :, 0] * 2.5
+        delay_between_blocks = [
+            acq_t_ms[n_block * n_rf_pulses_per_block] - acq_t_ms[n_block * n_rf_pulses_per_block - 1]
+            for n_block in range(1, 3 * 5)
+        ]
+        delay_between_blocks.append(delay_between_blocks[-1])  # last delay is not needed but makes computations easier
+
+        flip_angles = fa
+        rf_phases = 0.0
+        te = 1.52
+        tr = 6.6
+        inv_prep_ti = [21, None, None, None, None] * 3  # 21 ms delay after inversion pulse in block 0
+        t2_prep_te = [None, None, 30, 50, 100] * 3  # T2-preparation pulse with TE = 30, 50, 100
+        delay_due_to_prep = [0, 30, 50, 100, 21] * 3
+        delay_after_block = [
+            trig_delay - prep_delay for prep_delay, trig_delay in zip(delay_due_to_prep, delay_between_blocks)
+        ]
+        epg_mrf_fisp = mrpro.operators.models.EPG.EpgMrfFispWithPreparation(
+            flip_angles, rf_phases, te, tr, inv_prep_ti, t2_prep_te, n_rf_pulses_per_block, delay_after_block
+        )
+        (signal_dictionary,) = epg_mrf_fisp.forward(m0, t1, t2)
+
+        signal_dictionary = rearrange(
+            signal_dictionary[dyn_idx.flatten(), ...], "(other k1) t->other t k1", k1=dyn_idx.shape[-1]
+        )
+        signal_dictionary = torch.mean(signal_dictionary, dim=-1)
+        signal_dictionary = signal_dictionary.abs()
+
+        # Normalise dictionary entries
+        vector_norm = torch.linalg.vector_norm(signal_dictionary, dim=0)
+        signal_dictionary /= vector_norm
+
+        # Dictionary matching
+        n_y, n_x = img.shape[-2:]
+        dot_product = torch.mm(rearrange(img.abs(), "other y x->(y x) other"), signal_dictionary)
+        idx_best_match = torch.argmax(torch.abs(dot_product), dim=1)
+        data = rearrange(t1[idx_best_match], "(y x)->1 1 1 y x", y=n_y, x=n_x).to(torch.complex64)
+        # t2_match = rearrange(t2[idx_best_match], "(y x)->y x", y=n_y, x=n_x)
 
         logging.info("Reconstruction done")
-        logging.debug(f"Memory used by Python process: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024:.2f} MB")
+        logging.debug(
+            f"Memory used by Python process: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024:.2f} MB"
+        )
 
         ##################################################
 
         # TODO: create ISMRMRD Image from IData
-        data = image.rss(keepdim=True).detach().cpu().flatten(end_dim=-5).to(torch.complex64)
-        scale = (data.shape[-3:].numel()) ** 0.5
-        data = data * scale  # ICE uses no scaling in backward
         fov = kdata.header.recon_fov.apply(mrpro.utils.unit_conversion.m_to_mm)
 
         new_images = []
diff --git a/settings.json b/settings.json
index 350ba3f..319c624 100644
--- a/settings.json
+++ b/settings.json
@@ -1,8 +1,8 @@
 {
-    "name": "MRpro-RSS",
+    "name": "MRpro-MRF",
     "version": "1.{{ mrpro_minor }}.1",
     "vendor": "PTB",
-    "information": "MRpro RSS reconstruction",
+    "information": "MRpro MRF reconstruction",
     "can_process_adjustment_data": false,
     "can_use_gpu": false,
     "min_count_required_gpus": 0,