diff --git a/docs/HOWTO/img/converted_subgraph.png b/docs/HOWTO/img/converted_subgraph.png
deleted file mode 100644
index 6a5b7220777..00000000000
--- a/docs/HOWTO/img/converted_subgraph.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f7c8ab4f15874d235968471bcf876c89c795d601e69891208107b8b72aa58eb1
-size 70014
diff --git a/docs/HOWTO/img/unsupported_subgraph.png b/docs/HOWTO/img/unsupported_subgraph.png
deleted file mode 100644
index 80f7084a78a..00000000000
--- a/docs/HOWTO/img/unsupported_subgraph.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3d5ccf51fe1babb93d96d042494695a6a6e055d1f8ebf7eef5083d54d8987a23
-size 58789
diff --git a/docs/HOWTO/mo_extensions/front/tf/Complex.py b/docs/HOWTO/mo_extensions/front/tf/Complex.py
deleted file mode 100644
index 169a3e50050..00000000000
--- a/docs/HOWTO/mo_extensions/front/tf/Complex.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# Copyright (C) 2018-2023 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-#! [complex:transformation]
-
-from openvino.tools.mo.front.common.replacement import FrontReplacementSubgraph
-from openvino.tools.mo.graph.graph import Graph
-
-
-class Complex(FrontReplacementSubgraph):
-    enabled = True
-
-    def pattern(self):
-        return dict(
-            nodes=[
-                ('strided_slice_real', dict(op='StridedSlice')),
-                ('strided_slice_imag', dict(op='StridedSlice')),
-                ('complex', dict(op='Complex')),
-            ],
-            edges=[
-                ('strided_slice_real', 'complex', {'in': 0}),
-                ('strided_slice_imag', 'complex', {'in': 1}),
-            ])
-
-    @staticmethod
-    def replace_sub_graph(graph: Graph, match: dict):
-        strided_slice_real = match['strided_slice_real']
-        strided_slice_imag = match['strided_slice_imag']
-        complex_node = match['complex']
-
-        # make sure that both strided slice operations get the same data as input
-        assert strided_slice_real.in_port(0).get_source() == strided_slice_imag.in_port(0).get_source()
-
-        # identify the output port of the operation producing datat for strided slice nodes
-        input_node_output_port = strided_slice_real.in_port(0).get_source()
-        input_node_output_port.disconnect()
-
-        # change the connection so now all consumers of "complex_node" get data from input node of strided slice nodes
-        complex_node.out_port(0).get_connection().set_source(input_node_output_port)
-#! [complex:transformation]
diff --git a/docs/HOWTO/mo_extensions/front/tf/ComplexAbs.py b/docs/HOWTO/mo_extensions/front/tf/ComplexAbs.py
deleted file mode 100644
index 56594438611..00000000000
--- a/docs/HOWTO/mo_extensions/front/tf/ComplexAbs.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Copyright (C) 2018-2023 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-#! [complex_abs:transformation]
-import numpy as np
-
-from openvino.tools.mo.ops.elementwise import Pow
-from openvino.tools.mo.ops.ReduceOps import ReduceSum
-from openvino.tools.mo.front.common.replacement import FrontReplacementOp
-from openvino.tools.mo.graph.graph import Graph, Node
-from openvino.tools.mo.ops.const import Const
-
-
-class ComplexAbs(FrontReplacementOp):
-    op = "ComplexAbs"
-    enabled = True
-
-    def replace_op(self, graph: Graph, node: Node):
-        pow_2 = Const(graph, {'value': np.float32(2.0)}).create_node()
-        reduce_axis = Const(graph, {'value': np.int32(-1)}).create_node()
-        pow_0_5 = Const(graph, {'value': np.float32(0.5)}).create_node()
-
-        sq = Pow(graph, dict(name=node.in_node(0).name + '/sq', power=2.0)).create_node([node.in_node(0), pow_2])
-        sum = ReduceSum(graph, dict(name=sq.name + '/sum')).create_node([sq, reduce_axis])
-        sqrt = Pow(graph, dict(name=sum.name + '/sqrt', power=0.5)).create_node([sum, pow_0_5])
-        return [sqrt.id]
-#! [complex_abs:transformation]
diff --git a/docs/HOWTO/mo_extensions/front/tf/FFT_ext.py b/docs/HOWTO/mo_extensions/front/tf/FFT_ext.py
deleted file mode 100644
index ac1cfbdcbf8..00000000000
--- a/docs/HOWTO/mo_extensions/front/tf/FFT_ext.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# Copyright (C) 2018-2023 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-# ! [fft_ext:extractor]
-from ...ops.FFT import FFT
-from openvino.tools.mo.front.extractor import FrontExtractorOp
-
-
-class FFT2DFrontExtractor(FrontExtractorOp):
-    op = 'FFT2D'
-    enabled = True
-
-    @classmethod
-    def extract(cls, node):
-        attrs = {
-            'inverse': 0
-        }
-        FFT.update_node_stat(node, attrs)
-        return cls.enabled
-
-
-class IFFT2DFrontExtractor(FrontExtractorOp):
-    op = 'IFFT2D'
-    enabled = True
-
-    @classmethod
-    def extract(cls, node):
-        attrs = {
-            'inverse': 1
-        }
-        FFT.update_node_stat(node, attrs)
-        return cls.enabled
-# ! [fft_ext:extractor]
diff --git a/docs/HOWTO/mo_extensions/ops/FFT.py b/docs/HOWTO/mo_extensions/ops/FFT.py
deleted file mode 100644
index 604d893cfdf..00000000000
--- a/docs/HOWTO/mo_extensions/ops/FFT.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Copyright (C) 2018-2023 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-#! [fft:operation]
-from openvino.tools.mo.front.common.partial_infer.elemental import copy_shape_infer
-from openvino.tools.mo.graph.graph import Graph
-from openvino.tools.mo.ops.op import Op
-
-
-class FFT(Op):
-    op = 'FFT'
-    enabled = False
-
-    def __init__(self, graph: Graph, attrs: dict):
-        super().__init__(graph, {
-            'type': self.op,
-            'op': self.op,
-            'version': 'custom_opset',
-            'inverse': None,
-            'in_ports_count': 1,
-            'out_ports_count': 1,
-            'infer': copy_shape_infer
-        }, attrs)
-
-    def backend_attrs(self):
-        return ['inverse']
-#! [fft:operation]
diff --git a/docs/HOWTO/mri_reconstruction_demo.py b/docs/HOWTO/mri_reconstruction_demo.py
deleted file mode 100644
index d694d8819cf..00000000000
--- a/docs/HOWTO/mri_reconstruction_demo.py
+++ /dev/null
@@ -1,106 +0,0 @@
-# Copyright (C) 2018-2023 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-#! [mri_demo:demo]
-import numpy as np
-import cv2 as cv
-import argparse
-import time
-from openvino.inference_engine import IECore
-
-
-def kspace_to_image(kspace):
-    assert(len(kspace.shape) == 3 and kspace.shape[-1] == 2)
-    fft = cv.idft(kspace, flags=cv.DFT_SCALE)
-    img = cv.magnitude(fft[:,:,0], fft[:,:,1])
-    return cv.normalize(img, dst=None, alpha=255, beta=0, norm_type=cv.NORM_MINMAX, dtype=cv.CV_8U)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='MRI reconstrution demo for network from https://github.com/rmsouza01/Hybrid-CS-Model-MRI (https://arxiv.org/abs/1810.12473)')
-    parser.add_argument('-i', '--input', dest='input', help='Path to input .npy file with MRI scan data.')
-    parser.add_argument('-p', '--pattern', dest='pattern', help='Path to sampling mask in .npy format.')
-    parser.add_argument('-m', '--model', dest='model', help='Path to .xml file of OpenVINO IR.')
-    parser.add_argument('-l', '--cpu_extension', dest='cpu_extension', help='Path to extensions library with FFT implementation.')
-    parser.add_argument('-d', '--device', dest='device', default='CPU',
-                        help='Optional. Specify the target device to infer on; CPU, '
-                             'GPU, GNA is acceptable. For non-CPU targets, '
-                             'HETERO plugin is used with CPU fallbacks to FFT implementation. '
-                             'Default value is CPU')
-    args = parser.parse_args()
-
-    xml_path = args.model
-    assert(xml_path.endswith('.xml'))
-    bin_path = xml_path[:xml_path.rfind('.xml')] + '.bin'
-
-    ie = IECore()
-    ie.add_extension(args.cpu_extension, "CPU")
-
-    net = ie.read_network(xml_path, bin_path)
-
-    device = 'CPU' if args.device == 'CPU' else ('HETERO:' + args.device + ',CPU')
-    exec_net = ie.load_network(net, device)
-
-    # Hybrid-CS-Model-MRI/Data/stats_fs_unet_norm_20.npy
-    stats = np.array([2.20295299e-01, 1.11048916e+03, 4.16997984e+00, 4.71741395e+00], dtype=np.float32)
-    # Hybrid-CS-Model-MRI/Data/sampling_mask_20perc.npy
-    var_sampling_mask = np.load(args.pattern)  # TODO: can we generate it in runtime?
-    print('Sampling ratio:', 1.0 - var_sampling_mask.sum() / var_sampling_mask.size)
-
-    data = np.load(args.input)
-    num_slices, height, width = data.shape[0], data.shape[1], data.shape[2]
-    pred = np.zeros((num_slices, height, width), dtype=np.uint8)
-    data /= np.sqrt(height * width)
-
-    print('Compute...')
-    start = time.time()
-    for slice_id, kspace in enumerate(data):
-        kspace = kspace.copy()
-
-        # Apply sampling
-        kspace[var_sampling_mask] = 0
-        kspace = (kspace - stats[0]) / stats[1]
-
-        # Forward through network
-        input = np.expand_dims(kspace.transpose(2, 0, 1), axis=0)
-        outputs = exec_net.infer(inputs={'input_1': input})
-        output = next(iter(outputs.values()))
-        output = output.reshape(height, width)
-
-        # Save predictions
-        pred[slice_id] = cv.normalize(output, dst=None, alpha=255, beta=0, norm_type=cv.NORM_MINMAX, dtype=cv.CV_8U)
-
-    print('Elapsed time: %.1f seconds' % (time.time() - start))
-
-    WIN_NAME = 'MRI reconstruction with OpenVINO'
-
-    slice_id = 0
-    def callback(pos):
-        global slice_id
-        slice_id = pos
-
-        kspace = data[slice_id]
-        img = kspace_to_image(kspace)
-
-        kspace[var_sampling_mask] = 0
-        masked = kspace_to_image(kspace)
-
-        rec = pred[slice_id]
-
-        # Add a header
-        border_size = 20
-        render = cv.hconcat((img, masked, rec))
-        render = cv.copyMakeBorder(render, border_size, 0, 0, 0, cv.BORDER_CONSTANT, value=255)
-        cv.putText(render, 'Original', (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, color=0)
-        cv.putText(render, 'Sampled (PSNR %.1f)' % cv.PSNR(img, masked), (width, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, color=0)
-        cv.putText(render, 'Reconstructed (PSNR %.1f)' % cv.PSNR(img, rec), (width*2, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, color=0)
-
-        cv.imshow(WIN_NAME, render)
-        cv.waitKey(1)
-
-    cv.namedWindow(WIN_NAME, cv.WINDOW_NORMAL)
-    print(num_slices)
-    cv.createTrackbar('Slice', WIN_NAME, num_slices // 2, num_slices - 1, callback)
-    callback(num_slices // 2)  # Trigger initial visualization
-    cv.waitKey()
-#! [mri_demo:demo]